AI in Banking Risk Management and Fraud Detection in Preventing Financial Crimes and Optimizing Credit Decisions
Abstract
Artificial Intelligence is one of the major tech innovations in commercial banking, aimed to automate and streamline ways banks are using, assessing risk and worthiness of credit decisions, identifying frauds among other applications. The methodology was structured in two phases: a quantitative analysis using institutional data, followed by a qualitative exploration through expert interviews. In the quantitative phase, secondary data were collected from five financial institutions, including three commercial banks and two fintech companies in Bangladesh. The qualitative phase was conducted to explore practical and regulatory challenges of AI implementation in real-world banking contexts. A comparative analysis between traditional and AI-based credit scoring systems revealed significant improvements across all key performance indicators. The AI-based system achieved a higher loan approval rate (78%) and a lower default rate (6%), while reducing processing time from 45 to 12 minutes and significantly enhancing customer satisfaction. Legacy system incompatibility was the most often mentioned barrier (67%), followed by problems with real-time data access (61%), and the inability of AI to explain itself (55%). However, when it comes to the usage of AI, banking professionals' top concerns are explainability (80%) and human oversight (88%). The results highlight the importance of using more advanced AI models, like XGBoost and Neural Networks, to improve the precision of credit evaluations and the efficiency of fraudulent transactions in real time, particularly for formerly underserved customer segments.
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