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This paper aims to improve decision making in credit portfolio management through analytical data-mining methods, which should be used as data availability and data quality of credit portfolios increase due to (semi-)automated credit decisions, improved data warehouses and heightened information needs of portfolio management.

To contribute to this fact, this paper elaborates credit portfolio analysis based on cluster analysis. This statistical method, so far mainly used in other disciplines, is able to determine “hidden” patterns within a data set by examining data similarities.

Based on several real-world credit portfolio data sets provided by a financial institution, the authors find that cluster analysis is a suitable method to determine numerous multivariate contract specifications implying high or, respectively, low profit potential.

Nevertheless, cluster analysis is a statistical method with multiple possible settings that have to be adjusted manually. Thus, various different results are possible, and as cluster analysis is an application of unsupervised learning, a validation of the results is hardly possible.

By applying this approach in credit portfolio management, companies are able to utilize the information gained when making future credit portfolio decisions and, consequently, increase their profit.

The paper at hand provides a unique structured approach on how to perform a multivariate cluster analysis of a credit portfolio by considering risk and return simultaneously. In this context, this procedure serves as a guidance on how to conduct a cluster analysis of a credit portfolio including advices for the settings of the analysis.

This study aims to investigate the intersection of financial technology (fintech) and credit risk exploring the impact of fintech on credit risk within the banking and financial sector.

Using a bibliometric analysis approach, this study comprehensively reviews existing literature to understand the evolving landscape of fintech and credit risk. Data were extracted from the Scopus database using a comprehensive query encompassing various fintech-related keywords and their synonyms.

This study pinpoints six research streams on fintech and credit risk, spanning credit risk management, risk-sharing, credit scoring, regulatory challenges, small business lending impact and consumer credit market influence. It also examines recent advancements like artificial intelligence, blockchain and big data analytics in managing risk obligations.

While this study offers a comprehensive assessment, limitations include the ever-evolving nature of technology and potential biases in the retrieval process. Researchers should consider these factors when building on this study's findings.

The findings have practical implications for financial institutions, policymakers and researchers, offering insights into the opportunities and challenges presented by fintech in credit risk management. This study highlights potential areas for the application of advanced technologies in risk assessment and mitigation.

This study underscores the transformative impact of fintech on financial services, emphasizing the potential for more inclusive access and improved risk management. It encourages further exploration of fintech's societal implications, including its role in small business lending and consumer credit markets.

This study contributes to the existing body of knowledge by conducting a thorough bibliometric review, surpassing previous analyses in scope. It encompasses an extensive set of keywords to ensure the comprehensive retrieval of relevant papers, providing a foundation for future research in the dynamic field of fintech and credit risk.

Owing to the dramatic rise of FinTech credit in the financial sector, this study describes its knowledge and intellectual structure and paves the way for future research.

The study employs citation analysis, keyword analysis, co-author analysis, co-citation analysis and bibliographic coupling on 268 peer-reviewed articles published during 2010–2021 and extracted from the Web of Science database.

Research on FinTech credit has picked up momentum from 2016, with majority contributions from China, followed by UK and USA. International Journal of Bank Marketing is found to be the most productive journal. Co-citation analysis reveals that past studies have focused on three dominant themes, viz. (a) factors that influence user intention to adopt technological products and services (b) borrowers' and lenders' characteristics that impact fund-raising in FinTech credit platforms and (c) evolution of FinTech market over the years. Bibliographic coupling reveals that recent trends in FinTech credit include (a) impact of emerging technologies like blockchain, artificial intelligence, big data on financial system, (b) factors that encourage consumers to adopt the FinTech products and services, (c) mechanisms by which FinTechs have transformed formal credit markets, (d) factors that lead to successful fundraising in FinTech platforms and (e) critical perspectives on digital lending platforms.

To the best of the authors' knowledge, this is a pioneering study undertaking an exhaustive analysis of FinTech credit as a research area. The study offers valuable insights on potential topics of research in FinTech credit domain like investigating Balance Sheet Lending Model, investigating the impact of FinTechs on financial system, and new markets by collaborating with scholars of other regions.

This paper attempts to analysis the credit risk at the angle of industrial and macroeconomic factor using grey incidence analysis method.

Credit asset quality problem is one of the obstacles limiting the further development of commercial banks; the research on credit risk becomes an important part of the implementation of a commercial bank's risk management. Different industries may have different effects on the credit risk of commercial bank. This paper proposes finding out the different incidences between industries and credit risk, as well as macroeconomics. Incidence identification method is established to investigate whether the industry and macroeconomic factor could affect an impaired loan ratio of a bank using the grey incidence analysis method.

The results indicate that the impaired loan ratio differs with diverse industry's influence and the macroeconomics also affect it. From the angle of the industry, the result can also determine the risk deviation scope in the grey risk control process which offers new content and ideas within the grey risk control.

Under the guidance of the principle of “differential treatment, differential control”, this research will help to strengthen the implementation of differentiated credit policy, focus on guiding and promoting the optimization of credit structure, so as to maintain a reasonable size of credit facilities and build a steady currency credit system.

The paper succeeds in finding the top five influent industries compared with others by using one of the newest developed theories: grey systems theory.

Although credit unions are nonprofit organizations, their objectives depend on the efficient management of their resources and credit risk aligned with the principles of the cooperative doctrine. This paper aims to propose the combined use of credit scoring and profit scoring to increase the effectiveness of the loan-granting process in credit unions.

This sample is composed by the data of personal loans transactions of a Brazilian credit union.

The analysis reveals that the use of statistical methods improves significantly the predictability of default when compared to the use of subjective techniques and the superiority of the random forests model in estimating credit scoring and profit scoring when compared to logit and ordinary least squares method (OLS) regression. The study also illustrates how both analyses can be used jointly for more effective decision-making.

Replacing subjective analysis with objective credit analysis using deterministic models will benefit Brazilian credit unions. The credit decision will be based on the input variables and on clear criteria, turning the decision-making process impartial. The joint use of credit scoring and profit scoring allows granting credit for the clients with the highest potential to pay debt obligation and, at the same time, to certify that the transaction profitability meets the goals of the organization: to be sustainable and to provide loans and investment opportunities at attractive rates to members.

The purpose of this paper is to investigate proprietary data from customers of a Southern Louisiana credit union. It analyzes the factors that contribute to an accelerated failure time (AFT) using information from customers’ credit applications as well as information provided in their credit report.

This paper investigates the factors that affect credit risk using survival analysis by employing two primary models – the AFT model and the Cox proportional hazard (PH) model. While several studies employ the Cox PH model, few use the AFT model. However, this paper concludes that the AFT model has superior predictive qualities.

This paper finds that the factors specific to borrowers and local factors play an important role in the duration of a loan.

This paper offers an easily interpretable model for determining the duration of a potential borrower. The marketing department of credit unions can then use this information to predict when a customer will default, thus allowing the credit union to intervene in a timely manner to prevent defaults. Further, the credit union can use this information to seek out customers who are less likely to default.

This study is different from the previous research due to its focus on credit unions, which have distinct characteristics. Compared to similar lending institutions, the charter of the credit union does not allow management to sell off loans to other investors.

The credit initiation process for mid‐level corporate credit card customers involves dependencies on multiple people across divisions considered as a critical function for a US financial services company. Increasing efficiency and effectiveness of the process could save time and money for the company. The purpose of this study is to analyze the process using Six Sigma DMAIC tools in order to determine inefficiencies; specifically, to decrease the number of days it takes from the time a company submits a request, to the time it is approved from 20 days to 15 days, resulting in a 25 percent improvement in throughput.

The process improvement tool used is the Six Sigma DMAIC methodology, in addition to cause‐and‐effect diagrams and the development of poka‐yokes.

This study found several areas for improvement in the process studied. Using statistical testing, bottlenecks in the process were identified. Process changes are suggested, as well as, new measures that can be implemented to prevent variance in the process.

Business operations can benefit from evaluating key processes in this way to strengthen procedures and eliminate variation. The managers at the financial services operation studied will be able to implement the recommended process to improve efficiency and throughput.

Limitations exist that may prevent the recommendations from being carried out. These limitations lie in elements that are outside the control of the credit manager, such as the actions of the sales team and the approval of executive management. Success of this project hinges on cooperation from these parties.

The process under evaluation in the study has never before been examined with such scrutiny. The outcome of the study and recommendations for improvement will be of great value to the financial services operation studied. Other service organizations, however, can learn from the Six Sigma process executed for this study as well. Six Sigma is a valuable methodology that can be applied to a wide variety of organizations and business processes.