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• v.37(16); 2022 Apr 25

A Practical Guide to Writing Quantitative and Qualitative Research Questions and Hypotheses in Scholarly Articles

Edward barroga.

1 Department of General Education, Graduate School of Nursing Science, St. Luke’s International University, Tokyo, Japan.

Glafera Janet Matanguihan

2 Department of Biological Sciences, Messiah University, Mechanicsburg, PA, USA.

The development of research questions and the subsequent hypotheses are prerequisites to defining the main research purpose and specific objectives of a study. Consequently, these objectives determine the study design and research outcome. The development of research questions is a process based on knowledge of current trends, cutting-edge studies, and technological advances in the research field. Excellent research questions are focused and require a comprehensive literature search and in-depth understanding of the problem being investigated. Initially, research questions may be written as descriptive questions which could be developed into inferential questions. These questions must be specific and concise to provide a clear foundation for developing hypotheses. Hypotheses are more formal predictions about the research outcomes. These specify the possible results that may or may not be expected regarding the relationship between groups. Thus, research questions and hypotheses clarify the main purpose and specific objectives of the study, which in turn dictate the design of the study, its direction, and outcome. Studies developed from good research questions and hypotheses will have trustworthy outcomes with wide-ranging social and health implications.

INTRODUCTION

Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses. 1 , 2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results. 3 , 4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the inception of novel studies and the ethical testing of ideas. 5 , 6

It is crucial to have knowledge of both quantitative and qualitative research 2 as both types of research involve writing research questions and hypotheses. 7 However, these crucial elements of research are sometimes overlooked; if not overlooked, then framed without the forethought and meticulous attention it needs. Planning and careful consideration are needed when developing quantitative or qualitative research, particularly when conceptualizing research questions and hypotheses. 4

There is a continuing need to support researchers in the creation of innovative research questions and hypotheses, as well as for journal articles that carefully review these elements. 1 When research questions and hypotheses are not carefully thought of, unethical studies and poor outcomes usually ensue. Carefully formulated research questions and hypotheses define well-founded objectives, which in turn determine the appropriate design, course, and outcome of the study. This article then aims to discuss in detail the various aspects of crafting research questions and hypotheses, with the goal of guiding researchers as they develop their own. Examples from the authors and peer-reviewed scientific articles in the healthcare field are provided to illustrate key points.

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

A research question is what a study aims to answer after data analysis and interpretation. The answer is written in length in the discussion section of the paper. Thus, the research question gives a preview of the different parts and variables of the study meant to address the problem posed in the research question. 1 An excellent research question clarifies the research writing while facilitating understanding of the research topic, objective, scope, and limitations of the study. 5

On the other hand, a research hypothesis is an educated statement of an expected outcome. This statement is based on background research and current knowledge. 8 , 9 The research hypothesis makes a specific prediction about a new phenomenon 10 or a formal statement on the expected relationship between an independent variable and a dependent variable. 3 , 11 It provides a tentative answer to the research question to be tested or explored. 4

Hypotheses employ reasoning to predict a theory-based outcome. 10 These can also be developed from theories by focusing on components of theories that have not yet been observed. 10 The validity of hypotheses is often based on the testability of the prediction made in a reproducible experiment. 8

Conversely, hypotheses can also be rephrased as research questions. Several hypotheses based on existing theories and knowledge may be needed to answer a research question. Developing ethical research questions and hypotheses creates a research design that has logical relationships among variables. These relationships serve as a solid foundation for the conduct of the study. 4 , 11 Haphazardly constructed research questions can result in poorly formulated hypotheses and improper study designs, leading to unreliable results. Thus, the formulations of relevant research questions and verifiable hypotheses are crucial when beginning research. 12

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

Excellent research questions are specific and focused. These integrate collective data and observations to confirm or refute the subsequent hypotheses. Well-constructed hypotheses are based on previous reports and verify the research context. These are realistic, in-depth, sufficiently complex, and reproducible. More importantly, these hypotheses can be addressed and tested. 13

There are several characteristics of well-developed hypotheses. Good hypotheses are 1) empirically testable 7 , 10 , 11 , 13 ; 2) backed by preliminary evidence 9 ; 3) testable by ethical research 7 , 9 ; 4) based on original ideas 9 ; 5) have evidenced-based logical reasoning 10 ; and 6) can be predicted. 11 Good hypotheses can infer ethical and positive implications, indicating the presence of a relationship or effect relevant to the research theme. 7 , 11 These are initially developed from a general theory and branch into specific hypotheses by deductive reasoning. In the absence of a theory to base the hypotheses, inductive reasoning based on specific observations or findings form more general hypotheses. 10

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

Research questions and hypotheses are developed according to the type of research, which can be broadly classified into quantitative and qualitative research. We provide a summary of the types of research questions and hypotheses under quantitative and qualitative research categories in Table 1 .

Research questions in quantitative research

In quantitative research, research questions inquire about the relationships among variables being investigated and are usually framed at the start of the study. These are precise and typically linked to the subject population, dependent and independent variables, and research design. 1 Research questions may also attempt to describe the behavior of a population in relation to one or more variables, or describe the characteristics of variables to be measured ( descriptive research questions ). 1 , 5 , 14 These questions may also aim to discover differences between groups within the context of an outcome variable ( comparative research questions ), 1 , 5 , 14 or elucidate trends and interactions among variables ( relationship research questions ). 1 , 5 We provide examples of descriptive, comparative, and relationship research questions in quantitative research in Table 2 .

Hypotheses in quantitative research

In quantitative research, hypotheses predict the expected relationships among variables. 15 Relationships among variables that can be predicted include 1) between a single dependent variable and a single independent variable ( simple hypothesis ) or 2) between two or more independent and dependent variables ( complex hypothesis ). 4 , 11 Hypotheses may also specify the expected direction to be followed and imply an intellectual commitment to a particular outcome ( directional hypothesis ) 4 . On the other hand, hypotheses may not predict the exact direction and are used in the absence of a theory, or when findings contradict previous studies ( non-directional hypothesis ). 4 In addition, hypotheses can 1) define interdependency between variables ( associative hypothesis ), 4 2) propose an effect on the dependent variable from manipulation of the independent variable ( causal hypothesis ), 4 3) state a negative relationship between two variables ( null hypothesis ), 4 , 11 , 15 4) replace the working hypothesis if rejected ( alternative hypothesis ), 15 explain the relationship of phenomena to possibly generate a theory ( working hypothesis ), 11 5) involve quantifiable variables that can be tested statistically ( statistical hypothesis ), 11 6) or express a relationship whose interlinks can be verified logically ( logical hypothesis ). 11 We provide examples of simple, complex, directional, non-directional, associative, causal, null, alternative, working, statistical, and logical hypotheses in quantitative research, as well as the definition of quantitative hypothesis-testing research in Table 3 .

Research questions in qualitative research

Unlike research questions in quantitative research, research questions in qualitative research are usually continuously reviewed and reformulated. The central question and associated subquestions are stated more than the hypotheses. 15 The central question broadly explores a complex set of factors surrounding the central phenomenon, aiming to present the varied perspectives of participants. 15

There are varied goals for which qualitative research questions are developed. These questions can function in several ways, such as to 1) identify and describe existing conditions ( contextual research question s); 2) describe a phenomenon ( descriptive research questions ); 3) assess the effectiveness of existing methods, protocols, theories, or procedures ( evaluation research questions ); 4) examine a phenomenon or analyze the reasons or relationships between subjects or phenomena ( explanatory research questions ); or 5) focus on unknown aspects of a particular topic ( exploratory research questions ). 5 In addition, some qualitative research questions provide new ideas for the development of theories and actions ( generative research questions ) or advance specific ideologies of a position ( ideological research questions ). 1 Other qualitative research questions may build on a body of existing literature and become working guidelines ( ethnographic research questions ). Research questions may also be broadly stated without specific reference to the existing literature or a typology of questions ( phenomenological research questions ), may be directed towards generating a theory of some process ( grounded theory questions ), or may address a description of the case and the emerging themes ( qualitative case study questions ). 15 We provide examples of contextual, descriptive, evaluation, explanatory, exploratory, generative, ideological, ethnographic, phenomenological, grounded theory, and qualitative case study research questions in qualitative research in Table 4 , and the definition of qualitative hypothesis-generating research in Table 5 .

Qualitative studies usually pose at least one central research question and several subquestions starting with How or What . These research questions use exploratory verbs such as explore or describe . These also focus on one central phenomenon of interest, and may mention the participants and research site. 15

Hypotheses in qualitative research

Hypotheses in qualitative research are stated in the form of a clear statement concerning the problem to be investigated. Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes. 2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed-methods research question can be developed. 1

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

Research questions followed by hypotheses should be developed before the start of the study. 1 , 12 , 14 It is crucial to develop feasible research questions on a topic that is interesting to both the researcher and the scientific community. This can be achieved by a meticulous review of previous and current studies to establish a novel topic. Specific areas are subsequently focused on to generate ethical research questions. The relevance of the research questions is evaluated in terms of clarity of the resulting data, specificity of the methodology, objectivity of the outcome, depth of the research, and impact of the study. 1 , 5 These aspects constitute the FINER criteria (i.e., Feasible, Interesting, Novel, Ethical, and Relevant). 1 Clarity and effectiveness are achieved if research questions meet the FINER criteria. In addition to the FINER criteria, Ratan et al. described focus, complexity, novelty, feasibility, and measurability for evaluating the effectiveness of research questions. 14

The PICOT and PEO frameworks are also used when developing research questions. 1 The following elements are addressed in these frameworks, PICOT: P-population/patients/problem, I-intervention or indicator being studied, C-comparison group, O-outcome of interest, and T-timeframe of the study; PEO: P-population being studied, E-exposure to preexisting conditions, and O-outcome of interest. 1 Research questions are also considered good if these meet the “FINERMAPS” framework: Feasible, Interesting, Novel, Ethical, Relevant, Manageable, Appropriate, Potential value/publishable, and Systematic. 14

As we indicated earlier, research questions and hypotheses that are not carefully formulated result in unethical studies or poor outcomes. To illustrate this, we provide some examples of ambiguous research question and hypotheses that result in unclear and weak research objectives in quantitative research ( Table 6 ) 16 and qualitative research ( Table 7 ) 17 , and how to transform these ambiguous research question(s) and hypothesis(es) into clear and good statements.

a These statements were composed for comparison and illustrative purposes only.

b These statements are direct quotes from Higashihara and Horiuchi. 16

a This statement is a direct quote from Shimoda et al. 17

The other statements were composed for comparison and illustrative purposes only.

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

To construct effective research questions and hypotheses, it is very important to 1) clarify the background and 2) identify the research problem at the outset of the research, within a specific timeframe. 9 Then, 3) review or conduct preliminary research to collect all available knowledge about the possible research questions by studying theories and previous studies. 18 Afterwards, 4) construct research questions to investigate the research problem. Identify variables to be accessed from the research questions 4 and make operational definitions of constructs from the research problem and questions. Thereafter, 5) construct specific deductive or inductive predictions in the form of hypotheses. 4 Finally, 6) state the study aims . This general flow for constructing effective research questions and hypotheses prior to conducting research is shown in Fig. 1 .

Research questions are used more frequently in qualitative research than objectives or hypotheses. 3 These questions seek to discover, understand, explore or describe experiences by asking “What” or “How.” The questions are open-ended to elicit a description rather than to relate variables or compare groups. The questions are continually reviewed, reformulated, and changed during the qualitative study. 3 Research questions are also used more frequently in survey projects than hypotheses in experiments in quantitative research to compare variables and their relationships.

Hypotheses are constructed based on the variables identified and as an if-then statement, following the template, ‘If a specific action is taken, then a certain outcome is expected.’ At this stage, some ideas regarding expectations from the research to be conducted must be drawn. 18 Then, the variables to be manipulated (independent) and influenced (dependent) are defined. 4 Thereafter, the hypothesis is stated and refined, and reproducible data tailored to the hypothesis are identified, collected, and analyzed. 4 The hypotheses must be testable and specific, 18 and should describe the variables and their relationships, the specific group being studied, and the predicted research outcome. 18 Hypotheses construction involves a testable proposition to be deduced from theory, and independent and dependent variables to be separated and measured separately. 3 Therefore, good hypotheses must be based on good research questions constructed at the start of a study or trial. 12

In summary, research questions are constructed after establishing the background of the study. Hypotheses are then developed based on the research questions. Thus, it is crucial to have excellent research questions to generate superior hypotheses. In turn, these would determine the research objectives and the design of the study, and ultimately, the outcome of the research. 12 Algorithms for building research questions and hypotheses are shown in Fig. 2 for quantitative research and in Fig. 3 for qualitative research.

EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

• EXAMPLE 1. Descriptive research question (quantitative research)
• - Presents research variables to be assessed (distinct phenotypes and subphenotypes)
• “BACKGROUND: Since COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts.
• RESEARCH QUESTION: Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? ” 19
• EXAMPLE 2. Relationship research question (quantitative research)
• - Shows interactions between dependent variable (static postural control) and independent variable (peripheral visual field loss)
• “Background: Integration of visual, vestibular, and proprioceptive sensations contributes to postural control. People with peripheral visual field loss have serious postural instability. However, the directional specificity of postural stability and sensory reweighting caused by gradual peripheral visual field loss remain unclear.
• Research question: What are the effects of peripheral visual field loss on static postural control ?” 20
• EXAMPLE 3. Comparative research question (quantitative research)
• - Clarifies the difference among groups with an outcome variable (patients enrolled in COMPERA with moderate PH or severe PH in COPD) and another group without the outcome variable (patients with idiopathic pulmonary arterial hypertension (IPAH))
• “BACKGROUND: Pulmonary hypertension (PH) in COPD is a poorly investigated clinical condition.
• RESEARCH QUESTION: Which factors determine the outcome of PH in COPD?
• STUDY DESIGN AND METHODS: We analyzed the characteristics and outcome of patients enrolled in the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) with moderate or severe PH in COPD as defined during the 6th PH World Symposium who received medical therapy for PH and compared them with patients with idiopathic pulmonary arterial hypertension (IPAH) .” 21
• EXAMPLE 4. Exploratory research question (qualitative research)
• - Explores areas that have not been fully investigated (perspectives of families and children who receive care in clinic-based child obesity treatment) to have a deeper understanding of the research problem
• “Problem: Interventions for children with obesity lead to only modest improvements in BMI and long-term outcomes, and data are limited on the perspectives of families of children with obesity in clinic-based treatment. This scoping review seeks to answer the question: What is known about the perspectives of families and children who receive care in clinic-based child obesity treatment? This review aims to explore the scope of perspectives reported by families of children with obesity who have received individualized outpatient clinic-based obesity treatment.” 22
• EXAMPLE 5. Relationship research question (quantitative research)
• - Defines interactions between dependent variable (use of ankle strategies) and independent variable (changes in muscle tone)
• “Background: To maintain an upright standing posture against external disturbances, the human body mainly employs two types of postural control strategies: “ankle strategy” and “hip strategy.” While it has been reported that the magnitude of the disturbance alters the use of postural control strategies, it has not been elucidated how the level of muscle tone, one of the crucial parameters of bodily function, determines the use of each strategy. We have previously confirmed using forward dynamics simulations of human musculoskeletal models that an increased muscle tone promotes the use of ankle strategies. The objective of the present study was to experimentally evaluate a hypothesis: an increased muscle tone promotes the use of ankle strategies. Research question: Do changes in the muscle tone affect the use of ankle strategies ?” 23

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

• EXAMPLE 1. Working hypothesis (quantitative research)
• - A hypothesis that is initially accepted for further research to produce a feasible theory
• “As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness .” 24
• “In conclusion, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response . The difference in perceived safety of these agents in COVID-19 illness could be related to the more potent efficacy to reduce fever with ibuprofen compared to acetaminophen. Compelling data on the benefit of fever warrant further research and review to determine when to treat or withhold ibuprofen for early stage fever for COVID-19 and other related viral illnesses .” 24
• EXAMPLE 2. Exploratory hypothesis (qualitative research)
• - Explores particular areas deeper to clarify subjective experience and develop a formal hypothesis potentially testable in a future quantitative approach
• “We hypothesized that when thinking about a past experience of help-seeking, a self distancing prompt would cause increased help-seeking intentions and more favorable help-seeking outcome expectations .” 25
• “Conclusion
• Although a priori hypotheses were not supported, further research is warranted as results indicate the potential for using self-distancing approaches to increasing help-seeking among some people with depressive symptomatology.” 25
• EXAMPLE 3. Hypothesis-generating research to establish a framework for hypothesis testing (qualitative research)
• “We hypothesize that compassionate care is beneficial for patients (better outcomes), healthcare systems and payers (lower costs), and healthcare providers (lower burnout). ” 26
• Compassionomics is the branch of knowledge and scientific study of the effects of compassionate healthcare. Our main hypotheses are that compassionate healthcare is beneficial for (1) patients, by improving clinical outcomes, (2) healthcare systems and payers, by supporting financial sustainability, and (3) HCPs, by lowering burnout and promoting resilience and well-being. The purpose of this paper is to establish a scientific framework for testing the hypotheses above . If these hypotheses are confirmed through rigorous research, compassionomics will belong in the science of evidence-based medicine, with major implications for all healthcare domains.” 26
• EXAMPLE 4. Statistical hypothesis (quantitative research)
• - An assumption is made about the relationship among several population characteristics ( gender differences in sociodemographic and clinical characteristics of adults with ADHD ). Validity is tested by statistical experiment or analysis ( chi-square test, Students t-test, and logistic regression analysis)
• “Our research investigated gender differences in sociodemographic and clinical characteristics of adults with ADHD in a Japanese clinical sample. Due to unique Japanese cultural ideals and expectations of women's behavior that are in opposition to ADHD symptoms, we hypothesized that women with ADHD experience more difficulties and present more dysfunctions than men . We tested the following hypotheses: first, women with ADHD have more comorbidities than men with ADHD; second, women with ADHD experience more social hardships than men, such as having less full-time employment and being more likely to be divorced.” 27
• “Statistical Analysis
• ( text omitted ) Between-gender comparisons were made using the chi-squared test for categorical variables and Students t-test for continuous variables…( text omitted ). A logistic regression analysis was performed for employment status, marital status, and comorbidity to evaluate the independent effects of gender on these dependent variables.” 27

EXAMPLES OF HYPOTHESIS AS WRITTEN IN PUBLISHED ARTICLES IN RELATION TO OTHER PARTS

• EXAMPLE 1. Background, hypotheses, and aims are provided
• “Pregnant women need skilled care during pregnancy and childbirth, but that skilled care is often delayed in some countries …( text omitted ). The focused antenatal care (FANC) model of WHO recommends that nurses provide information or counseling to all pregnant women …( text omitted ). Job aids are visual support materials that provide the right kind of information using graphics and words in a simple and yet effective manner. When nurses are not highly trained or have many work details to attend to, these job aids can serve as a content reminder for the nurses and can be used for educating their patients (Jennings, Yebadokpo, Affo, & Agbogbe, 2010) ( text omitted ). Importantly, additional evidence is needed to confirm how job aids can further improve the quality of ANC counseling by health workers in maternal care …( text omitted )” 28
• “ This has led us to hypothesize that the quality of ANC counseling would be better if supported by job aids. Consequently, a better quality of ANC counseling is expected to produce higher levels of awareness concerning the danger signs of pregnancy and a more favorable impression of the caring behavior of nurses .” 28
• “This study aimed to examine the differences in the responses of pregnant women to a job aid-supported intervention during ANC visit in terms of 1) their understanding of the danger signs of pregnancy and 2) their impression of the caring behaviors of nurses to pregnant women in rural Tanzania.” 28
• EXAMPLE 2. Background, hypotheses, and aims are provided
• “We conducted a two-arm randomized controlled trial (RCT) to evaluate and compare changes in salivary cortisol and oxytocin levels of first-time pregnant women between experimental and control groups. The women in the experimental group touched and held an infant for 30 min (experimental intervention protocol), whereas those in the control group watched a DVD movie of an infant (control intervention protocol). The primary outcome was salivary cortisol level and the secondary outcome was salivary oxytocin level.” 29
• “ We hypothesize that at 30 min after touching and holding an infant, the salivary cortisol level will significantly decrease and the salivary oxytocin level will increase in the experimental group compared with the control group .” 29
• EXAMPLE 3. Background, aim, and hypothesis are provided
• “In countries where the maternal mortality ratio remains high, antenatal education to increase Birth Preparedness and Complication Readiness (BPCR) is considered one of the top priorities [1]. BPCR includes birth plans during the antenatal period, such as the birthplace, birth attendant, transportation, health facility for complications, expenses, and birth materials, as well as family coordination to achieve such birth plans. In Tanzania, although increasing, only about half of all pregnant women attend an antenatal clinic more than four times [4]. Moreover, the information provided during antenatal care (ANC) is insufficient. In the resource-poor settings, antenatal group education is a potential approach because of the limited time for individual counseling at antenatal clinics.” 30
• “This study aimed to evaluate an antenatal group education program among pregnant women and their families with respect to birth-preparedness and maternal and infant outcomes in rural villages of Tanzania.” 30
• “ The study hypothesis was if Tanzanian pregnant women and their families received a family-oriented antenatal group education, they would (1) have a higher level of BPCR, (2) attend antenatal clinic four or more times, (3) give birth in a health facility, (4) have less complications of women at birth, and (5) have less complications and deaths of infants than those who did not receive the education .” 30

Research questions and hypotheses are crucial components to any type of research, whether quantitative or qualitative. These questions should be developed at the very beginning of the study. Excellent research questions lead to superior hypotheses, which, like a compass, set the direction of research, and can often determine the successful conduct of the study. Many research studies have floundered because the development of research questions and subsequent hypotheses was not given the thought and meticulous attention needed. The development of research questions and hypotheses is an iterative process based on extensive knowledge of the literature and insightful grasp of the knowledge gap. Focused, concise, and specific research questions provide a strong foundation for constructing hypotheses which serve as formal predictions about the research outcomes. Research questions and hypotheses are crucial elements of research that should not be overlooked. They should be carefully thought of and constructed when planning research. This avoids unethical studies and poor outcomes by defining well-founded objectives that determine the design, course, and outcome of the study.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

• Conceptualization: Barroga E, Matanguihan GJ.
• Methodology: Barroga E, Matanguihan GJ.
• Writing - original draft: Barroga E, Matanguihan GJ.
• Writing - review & editing: Barroga E, Matanguihan GJ.

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Article Contents

Primacy of the research question, structure of the paper, writing a research article: advice to beginners.

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Thomas V. Perneger, Patricia M. Hudelson, Writing a research article: advice to beginners, International Journal for Quality in Health Care , Volume 16, Issue 3, June 2004, Pages 191–192, https://doi.org/10.1093/intqhc/mzh053

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Writing research papers does not come naturally to most of us. The typical research paper is a highly codified rhetorical form [ 1 , 2 ]. Knowledge of the rules—some explicit, others implied—goes a long way toward writing a paper that will get accepted in a peer-reviewed journal.

A good research paper addresses a specific research question. The research question—or study objective or main research hypothesis—is the central organizing principle of the paper. Whatever relates to the research question belongs in the paper; the rest doesn’t. This is perhaps obvious when the paper reports on a well planned research project. However, in applied domains such as quality improvement, some papers are written based on projects that were undertaken for operational reasons, and not with the primary aim of producing new knowledge. In such cases, authors should define the main research question a posteriori and design the paper around it.

Generally, only one main research question should be addressed in a paper (secondary but related questions are allowed). If a project allows you to explore several distinct research questions, write several papers. For instance, if you measured the impact of obtaining written consent on patient satisfaction at a specialized clinic using a newly developed questionnaire, you may want to write one paper on the questionnaire development and validation, and another on the impact of the intervention. The idea is not to split results into ‘least publishable units’, a practice that is rightly decried, but rather into ‘optimally publishable units’.

What is a good research question? The key attributes are: (i) specificity; (ii) originality or novelty; and (iii) general relevance to a broad scientific community. The research question should be precise and not merely identify a general area of inquiry. It can often (but not always) be expressed in terms of a possible association between X and Y in a population Z, for example ‘we examined whether providing patients about to be discharged from the hospital with written information about their medications would improve their compliance with the treatment 1 month later’. A study does not necessarily have to break completely new ground, but it should extend previous knowledge in a useful way, or alternatively refute existing knowledge. Finally, the question should be of interest to others who work in the same scientific area. The latter requirement is more challenging for those who work in applied science than for basic scientists. While it may safely be assumed that the human genome is the same worldwide, whether the results of a local quality improvement project have wider relevance requires careful consideration and argument.

Once the research question is clearly defined, writing the paper becomes considerably easier. The paper will ask the question, then answer it. The key to successful scientific writing is getting the structure of the paper right. The basic structure of a typical research paper is the sequence of Introduction, Methods, Results, and Discussion (sometimes abbreviated as IMRAD). Each section addresses a different objective. The authors state: (i) the problem they intend to address—in other terms, the research question—in the Introduction; (ii) what they did to answer the question in the Methods section; (iii) what they observed in the Results section; and (iv) what they think the results mean in the Discussion.

In turn, each basic section addresses several topics, and may be divided into subsections (Table 1 ). In the Introduction, the authors should explain the rationale and background to the study. What is the research question, and why is it important to ask it? While it is neither necessary nor desirable to provide a full-blown review of the literature as a prelude to the study, it is helpful to situate the study within some larger field of enquiry. The research question should always be spelled out, and not merely left for the reader to guess.

Typical structure of a research paper

The Methods section should provide the readers with sufficient detail about the study methods to be able to reproduce the study if so desired. Thus, this section should be specific, concrete, technical, and fairly detailed. The study setting, the sampling strategy used, instruments, data collection methods, and analysis strategies should be described. In the case of qualitative research studies, it is also useful to tell the reader which research tradition the study utilizes and to link the choice of methodological strategies with the research goals [ 3 ].

The Results section is typically fairly straightforward and factual. All results that relate to the research question should be given in detail, including simple counts and percentages. Resist the temptation to demonstrate analytic ability and the richness of the dataset by providing numerous tables of non-essential results.

The Discussion section allows the most freedom. This is why the Discussion is the most difficult to write, and is often the weakest part of a paper. Structured Discussion sections have been proposed by some journal editors [ 4 ]. While strict adherence to such rules may not be necessary, following a plan such as that proposed in Table 1 may help the novice writer stay on track.

References should be used wisely. Key assertions should be referenced, as well as the methods and instruments used. However, unless the paper is a comprehensive review of a topic, there is no need to be exhaustive. Also, references to unpublished work, to documents in the grey literature (technical reports), or to any source that the reader will have difficulty finding or understanding should be avoided.

Having the structure of the paper in place is a good start. However, there are many details that have to be attended to while writing. An obvious recommendation is to read, and follow, the instructions to authors published by the journal (typically found on the journal’s website). Another concerns non-native writers of English: do have a native speaker edit the manuscript. A paper usually goes through several drafts before it is submitted. When revising a paper, it is useful to keep an eye out for the most common mistakes (Table 2 ). If you avoid all those, your paper should be in good shape.

Common mistakes seen in manuscripts submitted to this journal

Huth EJ . How to Write and Publish Papers in the Medical Sciences , 2nd edition. Baltimore, MD: Williams & Wilkins, 1990 .

Browner WS . Publishing and Presenting Clinical Research . Baltimore, MD: Lippincott, Williams & Wilkins, 1999 .

Devers KJ , Frankel RM. Getting qualitative research published. Educ Health 2001 ; 14 : 109 –117.

Docherty M , Smith R. The case for structuring the discussion of scientific papers. Br Med J 1999 ; 318 : 1224 –1225.

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Fintechs: A new paradigm of growth

Over the past decade, technological progress and innovation have catapulted the fintech sector from the fringes to the forefront of financial services. And the growth has been fast and furious, buoyed by the robust growth of the banking sector, rapid digitization, changing customer preferences, and increasing support of investors and regulators. During this decade, fintechs have profoundly reshaped certain areas of financial services with their innovative, differentiated, and customer-centric value propositions, collaborative business models, and cross-skilled and agile teams.

As of July 2023, publicly traded fintechs represented a market capitalization of $550 billion, a two-times increase versus 2019. 1 F-Prime Fintech Index. In addition, as of the same period, there were more than 272 fintech unicorns, with a combined valuation of $936 billion, a sevenfold increase from 39 firms valued at $1 billion or more five years ago. 2 Dealroom.co; McKinsey analysis.

In 2022, a market correction triggered a slowdown in this explosive growth momentum. The impact continues to be felt today. Funding and deal activity have declined across the board, and there are fewer IPOs and SPAC (special purpose acquisition company) listings, as well as a decline in new unicorn creation. The macro environment also remains challenging and uncertain. In such a scenario, fintechs are entering a new era of value creation. The last era was all about firms being experimental—taking risks and pursuing growth at all costs. In the new era, a challenged funding environment means fintechs can no longer afford to sprint. To remain competitive, they must run at a slower and steadier pace.

About the authors

This report is a collaborative effort by Lindsay Anan, Diego Castellanos Isaza, Fernando Figueiredo, Max Flötotto , André Jerenz, Alexis Krivkovich , Marie-Claude Nadeau , Tunde Olanrewaju , Zaccaria Orlando, and Alessia Vassallo, representing views from McKinsey’s Financial Services Practice.

In this report, we examine how fintechs can continue to grow in strength and relevance for customers, the overall financial ecosystem, and the world economy, even in disruptive times. Based on research and interviews with more than 100 founders, fintech and banking executives, investors, and senior ecosystem stakeholders, we have identified key themes shaping the future of fintechs. To help fintechs capitalize on these themes, we also provide a framework for sustainable growth, based on an analysis of the strategies used by long-established public companies that have weathered previous economic cycles.

Fintech growth then and now

The fintech industry raised record capital in the second half of the last decade. Venture capital (VC) funding grew from $19.4 billion in 2015 to $33.3 billion in 2020, a 17 percent year-over-year increase (see sidebar “What are fintechs?”). Deal activity increased in tandem, with the number of deals growing 1.2 times over this period.

What are fintechs?

We define fintech players as start-ups and growth companies that rely primarily on technology to conduct fundamental functions provided by financial services, thereby affecting how users store, save, borrow, invest, move, pay, and protect money. For the analysis of this report, we included the following fintech sectors: daily banking; lending; wealth management; payments; investment banking and capital markets; small and medium-size enterprise (SME) and corporate services; operations; and infrastructure (including embedded finance, and banking as a service). The analysis excluded cryptocurrency, decentralized finance, and insurtech.

The industry fared even better in 2021, thriving on the backs of the pandemic-triggered acceleration in digitization and a financial system awash with liquidity. Funding increased by 177 percent year over year to $92.3 billion, and the number of deals grew by 19 percent.

The funding surge proved to be a one-off event. Funding levels in 2022 returned to long-term trend levels as inflated growth expectations from the 2021 extraordinary results were reanchored to business-as-usual levels, and as deteriorating macroeconomic conditions and geopolitical shocks destabilized the business environment. The correction caused fintech valuations to plummet. Many private firms faced down rounds, and publicly traded fintechs lost billions of dollars in market capitalization. VC funding was hit hard globally and across sectors, dropping to $459.6 billion in 2022 from $683.1 billion in 2021. Fintech funding faced a 40 percent year-over-year funding decline, down from $92 billion to $55 billion. Yet, when analyzed over a five-year period, fintech funding as a proportion of total VC funding remained fairly stable at 12 percent, registering only a 0.5 percentage point decline in 2022.

Looking ahead, the fintech industry continues to face a challenging future, but there are several opportunities yet to be unlocked. Investors are adapting to a new financial paradigm with higher interest rates and inflation, which has altered their assessment of risk and reward. At the same time, the once-in-a-generation technology revolution under way is generating more value creation opportunities. Our research shows that revenues in the fintech industry are expected to grow almost three times faster than those in the traditional banking sector between 2022 and 2028. Compared with the 6 percent annual revenue growth for traditional banking, fintechs could post annual revenue growth of 15 percent over the next five years.

McKinsey’s research shows that revenues in the fintech industry are expected to grow almost three times faster than those in the traditional banking sector between 2023 and 2028.

These trends are also coinciding with—and in many ways catalyzing—the maturation of the fintech industry. Based on our research and interviews, three themes will shape the next chapter of fintech growth. First, fintechs will continue to benefit from the radical transformation of the banking industry, rapid digital adoption, and e-commerce growth around the world, particularly in developing economies. Second, despite short-term pressures, fintechs still have room to achieve further growth in an expanding financial-services ecosystem. And finally, not all fintechs are being hit equally hard during the market correction: fintechs in certain verticals and at particular stages of growth are more resilient than their peers.

Radical transformation of the banking industry

Banking is facing a future marked by fundamental restructuring. As our colleagues wrote recently, banks and nonbanks are competing to fulfill distinct customer needs in five cross-industry arenas in this new era: everyday banking, investment advisory, complex financing, mass wholesale intermediation, and banking as a service (BaaS). 3 Balázs Czímer, Miklós Dietz, Valéria László, and Joydeep Sengupta, “ The future of banks: A $20 trillion breakup opportunity ,” McKinsey Quarterly , December 20, 2022.

At the same time, macro tailwinds are powering the growth of fintechs and the broader financial-services ecosystem. Digital adoption is no longer a question but a reality: around 73 percent of the world’s interactions with banks now take place through digital channels.

Moreover, retail consumers globally now have the same level of satisfaction and trust in fintechs as they have with incumbent banks. 4 McKinsey Retail Banking Consumer Survey, 2021. In fact, 41 percent of retail consumers surveyed by McKinsey in 2021 said they planned to increase their fintech product exposure. The demand—and need—for fintech products is higher across developing economies. In 2022, for example, Africa had almost 800 million mobile accounts, almost half of the whole world’s total. 5 The state of the industry report on mobile money , GSM Association, April 2023.

B2B firms’ demand for fintech solutions also is growing. In 2022, 35 percent of the small and medium-size enterprises (SMEs) in the United States considered using fintechs for lending, better pricing, and integration with their existing platforms. And in Asia, 20 percent of SMEs leveraged fintechs for payments and lending. 6 McKinsey 2022 US SMB Banking Survey, 2022 (n = 955).

To capitalize on this demand, fintechs will need to keep up with fast-evolving regulations and ensure they have adequate resources and capacity to comply. Some European Union member states, such as Ireland, are bringing buy-now-pay-later providers under the scope of financial regulation. 7 Miroslav Đurić and Verena Ritter-Döring, “Regulation of buy-now-pay-later in the EU: New regime on the horizon,” Law Business Research, February 8, 2023. Meanwhile, the US Consumer Financial Protection Bureau aims to issue a proposed rule around open banking this year that would require financial institutions to share consumer data upon consumers’ requests. 8 Farouk Ferchichi, “The US is one step closer to making open banking a reality,” Finextra, January 19, 2023. This would make it necessary for fintechs to ensure they have the available resources and capacity to respond to these requests.

A nascent industry in an expanding ecosystem

The banking industry generated more than $6.5 trillion in revenues in 2022, with year-over-year growth in volume and revenue margins. 9 “ McKinsey’s Global Banking Annual Review ,” McKinsey, December 1, 2022. Given the fintech market dynamics, this suggests there is still plenty of room for further growth in both public and private markets.

In 2022, fintechs accounted for 5 percent (or $150 billion to $205 billion) of the global banking sector’s net revenue, 10 Net revenue equals revenue after risk minus direct costs. according to our analysis. We estimate this share could increase to more than $400 billion by 2028, 11 Estimate based on historical growth at regional level and expert inputs from regional leaders in the banking industry (for example, forecast of roughly 80 percent 2021–22 revenue increase in Latin America). representing a 15 percent annual growth rate of fintech revenue between 2022 and 2028, three times the overall banking industry’s growth rate of roughly 6 percent (Exhibit 1).

Emerging markets will fuel much of this revenue growth. Fintech revenues in Africa, Asia–Pacific (excluding China), Latin America, and the Middle East represented 15 percent of fintech’s global revenues last year. We estimate that they will increase to 29 percent in aggregate by 2028. On the other hand, North America, currently accounting for 48 percent of worldwide fintech revenues, is expected to decrease its share to 41 percent by 2028.

While fintech penetration in emerging markets is already the highest in the world, its growth potential is underscored by a few trends. Many of these economies lack access to traditional banking services and have a high share of underbanked population. Fintechs have had some success in addressing these unmet needs. In Brazil, for example, 46 percent of the adult population is said to be using Nubank, a fintech bank in Latin America—double the share two years ago. 12 Oliver Smith, “Nubank turns $141m profit in Q1 as Brazilian market share nears 50%,” AltFi, May 16, 2023.

Moreover, while the market cap of private fintech companies has increased substantially over the past decade, the sector’s penetration of the public market remains small. 13 Michael Gilroy, Chase Packard, and Leslie Wang, Fintech and the pursuit of the prize: Who stands to win over the next decade? , Coatue, October 24, 2022. In the eight years leading up to October 2022, 44 modern fintechs (those that were founded in 1999 or later and went public after 2014) did an IPO, creating a combined market cap of $0.3 trillion. In contrast, during the same period, there were more than 2,500 legacy public financial-services companies (whose average year of founding was 1926) with a combined market cap of $11.1 trillion. 14 Michael Gilroy, Chase Packard, and Leslie Wang, Fintech and the pursuit of the prize: Who stands to win over the next decade? , Coatue, October 24, 2022.

Not all fintech businesses are created (or funded) equal

Last year was turbulent for fintechs, but there were differences in the fundraising performance of firms based on maturity and segments.

Maturity stage

Companies in the growth stage (series C and beyond) showed the highest sensitivity to last year’s funding downturn, with a sharp year-over-year funding decline of 50 percent. Meanwhile, fintechs in the early seed and pre-seed stages were more resilient and increased funding by 26 percent year over year (Exhibit 2). This funding outperformance of firms in the early and pre-seed stages was a consequence of the longer time to maturity, which gives start-ups more time to get through periods of economic uncertainty and recover any losses before an eventual sale.

Funding for B2B fintech segments last year was more resilient than for those in B2C, with smaller funding declines (Exhibit 3). The two B2B verticals that were least affected were (1) BaaS and embedded finance and (2) SME and corporate value-added services. These two verticals recorded year-over-year funding declines of 24 and 26 percent, respectively. In contrast, funding for payments-focused fintechs dropped 50 percent. Even then, payments and lending received the largest shares of total fintech funding.

Funding for B2B segments grew at more than 25 percent annually between 2018 and 2022, driven by an increasing number of businesses adopting off-the-shelf solutions provided by digital-native firms (including payments, open banking, and core banking technology) to address challenges arising from using legacy banking infrastructure—for example, limited flexibility, slower speed, and high costs.

Many businesses continue to rely on legacy banking infrastructure that limits flexibility and speed and can often be more costly. To address these challenges, businesses are benefiting from using off-the-shelf solutions provided by digital natives for services such as payments, open banking, and core banking technology.

For BaaS and embedded finance, demand is led by customer-facing businesses looking to control their users’ end-to-end experience. Meanwhile, SMEs have been underserved by traditional financial-services providers, despite the fact they represent about 90 percent of businesses and more than 50 percent of employment worldwide. 15 “Small and medium enterprises (SMEs) finance,” The World Bank, accessed October 10, 2023. And in developing countries, the finance gap for micro, small, and medium-size enterprises (MSME) is estimated to be approximately $5 trillion, or 1.3 times the current level of MSME lending. 16 “MSME finance gap,” IFC, accessed October 10, 2023. Fintech firms have successfully addressed some of SMEs’ needs worldwide, especially in developing countries.

The path to sustainable growth

The current churn in the markets makes it prudent for fintechs to define their next move carefully. After all, they are operating in a much different environment than in years past. In their hypergrowth stage, fintechs had access to capital that allowed them to be bold in their business strategy. They could make revenue generation their foremost objective; profits were expected to follow.

The narrative has shifted since last year. The time between funding rounds for fintechs increased by more than five months from the first to the fourth quarter of 2022. The average value of funding rounds decreased by 50 percent over the same period. 17 “SVB’s challenges will accelerate valuation down rounds, startup mortality, and layoffs,” CB Information Services, March 15, 2023. These changes are forcing fintechs to find newer ways to extend runways and adjust their operating models to make decreasing amounts of cash last longer.

The days of growth at any cost are behind the industry, for now at least. In a liquidity-constrained environment, fintechs and their investors are emphasizing profitability, not just growth in customer adoption numbers or total revenues. “In the past, the reward went to fintechs that showed growth at all costs, which led to healthy valuations,” said one Africa-based growth equity investor. “Now it is about the sustainability of the business, the addressable market, and profitability.”

In a liquidity-constrained environment, fintechs and their investors are emphasizing profitability, not just growth in customer adoption numbers or total revenues.

So how can fintechs get on a path of sustainable, profitable growth?

In 2019, McKinsey conducted an in-depth study of the growth patterns and performance of the world’s 5,000 largest public companies over the preceding 15 years. The researchers’ analysis identified ten rules for value-creating growth. 18 Chris Bradley, Rebecca Doherty, Nicholas Northcote, and Tido Röder, “ The ten rules of growth ,” McKinsey, August 12, 2022. According to the research, companies that set growth strategies addressing all available pathways to growth were 97 percent more likely to achieve above-peer profitable growth. 19 “ Choosing to grow: The leader’s blueprint ,” McKinsey, July 7, 2022.

This set of rules adopted by public companies that have lived through economic cycles and periods of uncertainty can also be useful for fintechs as they transition to a sustainable growth model. Based on our analysis of these rules and interviews with more than 40 fintech industry leaders, we expect four pathways to deliver the most impact for fintechs.

Cost discipline

When fintechs had access to abundant cash and funding was easy, they placed more emphasis on growing rapidly than on managing costs. Targeted cost savings have become a bigger priority today, as fintechs seek ways to lower expenses and achieve profitability while maintaining customer satisfaction and pursuing customer growth and acquisition. Our research has found that 50 percent of public fintechs (following their IPO) were profitable in 2022. And the key differentiator between profitable and nonprofitable fintechs was cost management, not revenue growth (Exhibit 4). While both categories recorded year-over-year revenue growth of 13 percent, profitable fintechs posted a median 3 percent decrease in costs. Nonprofitable fintechs, in contrast, saw costs rise by 27 percent, which affected their profit margins.

Successful implementation of cost management efforts is the key for fintechs in their next phase of evolution. Several leaders are already making moves: 60 percent of our survey respondents said their firms are significantly managing costs. An executive at an African mobile payments firm said they are now negotiating every cost and making sure the firm is thinking for the long run.

Consider the example of the Indian fintech company Paytm, which specializes in digital payments and financial services. The firm had had a target of achieving breakeven by September 2023 but was able to achieve this six months ahead of schedule. It did so through disciplined cost management, revenue growth across businesses, and a business model with strong operating leverage. 20 “Our discipline in cost management sustains and grows profitability,” Paytm, February 20, 2023.

While fintechs establish a clear focus on costs, they should also consider adjusting how they operate, thereby creating a more agile and flexible organization that can deal with the current environment. Around 80 percent of the interviewed fintechs report that they are currently making changes to their operating models. Of these, 66 percent cite a focus on profitability and a sustainable cost structure as being among their top three reasons. Such adjustments to the operating model are most sustainable when institutions also reinforce the control functions to protect customers and stay on top of regulatory changes.

A shift from hypergrowth to sustainable growth would also result in a greater focus on strong unit economics. To do this, fintechs ensure that the profitability view is embedded across the business. For example, assessment of the value of adding new customers would evolve from efficiency-only metrics such as the customer acquisition cost (CAC) to a more holistic approach. In this example, one way to embed profitability into acquisition investment and decision making is to compare the CAC with the projected lifetime value (LTV) of a customer, using the LTV/CAC ratio to assess the marginal return on investment for acquiring every new customer. In Latin America, for example, 68 percent of fintechs self-reported an LTV/CAC greater than five, which indicates a potential for fintechs to increase spending and further fuel growth without sacrificing profitability.

Measured growth

As leaders develop growth strategies, an important question is where growth should come from. Fintechs can grow sustainably by taking three steps: building a strong core, expanding into adjacent industries and geographies, and shrinking to grow. Identifying which steps will be most accretive to growth will depend on the unique circumstances of each fintech; some might find value in pursuing all three steps, while others could choose to focus on one. Regardless of the circumstances, this decision will have greater longer-term consequences in the current environment, compared with the earlier high-funding phase.

Focus on building a strong core as a precursor for expansion

The first step in cracking the growth code involves focusing on the local market and developing a healthy core business. According to our research, companies that focus on their core business and have a strong home market are 1.6 times more likely to generate peer-beating returns. 21 Chris Bradley, Rebecca Doherty, Tido Röder, and Jill Zucker, “ Growth rules: Which matter most? ,” McKinsey, March 6, 2023.

For fintechs, the key will be to relentlessly focus on growth in their core business. As a North American fintech executive told us: “It’s a bit of back to basics. On a core product or offering, 18 to 24 months ago, you would have built additional pieces on it to upsell and cross-sell. Now, we’re looking to double down on the core business and make sure it’s a stable, viable operation.”

To do this, fintechs must tailor their value propositions to their focus markets. Let’s take the example of B2C fintechs. Our recent research (McKinsey’s Retail Banking Consumer Survey and Global Banking Pools ) quantified the potential drivers for growth at B2C fintechs. Cross-selling will likely drive growth for fintechs in emerging economies, while those in developed countries will likely see greater growth from capturing new customers. Around 72 percent of revenue growth for companies in Brazil, for example, is expected to come from cross-selling, in contrast with 25 percent and 30 percent for the United Kingdom and the United States, respectively, with the remaining growth coming from new customers (Exhibit 5). There is arguably less potential for new-customer development in developing economies, given their high fintech penetration.

Across the competitive landscape, as markets are highly heterogenous, a dedicated strategy for each region is recommended. For example, our analysis found that in the United Kingdom and the United States, fintech revenue share is split almost equally between incumbent digital banks and pure fintech players. In contrast, digital incumbents in Germany and pure fintech players in Brazil could dominate banking’s revenue share in their respective markets.

Expand into adjacent segments and geographies

After building a strong core, fintechs can consider expanding into other segments and geographies as a second source of growth. According to our previously published research, companies that do so are 1.2 to 1.3 times more likely to generate sizable returns than peers that focus solely on their core. 22 Chris Bradley, Rebecca Doherty, Tido Röder, and Jill Zucker, “ Growth rules: Which matter most? ,” McKinsey, March 6, 2023.

Today, however, expansion is no longer a must-do strategy. It may be most advantageous for companies that have strong footholds in their core markets and can use some competitive or ownership advantage to expand elsewhere. The key is to pursue measured, value-creating growth. A case in point is OPay, which started as a mobile money platform in Nigeria and has since expanded across financial-services verticals. OPay now offers peer-to-peer payments and merchant and card services.

Shrink to grow

Fintechs are moving from hypergrowth to sustainable growth, but that growth may not necessarily be consistent across all parts of the business. If fintechs divest from underperforming parts of their portfolios and scale back from regions recording limited growth, they can reinvest that capital into high-performing segments—a strategy we call “shrinking to grow.” In our research, companies that use this approach are 1.4 times more likely to outperform their peers.

“In the past, many fintechs expanded geographically, even if it didn’t make much sense,” an executive at a Latin American fintech told us. “Now they will have to focus on their profitable segment and geography and stop expanding where they are not.”

Some fintechs have been deliberate about using a shrink-to-grow strategy, changing track if an expansion strategy did not materialize as expected or the local market had more potential for growth. German robo-adviser Scalable Capital, for example, announced plans to discontinue its Swiss operations as of 2020 to focus on other markets because the implementation of the Financial Services Act in Switzerland would have required the company to manage two regulatory frameworks simultaneously. Meanwhile, Wealthsimple, a Canadian online investment platform, exited from the United Kingdom and the United States in 2021 to concentrate on its local retail market and expand its product portfolio into new financial-services areas. Similarly, in late 2020, San Francisco–based fintech LendingClub shut down its retail peer-to-peer platform called Notes to focus on other products.

Programmatic M&A

Many companies will conclude they can achieve the steps outlined in this report—launching new features, building new capabilities, and pivoting toward new revenue streams and segments—more swiftly through thoughtful acquisitions and partnerships than by relying on pure organic development. Fintech firm Block, for example, completed its acquisition of the buy-now-pay-later platform Afterpay in January 2022 to accelerate its strategic priorities for its seller and cash app ecosystems. 23 “Block, Inc. completes acquisition of Afterpay,” Block, January 31, 2022. Nearly 60 percent of fintech executives in our survey told us they are considering an acquisition in the next 18 months.

Moreover, with IPO and SPAC (special purpose acquisition company) activity slowing considerably since last year, many fintechs that might otherwise go public are turning to private markets for funding. Take the example of the British fintech Zopa, which intended to list by 2022 but eventually decided to put IPO plans on hold in response to challenging market conditions. In the interim, the firm has been raising capital from its shareholders, including $92 million in February. 24 “Zopa raises £75 million,” Zopa Bank Limited, February 1, 2023.

M&A transactions increase significantly during periods of economic uncertainty, when they also tend to deliver higher returns. During the global financial crisis, around 45 percent of banking M&A deals showed positive excess two-year total shareholder returns (TSR) between 2007 and 2009. 25 As of the year of the deal’s announcement. In comparison, less than 30 percent of banking deals posted positive excess two-year TSR between 2010 and 2020. 26 McKinsey Fintech Quarterly Radar, Q1 2023. Across industries, companies actively making acquisitions worth 10 percent or more of their market cap in total had an average TSR of 6.4 percent between January 2007 and January 2008, compared with −3.4 percent for the less active companies. 27 Brian Salsberg, “The case for M&A in a downturn,” Harvard Business Review , May 2020.

However, not all M&As are successful. Many fail to create value due to contrasting values and cultures, mismatched product–market fit, and inflated revenue forecasts in the pursuit of customer engagement and growth at all costs.

Keeping the culture alive

What has made fintechs so disruptive over the years? The answer lies largely in their ability to innovate and differentiate. Since fintechs are not as encumbered by legacy systems and processes, they can be more agile in using emerging technologies to anticipate and solve customer needs. Typically, they also have a customer-centric and collaborative approach to deliver innovation with cross-skilled teams.

Innovations have happened across fintech verticals. Neobanks like Chime and Monzo, designed around a simple and intuitive user experience, have changed assumptions about the role of branches in traditional retail banking. In the United Kingdom, for example, the total number of bank and building society branches fell by 40 percent between 2012 and 2022. 28 Lorna Booth, Statistics on access to cash, bank branches and ATMs , House of Commons, September 1, 2023. Robo-advisers such as Wealthfront and Nutmeg disrupted the traditional wealth management industry by offering low-cost, accessible alternatives to individuals lacking access to personalized financial advice. Funding Circle introduced the peer-to-peer lending concept to the financial sector, bypassing traditional banks (which had owned this relationship) and enabling direct lending between parties.

Incumbents are fast catching up with these innovations by ramping up investments in new technologies. Around 94 percent of banks in a recent survey said they plan to invest more in modern payments technology to support end user demand for better payment capabilities over the next two to three years. Of these, 65 percent said they intend to make significant or moderate levels of investment. 29 “94% of banks eyeing investment in modern payment tech, to keep pace with fintech innovation,” Finastra press release, March 8, 2023. Many incumbents are also partnering with BaaS platforms to overhaul their digital capabilities. Examples include Fifth Third Bank’s acquisition of Rize Money in May 2023 and NatWest Group’s partnership with Vodeno Group in October 2022 to create a BaaS business in the United Kingdom.

Generative AI and the future of banking

Artificial intelligence (AI) technologies are increasingly integral to the world we live in, and investors are taking notice. Generative AI is among the advanced technologies for which investments are accelerating, thanks to its potential to transform business. According to McKinsey research published in June 2023, generative AI could add the equivalent of $2.6 trillion to $4.4 trillion annually  across as many as 63 use cases.

Generative AI’s impact on the banking industry will be significant, delivering benefits beyond existing applications of AI in areas such as marketing. As our colleagues have written, this technology could generate an additional $200 billion to $340 billion annually in value, arising from around 2.8 to 4.7 percent increase in the productivity of banking’s annual revenues—if the use cases are fully implemented. 1 “ The economic potential of generative AI: The next productivity frontier ,” McKinsey, June 14, 2023. For fintech, we expect a commensurate impact, if not more, given the already high exposure to tech.

Generative AI’s impact—and resulting reinvention—will span three broad categories:

• Automation. Half of today’s work activities could be automated between 2030 and 2060, according to McKinsey estimates. 2 “ The economic potential of generative AI: The next productivity frontier ,” McKinsey, June 14, 2023. Fintech firm Intuit, for example, has introduced a generative AI operating system on its platform. Its custom-trained large language financial models specialize in solving tax, accounting, cash flow, and personal finance challenges, among others. 3 “Intuit introduces generative AI operating system with custom trained financial large language models,” Intuit press release, June 6, 2023.
• Augmenting and enhancing productivity to do work more effectively. Generative AI could enable labor productivity growth of 0.1 to 0.6 percent annually through 2040, depending on the rate of technology adoption and redeployment of workers’ time to other activities. Morgan Stanley is building an AI assistant using GPT-4 to help the organization’s wealth managers quickly find and synthesize answers from a massive internal knowledge base. 4 “Morgan Stanley Wealth Management announces key milestone in innovation journey with OpenAI,” Morgan Stanley press release, March 14, 2023.
• Acceleration. Organizations can use generative AI to extract and index knowledge to shorten innovation cycles, thereby enabling continuous innovation.

To capture these opportunities, fintechs need an ecosystem of capabilities and partners that will allow them to move fast. First movers will accrue competitive advantage as they build their capabilities and mobilize with a focus on value, rather than rushing to deliver pilots. To do this, fintechs should consider investing more in people and change management, given generative AI’s unique potential to influence the future of work. Fintechs could think about developing a medium- to longer-term talent strategy and find ways to emphasize change management and adoption. Fintechs that delay building their capabilities risk becoming the disrupted instead of the disruptors.

To retain their competitive advantage, fintechs must continue to innovate. The next big disruptor is always around the corner. Technologies like generative AI are predicted  to revolutionize the competitive landscape of finance over the next decade (see sidebar “Generative AI and the future of banking”). WeBank’s CFO Arthur Wang is one executive who appreciates the urgency. He told us, “Even though our bank has been around for almost eight years, we consider ourselves a start-up. We’re always exploring better fintech technology. WeBank’s strategy is to provide better, more inclusive financial services—to the mass population as well as small and medium-size enterprises—with leading technology. We do business 100 percent online, so we rely on technology.” 30 See “ Making financial services available to the masses through AI ,” McKinsey, August 9, 2022.

A tight labor market has also made it more challenging for fintechs to attract and hire tech talent. Our survey uncovered a shift in the perception of fintechs as riskier employers. As a Europe-based fintech executive told us: “Fintechs are less attractive now because it is clearer that it is a ‘high risk’ job compared with established institutions. On the other hand, large fintechs are laying off, which can create a new pool of talents to attract.”

In such an environment, fintechs must work toward strengthening their culture and mission and, consequently, their hiring strategy. One European payments fintech, for example, has differentiated strategies based on the profile of open roles. An executive at the firm says it has been easier to recruit people for junior roles, since these workers are more eager to join a growing organization. “It is a different story with experienced profiles—for example, management team or 35-plus years—where recruiting is more difficult and retention is crucial,” he said. To attract such people, the firm offers stock options and other incentive packages. Meanwhile, an Africa-based payments and remittances fintech casts a more global net: “We hire globally, regardless of location, gender, or race,” an executive told us. “We have no quotas and try to just find the best person for each role.”

The fintech industry is undergoing a sea change, so players will have to evolve to survive. Approaches will vary, depending on each fintech’s maturity level and its vertical and geographic focus. The framework for sustainable growth, described in this report, provides a strong foundation:

• Measured growth based on a stable core. Ensure there is a strong and stable core business with a targeted and proven market fit before expanding, rather than trying to grow while strengthening the core.
• Programmatic M&A. Pursue M&A strategically and establish mutually beneficial partnerships based on a programmatic strategy rooted in value sharing (with incumbents and other fintechs), as opposed to pursuing M&A only as a response to a low-valuation environment.
• Cost discipline. Control costs to withstand the new funding environment while remaining flexible, nimble, and compliant.
• Keep the culture alive. Maintain the agility, innovation, and culture that have been the bedrock of disruption so far.

Decisions taken today will likely set the pace for fintechs over the mid to long term. The present conditions therefore call for a careful evaluation and focused implementation.

Lindsay Anan is an alumna of McKinsey’s San Francisco office, where Alexis Krivkovich and Marie-Claude Nadeau are senior partners; Diego Castellanos Isaza is a consultant in the London office, where Fernando Figueiredo is a partner and Tunde Olanrewaju is a senior partner; Max Flötotto is a senior partner in the Munich office; André Jerenz is a partner in the Hamburg office; and Zaccaria Orlando and Alessia Vassallo are associate partners in the Milan office.

The authors wish to thank Sonia Barquin, François Dorléans, Carolyne Gathinji, Eitan Gold, Carolina Gracia, Sheinal Jayantilal, Uzayr Jeenah, Yelda Kayik, Mayowa Kuyoro, Marina Mansur, Farid Minnikhanov, Bharath Sattanathan, Rinki Singhvi, and Katharine Watson for their contributions to this report.

This report was edited by Arshiya Khullar, an editor in the Gurugram office.

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FACT SHEET: President   Biden Issues Executive Order on Safe, Secure, and Trustworthy Artificial Intelligence

Today, President Biden is issuing a landmark Executive Order to ensure that America leads the way in seizing the promise and managing the risks of artificial intelligence (AI). The Executive Order establishes new standards for AI safety and security, protects Americans’ privacy, advances equity and civil rights, stands up for consumers and workers, promotes innovation and competition, advances American leadership around the world, and more. As part of the Biden-Harris Administration’s comprehensive strategy for responsible innovation, the Executive Order builds on previous actions the President has taken, including work that led to voluntary commitments from 15 leading companies to drive safe, secure, and trustworthy development of AI. The Executive Order directs the following actions: New Standards for AI Safety and Security

As AI’s capabilities grow, so do its implications for Americans’ safety and security.  With this Executive Order, the  President directs the  most sweeping  actions  ever taken  to protect Americans from  the potential  risks  of  AI  systems :

• Require that developers of the most powerful AI systems share their safety test results and other critical information with the U.S. government.  In accordance with the Defense Production Act, the Order will require that companies developing any foundation model that poses a serious risk to national security, national economic security, or national public health and safety must notify the federal government when training the model, and must share the results of all red-team safety tests. These measures will ensure AI systems are safe, secure, and trustworthy before companies make them public. 
• Develop standards, tools, and tests to help ensure that AI systems are safe, secure, and trustworthy.  The National Institute of Standards and Technology will set the rigorous standards for extensive red-team testing to ensure safety before public release. The Department of Homeland Security will apply those standards to critical infrastructure sectors and establish the AI Safety and Security Board. The Departments of Energy and Homeland Security will also address AI systems’ threats to critical infrastructure, as well as chemical, biological, radiological, nuclear, and cybersecurity risks. Together, these are the most significant actions ever taken by any government to advance the field of AI safety.
• Protect against the risks of using AI to engineer dangerous biological materials  by developing strong new standards for biological synthesis screening. Agencies that fund life-science projects will establish these standards as a condition of federal funding, creating powerful incentives to ensure appropriate screening and manage risks potentially made worse by AI.
• Protect Americans from AI-enabled fraud and deception by establishing standards and best practices for detecting AI-generated content and authenticating official content . The Department of Commerce will develop guidance for content authentication and watermarking to clearly label AI-generated content. Federal agencies will use these tools to make it easy for Americans to know that the communications they receive from their government are authentic—and set an example for the private sector and governments around the world.
• Establish an advanced cybersecurity program to develop AI tools to find and fix vulnerabilities in critical software,  building on the Biden-Harris Administration’s ongoing AI Cyber Challenge. Together, these efforts will harness AI’s potentially game-changing cyber capabilities to make software and networks more secure.
• Order the development of a National Security Memorandum that directs further actions on AI and security,  to be developed by the National Security Council and White House Chief of Staff. This document will ensure that the United States military and intelligence community use AI safely, ethically, and effectively in their missions, and will direct actions to counter adversaries’ military use of AI.

Protecting Americans’ Privacy

Without safeguards, AI can put Americans’ privacy further at risk. AI not only makes it easier to extract, identify, and exploit personal data, but it also heightens incentives to do so because companies use data to train AI systems.  To better protect Americans’ privacy, including from the risks posed by AI, the President calls on Congress to pass bipartisan data privacy legislation to protect all Americans, especially kids, and directs the following actions:

• Protect Americans’ privacy by prioritizing federal support for accelerating the development and use of privacy-preserving techniques— including ones that use cutting-edge AI and that let AI systems be trained while preserving the privacy of the training data.  
• Strengthen privacy-preserving research   and technologies,  such as cryptographic tools that preserve individuals’ privacy, by funding a Research Coordination Network to advance rapid breakthroughs and development. The National Science Foundation will also work with this network to promote the adoption of leading-edge privacy-preserving technologies by federal agencies.
• Evaluate how agencies collect and use commercially available information —including information they procure from data brokers—and  strengthen privacy guidance for federal agencies  to account for AI risks. This work will focus in particular on commercially available information containing personally identifiable data.
• Develop guidelines for federal agencies to evaluate the effectiveness of privacy-preserving techniques,  including those used in AI systems. These guidelines will advance agency efforts to protect Americans’ data.

Advancing Equity and Civil Rights

Irresponsible uses of AI can lead to and deepen discrimination, bias, and other abuses in justice, healthcare, and housing. The Biden-Harris Administration has already taken action by publishing the  Blueprint for an AI Bill of Rights  and issuing an  Executive Order directing agencies to combat algorithmic discrimination , while enforcing existing authorities to protect people’s rights and safety.  To ensure that AI advances equity and civil rights, the President directs the following additional actions:

• Provide clear guidance to landlords, Federal benefits programs, and federal contractors  to keep AI algorithms from being used to exacerbate discrimination.
• Address algorithmic discrimination  through training, technical assistance, and coordination between the Department of Justice and Federal civil rights offices on best practices for investigating and prosecuting civil rights violations related to AI.
• Ensure fairness throughout the criminal justice system  by developing best practices on the use of AI in sentencing, parole and probation, pretrial release and detention, risk assessments, surveillance, crime forecasting and predictive policing, and forensic analysis.

Standing Up for Consumers, Patients, and Students

AI can bring real benefits to consumers—for example, by making products better, cheaper, and more widely available. But AI also raises the risk of injuring, misleading, or otherwise harming Americans.  To protect consumers while ensuring that AI can make Americans better off, the President directs the following actions:

• Advance the responsible use of AI  in healthcare and the development of affordable and life-saving drugs. The Department of Health and Human Services will also establish a safety program to receive reports of—and act to remedy – harms or unsafe healthcare practices involving AI. 
• Shape AI’s potential to transform education  by creating resources to support educators deploying AI-enabled educational tools, such as personalized tutoring in schools.

Supporting Workers

AI is changing America’s jobs and workplaces, offering both the promise of improved productivity but also the dangers of increased workplace surveillance, bias, and job displacement.  To mitigate these risks, support workers’ ability to bargain collectively, and invest in workforce training and development that is accessible to all, the President directs the following actions:

• Develop principles and best practices to mitigate the harms and maximize the benefits of AI for workers  by addressing job displacement; labor standards; workplace equity, health, and safety; and data collection. These principles and best practices will benefit workers by providing guidance to prevent employers from undercompensating workers, evaluating job applications unfairly, or impinging on workers’ ability to organize.
• Produce a report on AI’s potential labor-market impacts , and  study and identify options for strengthening federal support for workers facing labor disruptions , including from AI.

Promoting Innovation and Competition

America already leads in AI innovation—more AI startups raised first-time capital in the United States last year than in the next seven countries combined.  The Executive Order ensures that we continue to lead the way in innovation and competition through the following actions:

• Catalyze AI research across the United States  through a pilot of the National AI Research Resource—a tool that will provide AI researchers and students access to key AI resources and data—and expanded grants for AI research in vital areas like healthcare and climate change.
• Promote a fair, open, and competitive AI ecosystem  by providing small developers and entrepreneurs access to technical assistance and resources, helping small businesses commercialize AI breakthroughs, and encouraging the Federal Trade Commission to exercise its authorities.
• Use existing authorities to expand the ability of highly skilled immigrants and nonimmigrants with expertise in critical areas to study, stay, and work in the United States  by modernizing and streamlining visa criteria, interviews, and reviews.

Advancing American Leadership Abroad

AI’s challenges and opportunities are global.  The Biden-Harris Administration will continue working with other nations to support safe, secure, and trustworthy deployment and use of AI worldwide. To that end, the President directs the following actions:

• Expand bilateral, multilateral, and multistakeholder engagements to collaborate on AI . The State Department, in collaboration, with the Commerce Department will lead an effort to establish robust international frameworks for harnessing AI’s benefits and managing its risks and ensuring safety. In addition, this week, Vice President Harris will speak at the UK Summit on AI Safety, hosted by Prime Minister Rishi Sunak.
• Accelerate development and implementation of vital AI standards  with international partners and in standards organizations, ensuring that the technology is safe, secure, trustworthy, and interoperable.
• Promote the safe, responsible, and rights-affirming development and deployment of AI abroad to solve global challenges,  such as advancing sustainable development and mitigating dangers to critical infrastructure.

Ensuring Responsible and Effective Government Use of AI

AI can help government deliver better results for the American people. It can expand agencies’ capacity to regulate, govern, and disburse benefits, and it can cut costs and enhance the security of government systems. However, use of AI can pose risks, such as discrimination and unsafe decisions.  To ensure the responsible government deployment of AI and modernize federal AI infrastructure, the President directs the following actions:

• Issue guidance for agencies’ use of AI,  including clear standards to protect rights and safety, improve AI procurement, and strengthen AI deployment.  
• Help agencies acquire specified AI products and services  faster, more cheaply, and more effectively through more rapid and efficient contracting.
• Accelerate the rapid hiring of AI professionals  as part of a government-wide AI talent surge led by the Office of Personnel Management, U.S. Digital Service, U.S. Digital Corps, and Presidential Innovation Fellowship. Agencies will provide AI training for employees at all levels in relevant fields.

As we advance this agenda at home, the Administration will work with allies and partners abroad on a strong international framework to govern the development and use of AI. The Administration has already consulted widely on AI governance frameworks over the past several months—engaging with Australia, Brazil, Canada, Chile, the European Union, France, Germany, India, Israel, Italy, Japan, Kenya, Mexico, the Netherlands, New Zealand, Nigeria, the Philippines, Singapore, South Korea, the UAE, and the UK. The actions taken today support and complement Japan’s leadership of the G-7 Hiroshima Process, the UK Summit on AI Safety, India’s leadership as Chair of the Global Partnership on AI, and ongoing discussions at the United Nations. The actions that President Biden directed today are vital steps forward in the U.S.’s approach on safe, secure, and trustworthy AI. More action will be required, and the Administration will continue to work with Congress to pursue bipartisan legislation to help America lead the way in responsible innovation. For more on the Biden-Harris Administration’s work to advance AI, and for opportunities to join the Federal AI workforce, visit AI.gov .

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