标题： 基于银行交易数据的现金流承保：推进马来西亚中小微企业金融包容性 显示英文标题

标题： Cash Flow Underwriting with Bank Transaction Data: Advancing MSME Financial Inclusion in Malaysia

摘要： 尽管在马来西亚所有企业中占96.1%，微型、小型和中型企业（MSMEs）仍然面临融资获取问题，这是最持续的挑战之一。 新成立或年轻的 businesses 常常被排除在正式信用市场之外，因为传统的承销方法高度依赖信用局数据。 本研究探讨了银行对账单数据作为信用评估的替代数据源的潜力，以促进新兴市场的金融包容性。 首先，我们提出了一种基于现金流的承销流程，在该流程中我们利用银行对账单数据进行端到端的数据提取和机器学习信用评分。 其次，我们引入了一个来自马来西亚贷款机构的611个贷款申请人的新数据集。 第三，我们开发并评估了基于申请信息和银行交易衍生特征的信用评分模型。 实证结果表明，使用此类数据可以提高我们在数据集上的所有模型的性能，这可以改善新接触贷款的MSMEs的信用评分。 最后，我们计划发布匿名化的银行交易数据集，以促进马来西亚新兴经济中MSMEs金融包容性的进一步研究。 显示英文摘要

摘要： 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 or young 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. Firstly, we propose a cash flow-based underwriting pipeline where we utilise bank statement data for end to end data extraction and machine learning credit scoring. Secondly, we introduce a novel dataset of 611 loan applicants from a Malaysian lending institution. Thirdly, we develop and evaluate credit scoring models based on application information and bank transaction-derived features. Empirical results show that the use of such data boosts the performance of all models on our dataset, which can improve credit scoring for new-to-lending MSMEs. Lastly, we intend to release the anonymised bank transaction dataset to facilitate further research on MSMEs financial inclusion within Malaysia's emerging economy.