Addressing Bias and Data Privacy Concerns in AI-Driven Credit Scoring Systems Through Cybersecurity Risk Assessment

Abstract

The increasing reliance on artificial intelligence (AI) in credit scoring has raised concerns about algorithmic bias and data privacy, necessitating robust cybersecurity risk assessment frameworks. This study investigates the role of cybersecurity risk assessment in mitigating these risks, utilizing multiple datasets, including the Home Mortgage Disclosure Act (HMDA) dataset, the Equifax Data Breach Report, the Financial Cybersecurity Incidents Database, and the MITRE ATT&CK Financial Sector Threat Intelligence Dataset. We employ statistical fairness metrics, Bayesian Probability Modeling, Markov Chain Analysis, and Monte Carlo Simulations to evaluate the extent of bias, privacy risks, and cybersecurity vulnerabilities. Findings reveal significant disparities in loan approvals, with Black applicants receiving approval rates 28% lower than White applicants (χ² = 59.83, p < 0.001), highlighting systemic bias in AI-driven credit scoring. Data privacy remains a pressing issue, as financial sector breaches affect an average of 5,069,760 individuals per incident. Insider threats pose the greatest risk, with a probability of 0.81 of leading to financial fraud. These findings underscore the urgency of integrating fairness-aware machine learning, enhancing regulatory compliance with AI governance policies, and deploying AI-driven cybersecurity tools to fortify financial AI applications against emerging threats. This research contributes to the broader discourse on ethical AI by providing a structured cybersecurity risk assessment approach to mitigate algorithmic bias and strengthen data privacy protections. Implementing these recommendations will enhance fairness, security, and transparency in AI-driven financial decision-making, ensuring compliance with evolving regulatory frameworks and fostering trust in automated credit scoring systems.