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Better Models for Granular Risk Management

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Peter Watson's picture
CTO, ACW Analytics

Peter is a data scientist who is passionate about using machine learning and other techniques to describe and model the impacts of weather on the infrastructure. He believes data-driven...

  • Member since 2020
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  • Feb 4, 2021
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Predictive models have become ubiquitous tools for businesses to manage financial and operational risk. All modeling approaches, however, are not created equal.

The most common type of risk models today are rooted in statistical aggregation. These models analyze past events in aggregate to estimate the number and type of events that will happen in the future. This is how automotive insurance companies, for example, ensure that what they charge their customers is enough to cover all of their claims. Some customers might be more or less expensive to insure than others, but as long as the policies are priced to overcome the aggregated risks, the company remains profitable.

This generally effective but simple approach has some shortcomings. Firstly, because it relies on aggregation, this technique does not describe individual events very effectively. This effect is pronounced for extremely disruptive events, often leading them not being taken seriously as a threat. Their risks are discounted by decision makers because they appear to be so rare; unfortunately their financial impact can be catastrophic in the situations when they do arise. Secondly, aggregation leads to inefficiency because risks end up being spread out evenly. This approach of equally penalizing everyone across a risk bucket leaves a significant amount of money on the table. A more granular slicing of risky assets can lead to more efficient capital allocation and elimination of leakage.  Finally, statistical aggregation using past data does not work well if the characteristics of the events change over time. This is especially important to consider in case of some events like natural disasters because their rate and magnitude has been rapidly accelerating due to climate change. The risks presented by these events will not be correctly quantified if we rely only on historical rates and trends.

We propose to move to an alternative approach called Dynamic Modeling that can overcome the above limitations and lead to more efficient capital allocation. This technique aims to simulate how a system actually works, and predicts outcomes based on the characteristics of individual events or customers. It requires more information than aggregation-based approaches, but also provides more granular insights with greater accuracy.

Dynamic Modeling is getting traction in several industries. Revisiting the example of automotive insurance, there are programs today that involve customers installing a sensor module in their car (e.g.,Progressive Snapshot), which provides the insurance company with information about their driving habits. This enables pricing customized to how each customer drives, hence managing the company’s risk while providing extra value to their customers at the same time.

In short, Dynamic Models help risk managers appreciate each individual case for what it is, allowing for specific decision making to prevent inefficient outcomes. Their individualized treatment of assets and events results in surgically precise insights that maximize capital efficiency. They also enable realistic planning for the most extreme cases and help improve organizational resilience.

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Peter Watson's picture
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