TruEra and Demyst celebrate their award at the Singapore Fintech Festival and finish in the Top 3

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Artificial intelligence solutions company TruEra this week announced that he, along with his data partner Demystify, is one of the three winners of the Global Veritas Challenge 2021 at the Singapore Fintech Festival. The winning solution competed in the credit scoring and credit profiling category, demonstrating how third-party data and AI quality management solutions can together improve the accuracy and fairness of credit decision models. .

As one of the three winning teams, TruEra and Demyst will receive a cash prize. They will also have the opportunity to further develop their solution and deploy it in banks with the financial support of the Monetary Authority of Singapore (MAS).

The first Global Veritas Challenge 2021 was organized by MAS, the ASEAN Financial Innovation Network and Accenture, under the theme “Codifying Responsible AI”. The challenge is part of Veritas’ global initiative, launched in 2019 to enable financial institutions to assess their solutions based on AI and data analytics against the principles of fairness, ethics, accountability. and transparency.

The joint solution was presented by Shameek Kundu, Head of Financial Services at TruEra, and Scott Albin, Managing Director, APAC at Demyst. He demonstrated how AI and machine learning models can be made more efficient and fair by leveraging data and third-party tools to analyze and monitor machine learning models.

“TruEra is a company built on deep innovation in ML explainability and AI quality and we are honored to be recognized by MAS at the world’s largest fintech event,” said Kundu. “Data quality, fairness and efficiency of models are critical to the success of AI and machine learning in financial services, especially when external data is used. “

“Machine learning use cases can be both extended and dramatically improved through the use of high-quality, organized and compliant external data,” added Albin. “This project clearly demonstrates the value of external data in improving accuracy and fairness. “


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