Many machine-learning systems provide insight to a human, who can then act on those insights. Examples of machine learning processes with the potential to become fodder for early adopters include:

Scoring risks based on characteristics of the policy and the insured; the application can learn from actual outcomes and adjust as necessary.
Recommending staffing needs when a catastrophe strikes.
Determining reserve opening amounts.

These types of applications can utilize data that originates within transaction systems as well as external data that can be purchased and appended to transaction data. Data solutions within PAS vendor offerings need to be powerful and capable enough to ensure that business people will trust the output of analytical applications that use their data. Once they trust the data, PAS vendors are back to options very similar to what I described years ago for integrating business intelligence applications:

Build their own advanced analytics/machine learning solutions.
Acquire an advanced analytics/machine learning vendor.
Partner with a vendor to provide predictive analytics and machine learning models.

While the variety of recent technological advancements has indeed been remarkable, one only has to look at the constants facing insurers who want to act fast to understand what will make or break the carriers of the future. To prepare for future technologies and take on transformative change with speed, P&C insurers must ensure that their core systems are built on platforms designed for openness and architected for change, and that their data is in a controlled, well-managed state.

Special Reports /

Business intelligence and machine learning highlight the need for change

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