AI-BAAM: AI-Driven Bank Statement Analytics as Alternative Data for Malaysian MSME Credit Scoring

Despite accounting for 96.1% of all businesses in Malaysia, access to financing remains one of the most persistent challenges faced by Micro, Small, and Medium Enterprises (MSMEs). Newly established businesses are often excluded from formal credit markets as traditional underwriting approaches rely heavily on credit bureau data. This study investigates the potential of bank statement data as an alternative data source for credit assessment to promote financial inclusion in emerging markets. First, we propose a cash-flow-based underwriting pipeline that uses bank statement data for end-to-end document extraction and machine-learning-based credit scoring. Second, we introduce a real-world dataset of 611 loan applicants from a Malaysian consulting firm, covering application and bank transaction records. Third, we develop and evaluate credit scoring models based on application information and bank transaction-derived features. The results show that incorporating bank statement features improves validation-set AUROC, with the best model achieving 0.806, representing a 24.6% improvement over models using application information only. Finally, we discuss the data governance considerations required for future sharing of anonymized bank transaction data to support research on MSME financial inclusion within Malaysia's emerging economy and formal credit markets.