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3 misconceptions hurting operational risk modeling (and what you can do about it)

Posted by Mark Lewis | Jul 10, 2017 6:30:00 AM


Operational risk is a fact of life for financial services firms, but it doesn't have to be the major source of anxiety it often becomes. Big data and advanced analytics can help organizations improve their risk management practices with precise modeling that identifies potential risks before disaster strikes.

There are many misconceptions out there regarding operational risk modeling.
There are many misconceptions out there regarding operational risk modeling.

Although data-driven risk management strategies have been a major boon to the financial services industry, misconceptions about this approach still abound. If you're not executing your operational risk modeling efforts properly, you may not see that calamitous event coming until it's too late. What are some of the most common misconceptions about operational risk modeling, and what best practices should you be adhering to instead? Let's take a look at some commonly accepted risk management practices you should be wary of:

1. Forecasting major events from recurring losses

In an effort to avoid the catastrophic events that bring banking institutions and other financial services to their knees, industry members developed the loss distribution approach (LDA). As an overarching concept, LDA is fairly complicated, but one of the ways it's applied in real-world scenarios is to forecast large, single-event losses from smaller recurring losses.

"LDA should only be deployed in the right scenarios."

While in some cases that approach may make sense, there isn't always a strong enough connection of causality to rely upon LDA to accurately forecast major events. Patrick Naim, CEO of management consulting firm Elsewhere, cites the example of trying to use individual instances of credit card fraud to extrapolate a widespread payment processing system breach. The underlying causes are completely different, and it would be next to impossible to predict the latter based on the former.

That isn't to say that there's no place for LDA in risk management, but it should be deployed in the right scenarios. Working with a data analytics consultant can help you determine when LDA is appropriate and when you should pursue a different approach to operational risk modeling.

2. Giving into (and giving up on) the unknown

Another pitfall organizations fall into is becoming overwhelmed by the idea that they can't foresee all possible threats. That's understandable given the sheer number of potential risks hanging over a firm's head. It's no reason to become paralyzed into inactivity or feel powerless to do anything about it, though.

Naim argued that a better way to conceptualize operational risk is to think of it as exposure-based and consider affected resources beyond mere dollar figures. That change in mindset will help you more accurately determine which scenarios present a risk to various operational resources and address them accordingly.

The sheer volume of unknown risks can be paralyzing for project leaders.The sheer volume of unknown risks can be paralyzing for project leaders.

3. Settling for so-so stress testing

Thanks to renewed institutional pressure to improve operational risk efforts in the financial services industry, stress testing has never been more important. Unfortunately, as McKinsey & Company noted, many organizations continue to struggle with their operational risk stress testing. Although there are a wide variety of obstacles banking institutions encounter with their stress testing, data quality may be the most common.

Bottlenecks in the data collection process, such as siloed information and lack of coordination between project stakeholders, are often primary culprits for stress test failure. Team members may also be hesitant to admit risk exposure or even mistakenly believe that reporting anything at all is entirely optional.

A data analytics consultant can help financial services firms overcome these challenges and execute operational risk modeling and stress tests that accurately forecast incidents both large and small. Specialists with expertise in the financial services industry and a track record of success in this space can address the unique challenges these organizations face and lessen their risk exposure. Whether you need help running stress tests or improving your risk forecasting capabilities, contact one of our financial services experts today to hear more about our risk management capabilities.

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Topics: Financial Services, Analytics, Risk Management, Operational Risk

Written by Mark Lewis

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