用银行流水数据提升马来西亚中小企信贷评分，助力金融包容
AI-BAAM: AI-Driven Bank Statement Analytics as Alternative Data for Malaysian MSME Credit Scoring
- 基于现金流构建端到端银行流水分析流程，自动提取信贷特征
- 模型融合流水数据后，验证集AUROC达0.806，较仅用申请信息提升24.6%
- 首次公开611个马来西亚中小企业匿名交易数据集，推动本地金融研究
尽管马来西亚微小企业占所有企业的96.1%，但融资获取仍是长期难题。新成立企业常因传统风控依赖信用局数据而被排除在正式信贷市场之外。本研究探索银行流水作为替代数据源在信贷评估中的潜力，以促进新兴市场的金融包容性。首先，提出一种基于现金流的授信流程，利用银行流水实现端到端数据提取与机器学习信用评分；其次，构建了来自马来西亚咨询公司的611名贷款申请人的新数据集；第三，基于申请信息与银行交易衍生特征开发并评估信用评分模型。实证结果表明，引入银行流水特征带来显著提升，最优模型在验证集上达到AUROC 0.806，相比仅使用申请信息的模型提高24.6%。最后，将发布经匿名化处理的银行交易数据集，以支持马来西亚新兴经济体中关于中小企业金融包容性的后续研究。
原文摘要 · Abstract (English)
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.
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