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Behavioral Factors in Credit Decision-Making and Risk Assessment

Surbhi Gupta

surbhiguptamca@gmail.com

Abstract- Historically, credit decisioning has been based on financial ratios, credit bureau reports, and statistical scoring models to predict loan default probability. However, emerging empirical findings in the field of behavioral economics, consumer psychology, and machine learning indicate that these conventional methods discard systematic behavioral drivers having a material impact on both lender decisions and borrower repayment patterns. This paper explores the potential of behavioral aspects, including cognitive biases, psychometric traits, as well as observed behavior “footprints” in improving credit risk evaluation. The purpose is two-fold: to develop the theoretical underpinnings of why it is that human decision makers do not follow rational-choice assumptions in lending, and to empirically establish an integrated modelling framework that supplements standard financial variables with psychometric scales and ethically sourced behavioral data. The proposed methodology aims to be fully reproducible and adhere to peer-review standards, with particular focus on methodological transparency, explainability, and fairness.

The overall COSMO-VHM structure follows a multi-scale behavioural coupling scheme. At the lender level, we examine how cognitive biases (e.g., overconfidence, anchoring, herding, and loss aversion) affect underwriting assessments, portfolio choice, and risk seeking. At the borrower level, the research incorporates psychometric traits including conscientiousness, self-control, impulsivity, and time-inconsistency using validated short-form measures. The models also incorporate summarized behaviour artifacts such as traces of behaviour from mobile usage patterns, transaction timing regularity, payment cadence, and digital footprint features. These characteristics are selected because they have been empirically demonstrated in literature on mobile-based credit scoring,  psychometric lending, and big-data credit models.

The study investigates four model design options (conventional credit scorecard with traditional financial variables, a psychometrics scorecard in conjunction with traditional credit features and behavioral indicators usage, along with demographic shocks data, and a combined model of two sets of predictors). With the 10,000 anonymized retail loan applicants data set, formal scorecard tests of comparison to select optimal models are performed. We train machine-learning models such as logistic regression, random forests, and gradient boosting with stratified cross-validation and temporal holdout splits. Finally, we evaluate performance using AUC-ROC, Brier score, precision-at-k, lift, and calibration analysis. To provide transparent models and to comply with regulations, we use Shapley Additive Explanations (SHAP) for interpretable local and global explanations, as well as fairness metrics, aiming to quantify whether there are potential disparate impacts for demographic subgroups.

We find that those behavioral enhancements provide significant predictive improvements: the cumulative model provides 7-10% AUC improvement over traditional scoring only, better calibration, and more lift at the top-decile ranges of borrower tiers. Psychometric characteristics, especially conscientiousness and self-control, are strong predictors of repayment outcomes, and behavioral rhythm indicators such as regularity of payment timing or variance in financial activity provide further discriminating power. SHAP-powered interpretability exposes invisible mechanisms: for instance, it allows us to observe patterns where some borrowers (here low low-bureau-score ones) show clear behavioral stability, which significantly decreases their predicted default risk, pointing out the necessity of integrating behavioral modelling in offering inclusive lending. The analysis of fairness shows that both variables improve decision-making, and cautious management is necessary to avoid proxy discrimination and ensure transparent communication with the borrower.

The results illustrate that behavioral insights may have a significant power to improve model fit and decision interpretability when modeled in an organized, ethically sound manner. The study also proposes prescriptive operational guidelines for the use of behavioral credit models (e.g., humanly interpretable explanations, data privacy-protection measures in the form of differential privacy and persistent calibration fairness surveillance) as well as future research work on causally validating the effectiveness of behavioral interventions and privacy-preserving learning approaches. Collectively, the study highlights that behavioral data enhances predictive accuracy of default beyond traditional scoring variables and promotes responsible, transparent, inclusive credit lending practices.

Keywords- Credit risk assessment, behavioral economics, psychometric scoring, cognitive biases, borrower behavior, digital footprints, alternative data, machine learning, explainable AI, SHAP values, credit decision-making, risk modeling, financial inclusion, predictive analytics, behavioral finance.