RISK MANAGEMENT TECHNOLOGIES
In 2015, McKinsey predicted that “by 2025, risk functions in banks around the globe will likely need to be fundamentally different than they are today”. They were right. However, with a few exceptions, banks to date have made slow progress in transforming their risk functions.
There are several structural trends that make the evolution of risk functions more pressing than ever: (i) the breadth and depth of regulation; (ii) changing customer expectations; (iii) new types of risk, especially non-financial risks like climate; (iv) technology and analytics as enablers; (v) the need for better, faster risk decisions; and (vi) strong downward pressure on cost.
Transformation is a complex, costly and disruptive activity. This is particularly true for organisations that are dragging the proverbial ball and chain of legacy infrastructure.
This article looks at how banks in Asia-Pacific can embrace four key technologies to evolve their legacy platforms, while reducing operating costs: artificial intelligence (AI), machine learning (ML), cloud computing and integrated risk architecture.
AI and ML
If you go to HSBC’s Fifth Avenue branch in New York, you may be greeted by an android called Pepper. Pepper can teach customers how to open accounts, it can crack jokes and relay account details. Pepper exemplifies AI – ‘intelligence demonstrated by machines’.
Globally, McKinsey estimates that AI could deliver $1 trillion of additional value to the banking industry each year, with risk management and financial crime accounting for a large part of those benefits. By 2024, Asia-Pacific financial services companies are expected to spend around $5 trillion on AI, with AI as a dominant technology trend in risk management over the next three years. Yet, this represents just 15 percent of worldwide spending, indicating significant potential for growth.
Jul-Sep 2022 Issue
SAS