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Risk Management

Machine Learning for Risk Management

Artificial intelligence systems must be matched with human oversight

Published: Thursday, August 11, 2016 - 10:54

It all started as a normal day for traders David and John (not their real names). Out of the blue, their company’s audit and compliance team called them, seeking clarifications about some of their recent trades. Shortly afterward, David and John realized they had just become victims of the rise of the machines.

Both traders had engaged in inappropriate behaviors. David had favored a single counterparty at the expense of his employer, but this had been cloaked by a complex trading pattern. John, on the other hand, had built a position with an unauthorized risk profile and camouflaged this through after-hours orders and inappropriate communications with other traders. For months, both individuals had been able to evade detection, but the bank had just implemented a new system of behavioral analysis based on artificial intelligence. That was how they got caught.

Tools of this nature now give banks the ability to process massive amounts of structured and unstructured data from multiple sources to reveal trends and detect deviations from expected behavior, incorporating data-driven rules that learn and adapt to changes in the environment. This particular solution includes extensive business logic to review multiple trading activities, and mines and analyzes chat-logs and news. Within days of system deployment, David and John were identified.

Naturally, the compliance team had conducted reviews of trading activities for years. However, the new system differs vastly from the traditional approach, which was:
Manual and not scalable. Significant manual effort was required to pre-process, cleanse, and analyze data. This made scalability of the process challenging, given the high number of traders and increasing volumes of trades.
Based on a low coverage of data sources. The prior framework relied on selected data sources and provided only a partial view of actual behaviors. Thus, it was not possible to holistically monitor and detect suspicious activities.
Suffering from the drawbacks of sampling. Due to the sheer size of the data, small sections were randomly selected for analysis, thus leading to higher risk of missing suspicious activities.
Not adaptive. The framework was not adaptive to changing business situations.

Aside from its blind spots, the old system was often inconclusive and often more useful for reconstructing incidents that were already detected.

Advantages of the new system

In contrast, the new system has essentially shifted the paradigm away from a risk-auditing methodology based on backward-looking sampling to more comprehensive and continuous monitoring. This provides several advantages. First, the approach is more efficient and allows the bank to do more with less manpower. Second, it is more effective; the fact that incidents can be detected earlier allows the bank to prevent them from spiraling out of control.

For example, many rogue traders follow a “doubling up” strategy of risking an increasing amount of capital. Stopping the spiral early enough can prevent cases such as the collapse of Barings Bank from happening again. Third, the system is adaptive. Humans have a great capacity to adapt to controls imposed on them. In contrast, policies adapt at a much slower rate to changes in practices and business conditions. The new system’s learning capability helps address this problem. This creates a positive effect on organizational culture by reducing the bureaucratic burden created by meaningless controls and by protecting social norms through the detection of early deviations.

The benefits of predictive analytics and machine learning are not limited to the detection of rogue trading. Take credit risk management, for example. Traditional systems focus mainly on borrowers’ financials, with limited assessment of their business dependencies and networks. Assessments are conducted based on events such as user-initiated loan applications and regular annual reviews. The process is labor-intensive and depends on the heuristics of individual judgments. Machine learning technology can leverage a range of different sources of information such as company financials, transactions, real-time market information, business networks, and news.

Another example is anti-money laundering (AML) compliance. Trade finance, one major area of AML monitoring, is traditionally supported by heavy documentation that is more or less manually reviewed for compliance. Big data analytics can similarly support the detection of trade anomalies through the monitoring of activities, networks, and trends.

There is an emerging recognition in the financial services sector that leveraging advanced technologies, such as artificial intelligence and machine learning, is the key to deriving real value from big data infrastructure.

Naturally, like any other innovation, the new approach is not a panacea. For example, although algorithms used to manage risks can be described in general terms, understanding and (perhaps more important) explaining exactly how they work is extremely challenging. Regulators, executives, auditors, or clients without a technical background may be wary of relying on these new oracles. Data scientists are currently in hot demand, but their technical skills will gradually become a commodity. However, the capacity to mesh hard and soft skills will continue to carry a premium. Perhaps paradoxically, the technicity of the new tools has made the combination more valuable. Indeed, the new technologies may have made the human element of risk management more important than ever before.

This article is republished courtesy of INSEAD Knowledge. © INSEAD 2016.


About The Authors

Gilles Hilary’s picture

Gilles Hilary

Gilles Hilary is an INSEAD professor of accounting and control and the Mubadala chaired professor in corporate governance and strategy. He is also a contributing faculty member to the INSEAD Corporate Governance Initiative. Hilary regularly teaches courses on corporate governance, risk management, financial analysis, decision making processes, and behavioral finance. He has an MBA from Cornell University, a Ph.D. from the University of Chicago, and a French professional accounting degree.

Sivakumar Viswanathan’s picture

Sivakumar Viswanathan

Sivakumar Viswanathan is Deputy Dept Head and Senior Scientist, at Data Analytics Department, Institute for Infocomm Research (IR), Agency for Science Technology and Research (A*STAR), Singapore.