Challenging Conventional Wisdom: Why Traditional Employ‎ment and Payment Behavioral Indicators Fail to Predict Financial Risk

  • Authors

    https://doi.org/10.14419/bbyae562

    Received date: June 14, 2025

    Accepted date: July 11, 2025

    Published date: August 24, 2025

  • Behavioral Finance; Credit Risk Assessment; Employment Stability; Payment History; Behavioral Finance Theory; Risk Modeling; Complex ‎Systems Theo-ry.

  • Abstract

    This study examined behavioral patterns in financial risk assessment using a comprehensive dataset of 15,000 individual records to challenge conventional assumptions about employment stability and payment history as primary risk predictors. The analysis employed descriptive statistics, cross-tabulation methods, and comparative analysis across employment tenure categories, payment history classifications, and ‎employment status groups. Results revealed counterintuitive findings that fundamentally contradict established risk assessment theory. ‎Senior employees with 16 or more years of tenure demonstrated the highest risk rate at 10.7 percent, while veteran employees with 8-15 ‎years showed the lowest risk rate at 9.7 percent, directly challenging the linear relationship typically assumed between employment stability ‎and financial reliability. Payment history analysis revealed minimal risk differentiation across categories, with high risk rates ranging from ‎only 9.3 percent for excellent payment history to 10.4 percent for good payment history. Employment status comparisons showed nearly ‎identical risk profiles across unemployed, employed, and self-employed individuals, with high risk rates varying by less than one percentage ‎point. The most frequent high-risk behavioral combination was self-employed individuals with good payment history, representing a pattern ‎that contradicts conventional risk assessment logic. These findings suggest that traditional behavioral indicators may systematically misallocate risk across demographic segments and point toward the need for alternative risk assessment frameworks that incorporate behavioral ‎finance principles, real-time transactional data, and advanced analytical methodologies to improve accuracy and fairness in financial risk ‎evaluation‎.

  • References

    1. Abdou, H., Pointon, J., & El-Masry, A. (2016). Neural nets versus conventional techniques in credit scoring in Egyptian banking. Expert Systems with Applications, 35(3), 1275-1292. https://doi.org/10.1016/j.eswa.2007.08.030.
    2. Adadi, A., & Berrada, M. (2018). Peeking inside the black-box: A survey on explainable artificial intelligence. IEEE Access, 6, 52138-52160. https://doi.org/10.1109/ACCESS.2018.2870052.
    3. Almgren, R., & Thaler, R. H. (2008). Mental accounting and consumer choice. Marketing Science, 27(2), 210–219. https://doi.org/10.1287/mksc.1070.0337.
    4. Altman, E. I., & Sabato, G. (2007). Modelling credit risk for SMEs: Evidence from the US market. Abacus, 43(3), 332-357. https://doi.org/10.1111/j.1467-6281.2007.00234.x.
    5. Anderson Jr, E. G., Parker, G. G., & Tan, B. (2023). Strategic investments for platform launch and ecosystem growth: A dynamic analysis. Journal of Management Information Systems, 40(3), 807-839. https://doi.org/10.1080/07421222.2023.2229125.
    6. Avery, R. B., Bostic, R. W., Calem, P. S., & Canner, G. B. (2003). An overview of consumer data and credit reporting. Federal Reserve Bulletin, 89(2), 47-73. https://doi.org/10.17016/bulletin.2003.89-2.
    7. Baesens, B., Van Gestel, T., Viaene, S., Stepanova, M., Suykens, J., & Vanthienen, J. (2003). Benchmarking state-of-the-art classification algo-rithms for credit scoring. Journal of the Operational Research Society, 54(6), 627-635. https://doi.org/10.1057/palgrave.jors.2601545.
    8. Barber, B. M., & Odean, T. (2001). Boys will be boys: Gender, overconfidence, and common stock investment. The Quarterly Journal of Econom-ics, 116(1), 261-292. https://doi.org/10.1162/003355301556400.
    9. Barberis, N., Shleifer, A., & Vishny, R. (1998). A model of investor sentiment. Journal of Financial Economics, 49(3), 307–343. https://doi.org/10.1016/S0304-405X(98)00027-0.
    10. Barocas, S., & Selbst, A. D. (2016). Big data's disparate impact. California Law Review, 104(3), 671-732. https://doi.org/10.2139/ssrn.2477899.
    11. Barocas, S., Hardt, M., & Narayanan, A. (2019). Fairness and machine learning: Limitations and opportunities. MIT Press.
    12. Bartlett, R., Morse, A., Stanton, R., & Wallace, N. (2019). Consumer-lending discrimination in the FinTech era. Journal of Financial Economics, 143(1), 30-56. https://doi.org/10.1016/j.jfineco.2021.05.047.
    13. Benartzi, S., & Thaler, R. H. (1995). Myopic loss aversion and the equity premium puzzle. The Quarterly Journal of Economics, 110(1), 73–92. https://doi.org/10.2307/2118515.
    14. Berg, T., Burg, V., Gombović, A., & Puri, M. (2020). On the rise of fintechs: Credit scoring using digital footprints. The Review of Financial Stud-ies, 33(7), 2845-2897. https://doi.org/10.1093/rfs/hhz099.
    15. Bjorkegren, D., & Grissen, D. (2020). Behavior revealed in mobile phone usage predicts credit repayment. The World Bank Economic Review, 34(3), 618-634. https://doi.org/10.1093/wber/lhz006.
    16. Brown, M., & Zehnder, C. (2007). Credit reporting, relationship banking, and loan repayment. Journal of Money, Credit and Banking, 39(8), 1883-1918. https://doi.org/10.1111/j.1538-4616.2007.00086.x.
    17. Caccioli, F., Barucca, P., & Kobayashi, T. (2017). Network models of financial systemic risk: A review. Journal of Computational Social Science, 1(1), 81–114. https://doi.org/10.1007/s42001-017-0008-3.
    18. Capasso, G., Gianfrate, G., & Spinelli, M. (2020). Climate change and credit risk. Journal of Cleaner Production, 266, 121634. https://doi.org/10.1016/j.jclepro.2020.121634.
    19. Cheema, A., & Soman, D. (2006). Malleable mental accounting: The effect of flexibility on the justification of attractive spending and consumption decisions. Journal of Consumer Psychology, 16(1), 33–44. https://doi.org/10.1207/s15327663jcp1601_7.
    20. Chen, J., Huang, H., Tian, S., & Qu, Y. (2016). Feature selection for text classification with Naïve Bayes. Expert Systems with Applications, 36(3), 5432-5435. https://doi.org/10.1016/j.eswa.2008.06.054.
    21. Crook, J. N., Edelman, D. B., & Thomas, L. C. (2007). Recent developments in consumer credit risk assessment. European Journal of Operational Research, 183(3), 1447-1465. https://doi.org/10.1016/j.ejor.2006.09.100.
    22. De Bondt, W. F. M., & Thaler, R. (1985). Does the stock market overreact? Journal of Finance, 40(3), 793–805. https://doi.org/10.1111/j.1540-6261.1985.tb05004.x.
    23. Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608.
    24. Fair Isaac Corporation. (2024). Understanding your FICO score. FICO. https://www.fico.com/en/products/fico-score.
    25. Federal Reserve. (2011). Supervisory guidance on model risk management (SR 11-7). Board of Governors of the Federal Reserve System.
    26. Fotiadis, A., Vlachos, I., & Kugiumtzis, D. (2023). Detecting nonlinear interactions in complex systems: Application in financial markets. Entropy, 25(2), 370. https://doi.org/10.3390/e25020370.
    27. [27] Frederick, S., Loewenstein, G., & O'donoghue, T. (2002). Time discounting and time preference: A critical review. Journal of Economic Lit-erature, 40(2), 351-401. https://doi.org/10.1257/jel.40.2.351.
    28. Gort, C., Wang, M., & Siegrist, M. (2008). Are pension fund managers overconfident? Journal of Behavioral Finance, 9(3), 163–170. https://doi.org/10.1080/15427560802341616.
    29. Hernán, M. A., & Robins, J. M. (2020). Causal inference: What if. Chapman & Hall/CRC.
    30. Hofstede, G., Hofstede, G. J., & Minkov, M. (2010). Cultures and organizations: Software of the mind (3rd ed.). McGraw-Hill.
    31. Hsieh, D. A. (1993). Implications of nonlinear dynamics for financial risk management. Journal of Financial and quantitative Analysis, 28(1), 41-64. https://doi.org/10.2307/2331150.
    32. Hurwitz, A., & Sade, O. (2021). Does financial sophistication help with financial decisions? Review of Finance, 25(2), 327-359. https://doi.org/10.1093/rof/rfaa026.
    33. Kahneman, D. (2011). Thinking, fast and slow. Farrar, Straus and Giroux.
    34. Kahneman, D., & Frederick, S. (2002). Representativeness revisited: Attribute substitution in intuitive judgment. In T. Gilovich, D. Griffin, & D. https://doi.org/10.1017/CBO9780511808098.004.
    35. Kahneman (Eds.), Heuristics and biases: The psychology of intuitive judgment (pp. 49-81). Cambridge University Press.
    36. Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263-291. https://doi.org/10.2307/1914185
    37. Khandani, A. E., Kim, A. J., & Lo, A. W. (2010). Consumer credit-risk models via machine-learning algorithms. Journal of Banking & Finance, 34(11), 2767-2787. https://doi.org/10.2307/1914185.
    38. Kliestik, T., Misankova, M., Valaskova, K., & Svabova, L. (2020). Bankruptcy prevention: New effort to reflect on legal and social changes. Sci-ence and Engineering Ethics, 26(1), 175-191. https://doi.org/10.1016/j.jbankfin.2010.06.001.
    39. Langer, E. J. (1975). The illusion of control. Journal of Personality and Social Psychology, 32(2), 311-328. https://doi.org/10.1037/0022-3514.32.2.311.
    40. Leo, M., Sharma, S., & Maddulety, K. (2019). Machine learning in banking risk management: A literature review. Risks, 7(1), 29. https://doi.org/10.3390/risks7010029.
    41. Lessmann, S., Baesens, B., Seow, H. V., & Thomas, L. C. (2015). Benchmarking state-of-the-art classification algorithms for credit scoring: An up-date of research. European Journal of Operational Research, 247(1), 124-136. https://doi.org/10.1016/j.ejor.2015.05.030.
    42. Lin, Z.-F., Zhao, H.-H., & Sun, Z. (2025). Nonlinear modeling of financial state variables and multiscale numerical analysis. European Physical Journal Special Topics, 234, 691–705. https://doi.org/10.1140/epjs/s11734-024-01441-7.
    43. Louzada, F., Ara, A., & Fernandes, G. B. (2016). Classification methods applied to credit scoring: Systematic review and overall comparison. Sur-veys in Operations Research and Management Science, 21(2), 117-134. https://doi.org/10.1016/j.sorms.2016.10.001.
    44. Malmendier, U., & Tate, G. (2005). CEO overconfidence and corporate investment. The Journal of Finance, 60(6), 2661-2700. https://doi.org/10.1111/j.1540-6261.2005.00813.x.
    45. Markose, S. M., Giansante, S., & Rais Shaghaghi, A. (2013). Systemic risk analytics: A data-driven multi-agent financial network approach. Journal of Banking Regulation, 14(3), 230–258. https://doi.org/10.1057/jbr.2013.10.
    46. Masten, A. B., Coricelli, F., & Masten, I. (2008). Non-linear growth effects of financial development: Does financial integration matter?. Journal of international money and finance, 27(2), 295-313. https://doi.org/10.1016/j.jimonfin.2007.12.009.
    47. Modigliani, F., & Brumberg, R. (1954). Utility analysis and the consumption function: An interpretation of cross-section data. In K. K. Kurihara (Ed.), Post-Keynesian economics (pp. 388-436). Rutgers University Press. https://doi.org/10.1017/CBO9780511803161.
    48. Moore, D. A., & Healy, P. J. (2008). The trouble with overconfidence. Psychological Review, 115(2), 502–517. https://doi.org/10.1037/0033-295X.115.2.502.
    49. Pearl, J. (2009). Causality: Models, reasoning, and inference (2nd ed.). Cambridge University Press.
    50. Rieger, M. O., Wang, M., & Hens, T. (2016). Estimating cumulative prospect theory parameters from an international survey. Theory and Decision, 80(1), 115–139.
    51. Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Ma-chine Intelligence, 1(5), 206-215. https://doi.org/10.1038/s42256-019-0048-x.
    52. Sornette, D., & Cauwels, P. (2012). Dragon-kings: Mechanisms, statistical methods and empirical evidence. European Physical Journal Special Topics, 205, 1–26. https://doi.org/10.1140/epjst/e2012-01559-5.
    53. Thaler, R. (1985). Mental accounting and consumer choice. Marketing Science, 4(3), 199-214. https://doi.org/10.1287/mksc.4.3.199.
    54. Thomas, L. C., Edelman, D. B., & Crook, J. N. (2002). Credit scoring and its applications. SIAM. https://doi.org/10.1137/1.9780898718317.
    55. Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and Uncertainty, 5(4), 297–323. https://doi.org/10.1007/BF00122574.
    56. Wang, R. Y., & Strong, D. M. (1996). Beyond accuracy: What data quality means to data consumers. Journal of Management Information Systems, 12(4), 5-33. https://doi.org/10.1080/07421222.1996.11518099.
    57. Wei, M. Exploring the Role of Behavioral Finance in Investment Decisions During Economic Uncertainty. Available at SSRN 5234966.
    58. Xia, Y., Liu, C., Li, Y., & Liu, N. (2017). A boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring. Expert Systems with Applications, 78, 225-241. https://doi.org/10.1016/j.eswa.2017.02.017.
    59. Yeo, K. H. K., Lim, W. M., & Yii, K. J. (2024). Financial planning behaviour: a systematic literature review and new theory development. Journal of Financial Services Marketing, 29(3), 979-1001. https://doi.org/10.1057/s41264-023-00249-1.

  • Downloads

  • How to Cite

    Balcıoğlu, Y. S., Altındağ, E., & Karakaya, T. (2025). Challenging Conventional Wisdom: Why Traditional Employ‎ment and Payment Behavioral Indicators Fail to Predict Financial Risk. International Journal of Accounting and Economics Studies, 12(4), 585-597. https://doi.org/10.14419/bbyae562