Trade asset distribution platform Tradeteq has released a white paper aimed at demonstrating how machine learning, combined with broader data collection, can improve access to trade finance for SMEs. The paper states that traditional models - such as the Altman Z-score - use a 'linear discriminant' analysis, which is based on several accounting indicators. This presents a number of issues for SMEs - including focusing on a small number of accounting entries while ignoring valuable non-accounting information. Being based on accounting data filed on an annual basis, traditional scoring also lacks timely information. The paper argues that a good predictive credit model for trade finance lending should accommodate varying data availability across companies to increase the depth of datasets, leverage a broad set of available and emerging data sources, and utilise trade network data, including common clients, suppliers, or bank relationships, to spot irregularities and predict credit risk.
TXF Spain 2018
TXF returns for its fourth Iberian iteration: TXF Spain. Join us for more interactive discussions with Spain's leading exporters, financial institutions, ECAs, DFIs, insurers and advisers.