The PRA QIS Exercise: What does it cover and what will it mean for firms?
While the PRA stresses QIS should not be viewed as policy proposals, firms should consider the impact the exercise could have on their balance sheets.
What it is and what it isn’t.
The term "Predictive Analytics" has become so distorted by vendors aiming to capitalize on buzzwords that it has practically lost all meaning. To clearly illustrate it, use the interactive graphic below.
Historical data summaries are not predictive analytics. While useful, these summaries, such as the one shown to the right, look backwards (retrospective) – akin to driving by looking in the rear view mirror. This summary of workers’ compensation claims data took less than 1 minute to create using basic data tools (e.g., a pivot table in Excel).
While historical data summaries can be useful to understand past experience, its weakness is just that, it’s retrospective. A data summary is descriptive analytics, not predictive analytics. Many vendors will try to convince you these are one and the same, but this is simply not true.
Predictive analytics is prospective – it looks forward. It aims to answer the question: “based on information available today, what is the predicted future outcome?” As you can imagine, this becomes much more complex. This is not as simple as summarizing data. Crystal balls are in short supply, so instead we rely on algorithms developed over the past few decades. An algorithm (i.e. a formula) is used to develop a model based on historical data. The model then returns a prediction for any new claim.
Imagine a new claim for a low back injury is reported for a 52-year-old worker with a history of prior injuries and comorbidities. Descriptive analytics would tell us that historically a low back claim has cost $20,000 on average. Predictive analytics would tell us that this claim is expected to cost $100,000 by considering all data related to the claim (such as worker’s age and medical history). Imagine how powerful this can be! Rather than being reactive, we can be proactive by preemptively identifying high-cost claims.
Despite the complexity of predictive analytics behind the scenes, an effective application of predictive analytics should be easy to understand for the end-user. The goal of predictive analytics is to make the end user’s work less complex (and save time).
The next time a vendor pitches you predictive analytics be skeptical. Determine if this information is retrospective or prospective. After all, predictive analytics is a truly powerful tool that will, and already has, revolutionized the way all businesses function.
To learn about our predictive analytics solution, Nodal, visit www.milliman.com/en/products/nodal.