Machine Learning Critical for Better Sme Credit Scoring in Trade Finance

AI and broader data collection can overcome the “trade finance gap” by improving credit scoring for SMEs in trade finance, says Michael Boguslavsky, head of AI at Tradeteq and author of a white paper released today.

LONDON: Tradeteq, the trade asset distribution platform, has today released a white paper aimed at demonstrating how machine learning, combined with broader data collection, can improve access to trade finance for SMEs. Authored by Michael Boguslavsky, Tradeteq’s head of AI, and titled Machine Learning Credit Analytics for Trade Finance, the paper proposes a radical new approach to credit scoring that could particularly benefit SMEs in trade finance.

The paper states that traditional models – such as the Altman Z-score – use a “linear discriminant” analysis, which is based on several accounting indicators. While widely utilised, such scoring presents a number of issues for SMEs – including focusing on a small number of accounting entries while ignoring valuable non-accounting information. Such hard requirements make credit scoring impossible for companies that miss even one entry. Being based on accounting data filed on an annual basis, traditional scoring also lacks timely information.

“Over the years there have been many attempts to improve traditional credit scoring,” says Michael Boguslavsky, “such as adding new financial ratios or replacing the Altman Z linear approach with other models. But they have never been very successful. What’s needed are models able to leverage non-homogenous data from multiple data sources – dramatically improving both quality and timeliness of credit event prediction.”

Boguslavsky’s white 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, including geographical data,
• utilise trade network data, including common clients, suppliers, or bank relationships, to spot irregularities and predict credit risk.

It’s this approach that will allow for a broader understanding of SMEs’ credit risk, leading to fewer loan rejections and improved credit decisions, claims Boguslavsky.

“The combination of machine learning techniques with deep and broad data coverage generates a neural network model that can outperform the traditional Altman Z-score and similar models even on pure registration data,” says Boguslavsky. “And this without using any accounting inputs – hence it’s potentially revolutionary impact on SMEs seeking trade finance.”

Tradeteq’s trade asset distribution platform generates credit scoring in just such a way, with the aim of expanding the universe of trade finance investors by encouraging an “originate to distribute” model by trade finance banks. The company – officially launched in March 2018 – is now looking for partnerships and collaborations to work on transaction-level trade finance datasets, leveraging Tradeteq’s expertise in deep data analysis and the broad data sourced from partners to produce state-of-the-art credit analysis for the trade finance community.  The white paper Machine Learning Credit Analytics for Trade Finance can be downloaded here.

About Tradeteq:

• Tradeteq provides a collaborative network for trade finance investors and originators to connect, interact, and transact. Tradeteq connects trade finance originators with funders and gives them the technology to interact and transact efficiently.
• The Tradeteq Marketplace delivers AI-powered credit analytics, reporting, investment, and operational solutions – transforming trade finance assets into transparent and scalable investments able to attract institutional funding.
• The Tradeteq Marketplace helps trade financiers build an “originate-to-distribute” model – helping banks overcome balance-sheet constraints within their lending portfolio by efficiently distributing trade finance assets to a broad investor base.
• After a soft launch in 2017, Tradeteq was officially launched in March 2018.

AI and broader data collection can overcome the “trade finance gap” by improving credit scoring for SMEs in trade finance, says Michael Boguslavsky, head of AI at Tradeteq and author of a white paper released today.

LONDON: Tradeteq, the trade asset distribution platform, has today released a white paper aimed at demonstrating how machine learning, combined with broader data collection, can improve access to trade finance for SMEs. Authored by Michael Boguslavsky, Tradeteq’s head of AI, and titled Machine Learning Credit Analytics for Trade Finance, the paper proposes a radical new approach to credit scoring that could particularly benefit SMEs in trade finance.

The paper states that traditional models – such as the Altman Z-score – use a “linear discriminant” analysis, which is based on several accounting indicators. While widely utilised, such scoring presents a number of issues for SMEs – including focusing on a small number of accounting entries while ignoring valuable non-accounting information. Such hard requirements make credit scoring impossible for companies that miss even one entry. Being based on accounting data filed on an annual basis, traditional scoring also lacks timely information.

“Over the years there have been many attempts to improve traditional credit scoring,” says Michael Boguslavsky, “such as adding new financial ratios or replacing the Altman Z linear approach with other models. But they have never been very successful. What’s needed are models able to leverage non-homogenous data from multiple data sources – dramatically improving both quality and timeliness of credit event prediction.”

Boguslavsky’s white 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, including geographical data,
• utilise trade network data, including common clients, suppliers, or bank relationships, to spot irregularities and predict credit risk.

It’s this approach that will allow for a broader understanding of SMEs’ credit risk, leading to fewer loan rejections and improved credit decisions, claims Boguslavsky.

“The combination of machine learning techniques with deep and broad data coverage generates a neural network model that can outperform the traditional Altman Z-score and similar models even on pure registration data,” says Boguslavsky. “And this without using any accounting inputs – hence it’s potentially revolutionary impact on SMEs seeking trade finance.”

Tradeteq’s trade asset distribution platform generates credit scoring in just such a way, with the aim of expanding the universe of trade finance investors by encouraging an “originate to distribute” model by trade finance banks. The company – officially launched in March 2018 – is now looking for partnerships and collaborations to work on transaction-level trade finance datasets, leveraging Tradeteq’s expertise in deep data analysis and the broad data sourced from partners to produce state-of-the-art credit analysis for the trade finance community.  The white paper Machine Learning Credit Analytics for Trade Finance can be downloaded here.

About Tradeteq:

• Tradeteq provides a collaborative network for trade finance investors and originators to connect, interact, and transact. Tradeteq connects trade finance originators with funders and gives them the technology to interact and transact efficiently.
• The Tradeteq Marketplace delivers AI-powered credit analytics, reporting, investment, and operational solutions – transforming trade finance assets into transparent and scalable investments able to attract institutional funding.
• The Tradeteq Marketplace helps trade financiers build an “originate-to-distribute” model – helping banks overcome balance-sheet constraints within their lending portfolio by efficiently distributing trade finance assets to a broad investor base.
• After a soft launch in 2017, Tradeteq was officially launched in March 2018.