Tradeteq: Machine learning critical for better SME credit scoring in trade finance
25 May 2018

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.

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