TruEra and Demyst win at the Global Veritas Challenge 2021

TruEra, along with its data partner Demyst, has emerged as one of the three winners of the Global Veritas Challenge 2021 at the Singapore Fintech Festival, the world's largest fintech event. The winning solution competed in the credit scoring and credit profiling category, demonstrating that third-party data and AI Quality management solutions together can improve the accuracy and fairness of credit decisioning models. 

TruEra and Demyst win at the Global Veritas Challenge 2021

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

The inaugural Global Veritas Challenge 2021 was organized by the Monetary Authority of Singapore (MAS), ASEAN Financial Innovation Network, and Accenture, with the theme of "Codifying Responsible AI." The Challenge is part of the overall Veritas initiative, launched in 2019 to enable financial institutions to assess their Artificial Intelligence and Data Analytics, driven solutions against the principles of Fairness, Ethics, Accountability, and Transparency (FEAT). 

The joint solution was showcased by Shameek Kundu, Head of Financial Services at TruEra, and Scott Albin, General Manager, APAC at Demyst. It demonstrated how AI and Machine Learning (ML) models can be made more effective and fair by leveraging third-party data and tools for analyzing and monitoring machine learning models. Partnering with Demyst, a software company that accelerates the deployment of external data solutions for the world's leading banks, insurers, and fintechs, TruEra demonstrated a clear and straightforward process for how its software improves model performance while achieving the goals of responsible, fair AI.

"TruEra is a company based 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. Data quality, fairness, and model effectiveness are key to the success of AI and machine learning in financial services, particularly when external data is being used," said Kundu.

"Machine learning use cases can be both expanded and dramatically improved by the use of high quality, curated, and compliant external data. This project demonstrates the clear value of external data in improving accuracy and fairness," said Albin.