• Complex machine learning based algorithms could act like a ‘black box’ resulting in biased outcomes.
• Several banks are exploring new techniques and partnerships to improve AI transparency.
• Standard Chartered recently collaborated with a US based start-up to improve its credit risk decision AI models and mitigate unfair bias.

Artificial intelligence (AI) and machine learning (ML) is gaining rapid adoption in the financial services especially in predictive analytics, transforming customer engagements and improving efficiency of operations. By 2022, banks are expected to spend about $12.3 billion on AI and cognitive technologies, according to The Asian Banker estimates.

However, with this increase in usethere is also a growing concern regarding lack of transparency of the logic behind the decision-making models that act like a ‘black box’.

Bias in AI leads creates a trust gap

Potential risk of human biases creeping in unintentionally during building of models or on account of flaws in the training data points or algorithm logic could result in inaccurate or biased outcomes. These lead to a trust gap.

Sam Kumar, global head of analytics and data management at Standard Chartered (StanChart) commented, “Because AI and ML offer raw horsepower and access to more sophisticated algorithms than before, increasingly the industry is using wider data sets to build predictive capabilities. This combination leads to increasing opaqueness around the criteria that is driving the outcomes, which adds to the challenge of explaining the outcome.”

In a regulated industry like financial services the implications of any inaccurate prediction and biases can be huge. Monetary Authority of Singapore (MAS) in Singapore is now developing framework around the responsible use of AI technology in credit assessment. While EU’s General Data Protection Regulation (GDPR) stipulates that...

Categories:

Keywords: