Risk Management (In or With?) Data Analytics

I just finished the audiobook on “Fluke” by Brian Klaas. It’s quite an interesting book on how a small event and the coming together of many small events can alter history and set the world on another course. Interesting fact, one of the two Japanese cities that were thrown an atomic bomb, was not the intended city initially.

In one of the book chapters, there was a discussion on risk management and how data plays a role in it. It got me thinking more about risk management and data where I resided for four years of my career.

In any analysis, there are two parts to it, quantitatively and qualitatively. Quantitatively will be provided by data, and qualitatively will be provided by context and sensemaking.

Now if you look at any risk management function, the biggest value comes from managing major events, like pandemics, large company crashes, large unsystemic shocks to business systems like finance and supply chain, wars, etc.

So here is the thing. Most risk management department, especially in the banks, has large troves of data. So large that it needs a sizable team of data professionals to go through it. However, focusing on the data provided only helps the organization to manage the day-to-day risk, that while it is in large numbers the risk it presents in terms of business value is minuscule as compared to the large shocks that I mentioned above. This is what I would call, risk management in data analytics i.e. looking to manage risk seen in the data and data analysis. So the value provided, I feel is not that great though.

However, if we are to start with a qualitative approach and move on to the quantitative approach, this is where the value of risk management can be amplified. The risk management department should start by playing out the possible scenarios that can happen in the macro economy, followed by using data to monitor for these tell-tale signs of these possible scenarios, followed by using possible data simulation to determine the potential shock impact. These I feel will realize the value of risk management tremendously. This is what I call risk management with data analytics.

Conclusion

Is your organization using data to execute risk management? This could potentially blindside your organization if a large shock hits the industry. Data analytics should be seen as supporting risk management rather. I admit the difference can be quite subtle here, so to see if your organization’s risk management function is done in or with data analytics, I feel the difference to look out for is whether your organization is doing scenario planning and also actively seeking information and data to make a more informed scenario planning exercise.

Quick thoughts here. Do you have something to share? Would love to hear your perspectives, especially if the risk management function is carried out in your organization and if so, how is it carried out. :)

I very much enjoy discussions that present many perspectives. If you do, consider reaching out to me on LinkedIn.

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