Tradeteq’s Michael Boguslavsky on how machine learning can improve credit scoring for SMEs in TRF News

Michael Boguslavsky, head of AI at Tradeteq, and author of a newly-released whitepaper, “Machine Learning Credit Analytics for Trade Finance”, has written a commentary for Trade & Receivables Finance News where he discusses how machine learning techniques, combined with broader and deeper company data, can dramatically improve credit scoring for SMEs. Current scoring methods – such as forms of the Altman Z-score – are a primary reason SMEs so often fail to secure the trade finance they need, argues Boguslavsky.  Using new models, receivables finance becomes more accurate and less risky, making it a more readily available and less costly source of working capital for SMEs than ever before.

Go here to read the full article.

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