Big data analytics is a powerful tool that transforms the underwriting process ― enabling greater speed and efficiency, providing deeper insights into individual risk, driving better business decisions, increasing profitability and improving the customer experience.
Here are some do’s and don’ts on integrating big data analytics into your business.
Gather lots of data. Big data starts with… data. Storage is inexpensive, so err on the side of more data rather than less.
Understand that insight is iterative. Insights evolve from analyzing data. Those insights generate new data and new insights. Keep going.
Remember that history is important. Information often evolves on a continuum. Build historical perspective into data models from the beginning, so that you have more than just point-in-time perspective.
Be vigilant about data quality. Measuring and improving data quality is also an iterative process. Your ability to leverage analytics depends in large part on your understanding of your data quality and the investment you make in managing your data on an ongoing basis.
Commit. The entire business must make a commitment to trusting and acting upon the findings that result from Big data analytics. Creating a culture that embraces data and analytics as a key business fundamental is crucial to success.
Underestimate the work required to prep the data. While more data is better, data has to be of high quality and relevant to the task. Make sure you’ve allotted enough time to prepare your company’s data.
Ignore regulatory requirements. There are a number of regulatory guidelines governing data usage, especially for underwriting, including The Fair Credit Reporting Act (FCRA), Driver Privacy Protection Act (DPPA), Gramm-Leach-Bliley Act (GLBA), and Health Insurance Portability and Accountability Act (HIPAA). You must diligently comply with restrictions on how an individual’s data may be used, and ensure that the data and your use meet all requirements.
Violate customers’ privacy. Carriers must be aware of and sensitive to the responsibility of using the increasing amount of available individual data, and must abide by the highest standards of privacy. Used properly, this data can result in better risk management and more accurate pricing, which benefits both you and your customers.
Be reckless about attribution and accuracy. Shopper data and social media information aren’t always directly attributable to the proposed insured. Therefore, this type of data is inappropriate for underwriting purposes. Be sure you use the right data for the right purpose.
More good news
Implementing a data and analytics solution doesn’t have to be a major disruption. Whether you have an automated underwriting engine or not, these solutions plug right into existing systems, decision engines and workflows. So what’s holding you back?
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