A consortium led by Singapore’s central bank released Veritas Toolkit 2.0 – a new version of the solution developed to improve the responsible use of artificial intelligence (AI) technology in the financial sector. Among other improvements, the toolkit brings new assessment methodologies focused on the four key principles of fairness, ethics, accountability, and transparency.

MAS Launches New Version of Veritas Toolkit

The Monetary Authority of Singapore (MAS), the city-state’s central bank and financial regulator, launched a new version of its open-source toolkit developed to guide financial industry players regarding the responsible use of AI in their products and offerings. The solution, known as the ‘Veritas Toolkit version 2.01,’ helps financial institutions to implement “assessment methodologies for the Fairness, Ethics, Accountability, and Transparency (FEAT) principles.”

“The FEAT principles provide guidance to firms offering financial products and services on the responsible use of AI and data analytics.”

– the MAS said in the press release.

According to MAS, Veritas Toolkit represents the first-of-its-kind tool developed for the financial sector. The launch of the new version comes more than a year after the central bank released the initial edition, which primarily focused on the Fairness assessment approach.

Veritas was developed by a 31-member consortium spearheaded by the MAS. Specifically, the key developers of the toolkit were Accenture and Bank of China. At the same time, BNY Mellon, DBS Bank (DBS), OCBC Bank (OCBC), and United Overseas Bank Limited (UOB) were mainly responsible for the pilot testing.

AI Demonstrates Bias Risks, Especially in Finance

The launch of the Veritas toolkit comes amid a frenzy in the broader AI sector. However, while this sophisticated technology has displayed drastic advancements and novel use cases in the previous months, there are also areas to improve.

For instance, AI is currently grappling with a significant issue of racial bias. Various instances reveal the presence of discrimination, such as biometric identification systems that exhibit higher rates of misidentification for individuals from minority backgrounds.

Furthermore, these repercussions of bias in AI become particularly pronounced within the banking and financial services sector. Rumman Chowdhury, Twitter’s ex-chief of machine learning ethics, transparency and accountability, said, “algorithmic discrimination is actually very tangible in lending.”

One of the possible reasons behind this is that banks are usually slow in adopting new tech tools, and the same goes for AI. Given that lenders and other financial institutions are heavily regulated, it will likely take some time for them to embrace the latest AI solutions that could address the aforementioned risks.

This article originally appeared on The Tokenist

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