Predictive Analytics: Why You Should Care

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Insurance companies have long based their estimates and decisions on analyzing data to help predict future events. However, with increasingly available data and faster processing power, more sophisticated algorithms designed expressly for the insurance industry can be used to augment their data analytics. By applying machine learning and modeling algorithms to historical data patterns, insurance companies now have a more powerful tool set to anticipate future outcomes with greater accuracy than ever before.
The results of predictive analytics for insurance can yield immediate improvements across your entire operation. Whether you are just starting to apply predictive analytics or you are already using it for multiple areas of your business, predictive analytics can help you:

Remain competitive in the marketplace.

More and more insurance companies are adopting predictive analytics to increase profitability and gain an advantage over competitors. Smart companies are already harnessing predictive analytics tools to select risks and price accurately. Therefore, the gap continues to widen between companies who are maximizing their data usage and those who are being left behind.

Make data-driven decisions more quickly.

By advancing your analytic capabilities through the use of sophisticated algorithms, you are using current technology to its fullest capability. This enables your team to base conclusions on accurate and reliable analytics and accelerate data-driven decision making.

Become more proactive.

Traditional data monitoring methods require a tremendous amount of time to uncover patterns and take necessary corrective steps. Even while working at maximum speed, your teams are still reacting to issues as they arise. Once in place, predictive analytics enables your team to anticipate issues and make decisions before they become full-blown problems. Monitoring of predictive models allows for proactive action as your business changes.

Create more accurate pricing and underwriting structures.

This is where most companies are already using predictive analytics: to better segment their business and develop more accurate pricing. Rely on predictive analytics to a greater degree, and ensure that your company is charging the correct price relative to risk.  By running quality data run through a reliable predictive analytic model, you are giving underwriters a tool to better select desired risks and achieve greater precision in discretionary pricing.

Detect fraud faster.

Appropriately developed algorithms can highlight anomalies in data, increasing the speed in which your claims department can reveal fraud incidents. This reduces the number of fraudulent payouts and immediately improves your bottom line.

How to get the most from your analytics model

Models can never replace the expertise of an experienced underwriter but they make the job more efficient and improve results.  However, the biggest mistake we see insurance companies make is not soliciting upfront input and feedback from the end users – the underwriters and agents who will be expected to use these models. If developed correctly, predictive analytic models can become invaluable tools that enable teams to do their jobs faster and more accurately. Involve your end users in meetings with your predictive analytics development team to ensure that the model captures and interprets the data which will be most helpful to your organization.

The importance of maintaining data quality

Analytics are only as reliable as the quality of data they capture. Because effective predictive analytics models use very detailed policy and claim information, be sure to work with a company who has expertise in the insurance field and understands the significance of certain anomalies. When you evaluate your data capture in detail, you can improve your data quality moving forward.
The power of predictive analytics for insurance is not limited to the pricing and handling of the insurance product. Once the correct tools are in place, predictive analytics can improve many other internal and ancillary aspects of your insurance company’s business. Finance departments can apply predictive analytics to collection strategies. Human resources departments use analytics to narrow down a range of potential candidates, selecting those with desirable characteristics that will best support the company. Marketing departments can use predictive analytics to gauge the effectiveness of communications, increasing marketing ROI. The applications of predictive analytics for insurance can extend as far as the questions you ask about how to advance your business.
If you would like to enhance your insurance business and develop more powerful models for pricing, reserving, underwriting and/or internal operations, contact us at (888) 201-5123 Ext 3. Our predictive modeling experts can help you develop solutions that apply analytics to boost your company’s performance.