With enhanced customer service and mounting cost pressures, financial services institutions are looking at Artificial Intelligence (AI) and Machine Learning (ML) to improve cost and operational efficiencies while mitigating business risks.

Artificial Intelligence is one of the hot tops nowadays, but not everyone understands its scale and productivity value in the business. Thus, just for reference, Artificial intelligence can increase overall productivity by 40%. Netflix uses AI to provide automated personalized recommendations on TV shows and Web series which generates a revenue of $1B annually. When a host personalizes any category to the ultimate user, it creates belongingness in the mind of the customer. Apart from this, 49% are thinking about buying something from the company more often in case it uses AI personalization.

Artificial Intelligence

The consulting firm KPMG issued a research paper on “Modern Risk Management for AI Models” accentuating adaptable key pillars of risk management that aid a bank’s framework. The report further explains how Banks should consider using AI technology before investing in any risky bespoken assignment. 

“With model-induced decision making, widespread differences in the approach have been taken by banks on AI risks around bias, interpretability, and other challenges. Hence, banks should conduct enterprise-wise training programs to educate all stakeholders including the senior management on key aspects of AI/ML such that they can gauge the risks better,” said Rajosik Banerjee, Partner, and Head, Financial Risk Management, KPMG in India.


“It is imperative that a cross-functional governance framework must be established with clear definitions of roles and accountabilities. There are key elements that need to be specifically tested during the model’s life cycle, including e.g. during design, implementation, operation, and validation. Artificial Intelligence and ML models will entail risks and challenges that both banks and financial institutions do not yet sufficiently consider in the existing Model Risk Management (MRM) approaches. Also increasing regulatory requirements, reasoning now they have to expand and operate their existing MRM approach in specific areas”, said Banerjee.

Banks are required to create internal skill sets or call experts for operations. External subjects will help banks more efficiently as they have the respective skill which will help them benchmark themselves with competitive banks around their risk management, controls, and framework enhancement. 

It remains to be seen how this area evolves and regulations shape up over the next few years, but international guidance or standards in this area will be helpful in setting the minimum benchmark for MRM practices across jurisdictions.

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