A Global Comparison of Credit Bureaus Based on Data Utilization in Credit Scoring
DOI:
https://doi.org/10.32890/ijbf2025.20.2.2Keywords:
Credit bureaus, consumer reporting agencies, credit scoring, credit reports, data utilizationAbstract
The basic concept of credit scoring is to assess an individual`s payment ability as well as the specific individual`s credit default risk, hence determining an individual`s creditworthiness. Based on the credit score, financial institutions, insurance companies, telecommunication companies and other businesses decide whether consumers are eligible for a mortgage, credit card, auto loan, and other credit products. However, in many countries, potential tenants and insurance applicants also use credit scores extensively for screening. Accordingly, Credit Bureaus (CB) or Consumer Reporting Agencies (CRA) exert an essential gatekeeper function for important economic areas of consumers’ everyday life. However, when examining CBs globally, there are considerable differences in the use of data to calculate credit scores. Interestingly, the influence of CBs on credit rating receives little to no attention in academic research. This is particularly evident in the absence of a framework for classifying Credit Bureaus. Therefore, 24 traditional and non-traditional Credit Bureaus operating in 17 different countries are analyzed. First, the study identifies the different data types underlying credit reports and credit scores. Second, CBs are classified and clustered according to the type of information used for credit scoring. Furthermore, promising areas of research, in particular the ethical conflict between data protection and economic participation are highlighted.
References
Ali, M. S. B. (2022). Credit bureaus, corruption and banking stability. Economic Systems, 46(3), 1–8
Amat, O., Manini, R., & Renart, M. A. (2017). Credit concession through credit scoring: Analysis and application proposal. Intangible Capital, 13(1), 51–70.
Andrus, M. T. (2016). The right to be forgotten in America: Have search engines inadvertently become consumer reporting agencies? Business Law Today, 1(1), 1.
Asher, J. (1994). Look what credit scoring can do now. American Bankers Association. ABA Banking Journal, 86(5), 51.
Avery, R. B., Bostic, R. W., Calem, P. S., & Canner, G.B. (2000), Credit scoring: Statistical issues and evidence from credit-bureau files. Real Estate Economics, 28, 523-547.
Baer, T., Goland, T., & Schiff, R. (2012). New credit-risk models for the unbanked. McKinsey & Company.
Berg, T., Burg, V., Gombovic, A., & Puri, M. (2020). On the rise of Fintech: Credit scoring using digital footprints. The Review of Financial Studies, 33(7), 2845–2897.
Blechmann, J. G. (2016). Mobile credit in Kenya and Tanzania: Emerging regulatory challenges in consumer protection, credit reporting and use of customer transactional data. The African Journal of Information and Communication, 17, 61–88.
Brainard, L. (2018). What are we learning about artificial intelligence in financial services? A speech at Fintech and the new financial landscape. Philadelphia, Pennsylvania (No. 1021). Board of Governors of the Federal Reserve System (US).
Bundesgerichtshof. (2014). VI ZR 156/13. http://juris.bundesgerichtshof.de/cgi-bin/rechtsprechung/document.py?Gericht=bgh&Art=pm&sid=99f39ef8e8587ba87abfaf9e6104 0eb8&nr=66910&linked=urt&Blank=1&file=dokument.pdf
Chatterjee, S., Corbae, D., Dempsey, K. P., & Ríos-Rull, J. V. (2020). A quantitative theory of the credit score. National Bureau of Economic Research, 91(5), 1803-1840
Consumer Financial Protection Bureau. (2022). What is a credit score? Federal Reserve, Consumer Financial Protection Bureau.
Consumer Financial Protection Bureau. (2023). List of consumer reporting agencies. Federal Reserve, Consumer Financial Protection Bureau.
Cornelli, G., Frost, J., Gambacorta, L., Rau, P. R., Wardrop, R., & Ziegler, T. (2020). Fintech and big tech credit: A new database. https://papers.ssrn.com/sol3/papers.cfm? abstract_id=3707437
Djeundje, V. B., Crook, J., Calabrese, R., & Hamid, M. (2021). Enhancing credit scoring with alternative data. Expert Systems with Applications, 163, 113766.
European Banking Authority. (2020). Guidelines on loan origination and monitoring. European Banking Authority.
European Union. (2016). Regulation 2016/679. Regulation (EU 2016/679) of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation). https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32016R0679
Ferretti, F. (2013). The legal framework of consumer credit bureaus and credit scoring in the European Union: pitfalls and challenges over indebtedness, responsible lending, market integration, and fundamental rights. Suffolk University Law Review, 46(3), 791–828.
Ferretti, F. (2015). Credit bureaus between risk-management, creditworthiness assessment and prudential supervision. EUI Department of Law Research Paper (2015/20).
Finlay, S. (2012). Credit scoring, response modeling, and insurance rating: A practical guide to forecasting consumer behavior (2nd ed.). Palgrave Macmillan UK.
Francis, E., Blumenstock, J., & Robinson, J. (2017). Digital credit: A snapshot of the current landscape and open research questions. UC Berkeley: Center for Effective Global Action. https://escholarship.org/uc/item/88r1j7sz
Frost, J., Gambacorta, L., Huang, Y., Shin, H. S., & Zbinden, P. (2019). BigTech and the changing structure of financial intermediation. Economic Policy, 34(100), 761-799.
Girault, M. G., & Hwang, J. C. (2010). Public credit registries as a tool for bank regulation and supervision. World Bank Policy Research Working Paper (5489).
Golden, L. L., Brockett, P. L., Ai, J., & Kellison, B. (2016). Empirical evidence on the use of credit scoring for predicting insurance losses with psycho-social and biochemical explanations. North American Actuarial Journal, 20(3), 233–251.
Hamon, R., Junklewitz, H., & Sanchez, I. (2020). Robustness and explainability of artificial intelligence. From technical to policy solutions. JRC Technical Report. https://www.google.de/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&ved=2ahUKEwjHpsS MuMqAAxVf2QIHHYEtDrEQFnoECBMQAQ&url=https%3A%2F%2Fpublications.jrc.ec.eur opa.eu%2Frepository%2Fbitstream%2FJRC119336%2Fdpad_report.pdf&usg=AOvVaw1JlIaG Hrx9Tl2KGtptWFWK&opi=89978449
Hartwig, R. P., & Wilkinson, C. (2003). The use of credit information in personal lines insurance underwriting. Insurance Issues Series, 1(2), 1–20
Hayashi, Y. (2019, August 9). Banking & finance: New credit criteria raise fairness issue. Wall Street Journal, 2019. https://www.proquest.com/newspapers/banking-amp-finance-new-credit-criteria-raise/docview/2269852016/se-2
Hertza, V. A. (2018). Fighting unfair classifications in credit reporting: Should the United States adopt GDPR-inspired rights in regulating consumer credit. New York University Law Review, 93(1), 1707–1743.
Hiller, J. S., & Jones, L. S. (2022). Who's keeping score? Oversight of changing consumer credit infrastructure. American Business Law Journal, 59(1), 61–121.
Kozodoi, N., Lessmann, S., Papakonstantinou, K., Gatsoulis, Y., & Baesens, B. (2019). A multi-objective approach for profit-driven feature selection in credit scoring. Decision Support Systems, 120, 106–117.
Kallberg, J. G., & Udell, G. F. (2003). The value of private sector business credit information sharing: The US case. Journal of Banking & Finance, 27(3), 449–469.
Kiviat, B. (2019). Credit scoring in the United States. Economic sociology the European Electronic Newsletter, 21(1), 33-42.
Kumar, A., Sharma, S., & Mahdavi, M. (2021). Machine learning (ML) technologies for digital credit scoring in rural finance: A literature review. Risks, 9(11), 192.
Kusi, B. A., Agbloyor, E. K., Ansah-Adu, K., & Gyeke-Dako, A. (2017). Bank credit risk and credit information sharing in Africa: Does credit information sharing institutions and context matter? Research in International Business and Finance, 42, 1123–1136.
Kyeong, S., Kim, D., & Shin, J. (2021). Can system log data enhance the performance of credit scoring?—Evidence from an internet bank in Korea. Sustainability, 14(1), 130.
Lin, M., Prabhala, N. R., & Viswanathan, S. (2013). Judging borrowers by the company they keep: Friendship networks and information asymmetry in online peer-to-peer lending. Management Science, 59(1), 17–35.
Markov, A., Seleznyova, Z., & Lapshin, V. (2022). Credit scoring methods: Latest trends and points to consider. Journal of Finance and Data Science, 8, 180–201.
McCann, F., & McIndoe-Calder, T. (2015). Firm size, credit scoring accuracy and banks’ production of soft information. Applied Economics, 47(33), 3594–3611.
McIntosh, C., Sadoulet, E., Buck, S., & Rosada, T. (2013). Reputation in a public goods game: Taking the design of credit bureaus to the lab. Journal of Economic Behavior & Organization, 95, 270–285.
Miller, M. J. (2003). Credit reporting systems and the international economy. The MIT Press.
Njathi, A. (2019). The effects of mobile lending apps on bottom of the pyramid consumers in Kenya: A case of Tala FinTech Company. Journal of African Interdisciplinary Studies, 3(8), 112–126. Óskarsdóttir, M., Bravo, C [Cristián], Sarraute, C., Baesens, B., & Vanthienen, J. (2018). Credit scoring for good: Enhancing financial inclusion with smartphone-based microlending. Thirty Ninth International Conference on Information Systems. Óskarsdóttir, M., Bravo, C [Cristián], Sarraute, C., Vanthienen, J., & Baesens, B. (2019). The value of big data for credit scoring: Enhancing financial inclusion using mobile phone data and social network analytics. Applied Soft Computing, 74, 26–39.
Roa, L., Correa-Bahnsen, A., Suarez, G., Cortés-Tejada, F., Luque, M. A., & Bravo, C [C.] (2021). Super-app behavioral patterns in credit risk models: Financial, statistical and regulatory implications. Expert Systems with Applications, 169, 114486.
Saia, R., Giuliani, A., Pompianu, L., & Carta, S. (2021). From payment services directive 2 (PSD2) to credit scoring: A case study on an Italian Banking Institution. In KDIR (pp. 164-171). https://www.scitepress.org/PublishedPapers/2021/106532/106532.pdf
Shaikh, A. A., & Karjaluoto, H. (2015). Mobile banking adoption: A literature review. Telematics and Informatics, 32(1), 129–142.
Siddiqi, N. (2017). Intelligent credit scoring: Building and implementing better credit risk scorecards (2nd ed.). John Wiley & Sons.
Smith, B. C. (2011). Stability in consumer credit scores: Level and direction of FICO score drift as a precursor to mortgage default and prepayment. Journal of Housing Economics, 20(4), 285-298.
Smith, L. D., Staten, M., Eyssell, T., Karig, M., Freeborn, B. A., & Golden, A. (2013). Accuracy of information maintained by US credit bureaus: Frequency of errors and effects on consumers' credit scores. Journal of Consumer Affairs, 47, 588-601.
Spader, J. S. (2010). Beyond disparate impact: Risk-based pricing and disparity in consumer credit history scores. The Review of Black Political Economy, 37(2), 61–78.
Thorhauer, Y. (2020). Enlightenment in the digital age – Implications for applied ethics. In Y. Thorhauer & C. A. Kexel (Eds.), Facetten der digitalisierung: Chancen und herausforderungen für mensch und management (pp 7-29). Springer Gabler.
Toh, Y. L. (2023). Addressing traditional credit scores as a barrier to accessing affordable credit. Economic Review, 108(3), 1-22.
VG Wiesbaden. (2021a). 6 K 788/20.WI. Court Wiesbaden.
VG Wiesbaden. (2021b). 6 K 441/21.WI. Court Wiesbaden
Wei, Y., Yildrim, P., van den Bulte, C., & Dellarocas, C. (2016). Credit scoring with social network data. Marketing Science, 35(2), 234–258.
Wong, K. L. X., & Dobson, A. S. (2019). We’re just data: Exploring China’s social credit system in relation to digital platform ratings cultures in Westernised democracies. Global Media and China, 4(2), 220–232.
Yu, L., Li, X., Tang, L., Zhang, Z., & Kou, G. (2015). Social credit: A comprehensive literature review. Financial Innovation, 1(1), 1–18.
Published
Issue
Section
How to Cite
Research impact
Harvested 2026-09-07Counts differ between services because each indexes a different body of literature. None of them is the whole picture.
