Predictive Analytics and Insurance Regulation: 5 Tips for Success

As the influence of big data continues to rise, insurers are utilizing analytic models more often than in the past. But when launching new predictive models for use in insurance programs, it’s never a good idea to submit your model to regulators without the right support. By applying a regulatory-focused strategy, you can ensure that the review process does not slow down your model implementation.

Here are our tips for success:

Tip 1. Prepare thorough data documentation

The models you create for use in your insurance programs will be reviewed by regulators. Make sure your documentation on the data used in the model is clear and thorough, such as disclosure of internal and external data sources, data quality and accuracy checks, handling of missing data fields, as well as any adjustments you made to the data, including capping, removing outliers, etc. Regulators want to be sure best practices are followed by the modeler who conducted the analytics. To ensure that your data documentation is clear, it’s beneficial to contract an outside actuarial consulting firm to conduct an external review of your analytic model prior to submitting to state insurance departments.

Tip 2. Provide strong analytic support

Once you have documented your data, you still need to provide regulators with model validation results. Did you follow best practices when generating your predictive analytic model? A thorough regulatory review will require analytic support such as correlation and interaction tests, the statistical significance of results, confidence intervals, lift charts and many other items. Someone familiar with the regulatory review of predictive models can help ensure you are prepared with the necessary support. Once again, this is another area where it’s smart to partner with an actuarial consulting firm to confirm the accuracy of your results and conclusions.

Tip 3. Be prepared for variations by state

Remember that states may require different levels of support for regulatory approval of an analytic model. Some states have questionnaires as part of the filing process for predictive analytics models. Do all of your data fields comply with state-specific rules regarding allowable data fields? Never assume that just because your submission went smoothly in one state that you can count on an approval in another. If you’re submitting in multiple states, a third-party consultant can save you from major setbacks by performing a compliance review of your model before you submit.

Tip 4. Adopt the regulator’s perspective

Take the time to anticipate the areas that regulators will watch closely. Consider questions and concerns they might have and address them upfront in your support. This will help you stand a better chance of fast approval. If you do have questions about how to best support your model, request to discuss these concern with regulators prior to submitting your model.

Tip 5. Predictive analytic support versus intellectual property

It’s understandable that when you invest so much work and so many resources in developing your model you don’t want to share your valuable intellectual property with the world.  Whether you have developed your predictive model in-house or are using an InsurTech vendor’s model, you need to balance regulator review with protecting proprietary formulas. Partnering with a consulting firm that is familiar with confidentiality requirements can help protect your work without slowing the approval process.
When it comes to predictive models and insurance regulation, the most important thing to remember is: be prepared. Make sure your documentation clearly outlines your predictive analytic process to support the use of the model and address any state-specific regulatory concerns. It’s in your best interest to have all pertinent information at the ready so the process proceeds as smoothly as possible.

For more information about how Perr&Knight can provide predictive analytic consulting services to help you navigate the regulatory process, contact us at (888)201-5123 x3.

Predictive Analytics: Why You Should Care

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.