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.

翻译：尽管马来西亚中小微企业占全国企业总数的96.1%，但融资渠道仍是其面临的最持久挑战之一。由于传统承销方法严重依赖信用局数据，新成立企业往往被排除在正规信贷市场之外。本研究探索将银行流水数据作为信用评估的替代数据源，以促进新兴市场金融包容性。首先，我们提出基于现金流的承销流程，利用银行流水数据实现端到端数据提取与机器学习信用评分。其次，我们引入由马来西亚某咨询公司提供的611份贷款申请人新数据集。第三，我们基于申请信息和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，融入银行流水特征可显著提升模型性能：最优模型在验证集上AUROC达到0.806，较仅使用申请信息的模型提升24.6%。最后，我们将发布匿名化银行交易数据集，以促进马来西亚新兴经济体中中小微企业金融包容性的后续研究。