AI-BAAM: AI-Driven Bank Statement Analytics as Alternative Data for Malaysian MSME Credit Scoring
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866911568145416192 |
|---|---|
| author | Ng, Chun Chet Chu, Zhen Hao Lim, Jia Yu Boon, Yin Yin Low, Wei Zeng Tan, Jin Khye |
| author_facet | Ng, Chun Chet Chu, Zhen Hao Lim, Jia Yu Boon, Yin Yin Low, Wei Zeng Tan, Jin Khye |
| contents | 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 where we utilize bank statement data for end-to-end data extraction and machine learning credit scoring. Second, we introduce a novel dataset of 611 loan applicants from a Malaysian consulting firm. Third, we develop and evaluate credit scoring models based on application information and bank transaction-derived features. Empirical results demonstrate that incorporating bank statement features yields substantial improvements, with our best model achieving an AUROC of 0.806 on validation set, representing a 24.6% improvement over models using application information only. Finally, we will release the anonymized bank transaction dataset to facilitate further research on MSME financial inclusion within Malaysia's emerging economy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_16066 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AI-BAAM: AI-Driven Bank Statement Analytics as Alternative Data for Malaysian MSME Credit Scoring Ng, Chun Chet Chu, Zhen Hao Lim, Jia Yu Boon, Yin Yin Low, Wei Zeng Tan, Jin Khye Statistical Finance Artificial Intelligence Computational Engineering, Finance, and Science Computers and Society Machine Learning Risk Management 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 where we utilize bank statement data for end-to-end data extraction and machine learning credit scoring. Second, we introduce a novel dataset of 611 loan applicants from a Malaysian consulting firm. Third, we develop and evaluate credit scoring models based on application information and bank transaction-derived features. Empirical results demonstrate that incorporating bank statement features yields substantial improvements, with our best model achieving an AUROC of 0.806 on validation set, representing a 24.6% improvement over models using application information only. Finally, we will release the anonymized bank transaction dataset to facilitate further research on MSME financial inclusion within Malaysia's emerging economy. |
| title | AI-BAAM: AI-Driven Bank Statement Analytics as Alternative Data for Malaysian MSME Credit Scoring |
| topic | Statistical Finance Artificial Intelligence Computational Engineering, Finance, and Science Computers and Society Machine Learning Risk Management |
| url | https://arxiv.org/abs/2510.16066 |