Behavioral Patterns in Micro-lending: Enhancing Credit Risk Assessment with Collaborative Filtering and Federated Learning

Abstract

Credit risk assessment uses finance-based behavioural patterns for micro-lending purposes and organizations. The repayment behaviour and credit stability patterns are analyzed across varying repayment tenures and financed amounts. Due to limited borrower data and fluctuating financial patterns, micro-lending platforms have substantial hurdles when it comes to effectively evaluating credit risk. This article introduces a Collaborative Filtering Method using Lending Pattern Analysis (CFM-LPA). The proposed method is enhanced through collaborative federated learning, enabling the analysis of these patterns. This approach evaluates the return rate, credit limit, and consumer response behaviours. Federated learning processes one or more of these factors to assess diverse lending patterns. Based on these evaluations, the behavioural factor is updated for each return period, influencing the credit risk for subsequent return periods and supporting the financial stability of micro-lending operations. The model is trained individually on the identified factors, allowing the behavioural factor to be filtered. New credit risks are identified using this filtered factor from the previous return period. These insights help define new behavioural patterns for the specified credit limit. The proposed method enhances risk detection accuracy by 14.03% and improves return rate analysis by 13.28% across financed amounts. The above abstract is also graphically presented.

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