Mitigating Risk, Advancing Innovation

Prevention Through Prediction: How Technology, Data Analysis and People Can Build Intelligent Resilience

By Andrew Gray, Managing Director, Group Chief Risk Officer, DTCC | Oct 28, 2019

Prevention Through Prediction

While resiliency has always been a cornerstone of risk management in financial services, the focus has shifted considerably over the past two decades, from an emphasis on business continuity and physical resilience after the 9/11 terrorist attacks, to financial resilience following the 2008 financial meltdown. Today, the industry and regulators are placing a premium on operational resilience and continuity of services as the nature of risk has evolved and the landscape has become more complex and challenging.

As a result, risk managers are approaching resiliency more holistically in order to gain a deeper understanding of their organizations and the extended enterprise, reflecting the reality that disruptions will occur, so firms need to be able to absorb these inevitable shocks and recover quickly from them.

Technology has always played a role in risk management, but new innovations like artificial intelligence (AI) and machine learning are opening the door for risk managers to create more sophisticated and forward-looking programs. The true power of technology, however, can be fully harnessed by combining it with human intelligence and insights drawn from strategic data analysis. Bringing these pieces together will move the industry closer to achieving prevention through prediction – or what we call “intelligent resilience.”

Analytical Power

For instance, AI and machine learning are beginning to enable risk departments to turn the massive stores of data held by the enterprise into actionable information. In the future, risk managers will be able to perform data mining across the extended enterprise to detect trends and patterns or use technology to support faster adaptation of risk models through automated learning. Furthermore, data analytics, AI and machine learning will empower risk management analysts to draw greater value from unstructured data, which often holds tremendous insights but tends to be under-utilized because of its sheer volume and the lack of tools to sort, process and understand it. As technology progresses, however, firms will be able to more fully leverage this data and couple it with structured data to yield even deeper and more meaningful information on trends and patterns on a much wider range of risks.

Human Element

While technology can help transform how financial firms manage risk, we can't become so enamored with machines and algorithms that we allow them to replace human intelligence, insight and intervention. Technology is an enabler of the risk management strategy. The human element of risk management is essential to setting risk management policy and building a strong risk management culture that empowers all employees to act as risk managers and establish processes that support a risk mindset.

Unlike machines, people can apply judgment to different scenarios, ask the right questions, analyze and make sense of data and derive insights to guide the actions of the organization. These are all critical components of building intelligent resilience.

Going forward, firms should adopt a holistic approach to risk management that balances machine capabilities with human intelligence. In addition, organizations must have the proper controls in place to continually assess potential risks associated with the adoption of emerging fintech to understand the wider risk landscape, monitor possible imbalances and prevent risks from spreading to other parts of the ecosystem. By finding the right balance of technology, data analysis and people, we can establish a robust risk management strategy built on the foundation of intelligent resilience.

This article was originally published in GARP Risk Intelligence on Friday, October 04, 2019.

 

 

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