- Data Analytics
- Risk Management
Small and Medium Enterprises (SMEs) are 2.6 times more likely to be rejected for a loan than a multinational, and the worldwide trade finance gap for SMEs is estimated at $1.7 Trillion USD. The main barrier to finance for SMEs is the high costs of due diligence during the financing process. Our research partner, a third-party logistics (3PL) provider was interested in exploring using their trade data to inform creditworthiness decisions for SMEs. Previous research has shown that alternative databases can be used to improve the risk assessment of SMEs’ creditworthiness, however, we found no evidence that supply chain operational data from 3PLs can be used to improve the creditworthiness assessment of SMEs for trade financing. Through a partnership with a 3PL with a financial institution branch, we collect insights into the challenges and opportunities for 3PLs to leverage their databases to better inform credit scoring decisions for SMEs. We also use two publicly available databases to illustrate the methodology we propose in our research for 3PLs to build their own credit scoring methodologies. We document the proposed features to be explored by 3PLs which to build their own credit scoring models. Aligned with the research on alternative databases, we conclude that the use of operational supply chain data from 3PLs can be useful to strengthen credit scoring models for trade financing of SMEs. In addition, we propose solutions to common challenges drawn from the nature of a 3PL’s data structure and initial model iterations (i.e., cold start problem, feature acquisition). Supply chain operational data from 3PLs can be leveraged to build a credit score model and could be a steppingstone for 3PLs to take a central role in the trade ecosystem.