“One way to future-proof a business in the insurance sector is to lean on data monetization software.”
As the insurance industry changes alongside a number of social and technological trends, many companies are looking for ways to improve their bottom lines using data analytics tools. One way to future-proof a business in the insurance sector is to lean on data monetization software. You may be wondering, though, what exactly data monetization is and how you can put it to work.
The What of Monetizing Data
Data analysis can be performed using a number of resources that most insurance providers already have access to. The industry is demanding in terms of the amount of data that is taken in from customers themselves and from incident reports. This offers a lot of opportunities to go into a data lake and derive insights that may help a firm operate more efficiently, reduce risks and properly priced products. You may be interested in conducting:
- Fraud detection
- Loss prevention
- Predictive modeling of macro-scale risks
- Analysis of customer relationships
One major advantage that most insurers have over companies in other sectors is they tend to own huge repositories of historical data. When working with analytics, it’s impossible to over-emphasize just how much value comes from feeding more information into any data monetization software system.
If your company is curious about the potential impact of new safety features on automobiles, for example, you can make comparisons to historical precedents. This can include looking at moments like the advent of seat belts and airbags, to get a sense of what the risk profile of your average customer will look like in 5, 10 or 20 years. While this sort of modeling isn’t considered purely predictive, it provides a starting point for understanding changes that are hard to plan for.
Fraud detection is also a major opportunity for monetizing data. In the modern business environment, many people engaged in fraud are working together either directly or sharing information across the internet. This means that new kinds of fraud can appear seemingly out of nowhere. Likewise, individuals engaged in fraud may move around. By looking for patterns in how they purchase insurance and file claims, it’s possible to identify both buying and filing behaviors as they’re just appearing.
Acquiring the staff and building out the infrastructure required to perform meaningful data analysis requires a significant shift in a company’s attitude toward computing. Data science places an emphasis on testing various hypotheses, and that means you’ll need team members who have strong backgrounds in statistics in order to assess the relevance of output from your data analytics tools. This goes beyond the basic actuarial work that’s done in the insurance world and extends into other disciplines, including computer programming, economics, pattern recognition, social sciences and even psychology.
All this work is underpinned by significant amounts of computing resources. In particular, companies need a lot of data storage capacity to provide robust enough databases for analysis work. This entails installing servers, setting up redundancies and providing reliable networks for both machines and users to communicate across. In some cases, a high-speed network may call for completely re-cabling buildings to ensure the infrastructure is robust enough.
Establishing a culture that values data and analysis is also critical, and it demands more than just bringing in stats geeks, IT people and computer programmers. From the bottom to the very top of your organization, stakeholders need to be on-boarded with the culture change. This includes training sessions where decision-makers are taught about data dashboards and what their contents actually allow them to do.
Likewise, training needs to include education about the power and limitations of data. The insurance industry has many privacy issues that have to be broached. There also needs to be an understanding that excessive reliance on computer-driven answers can create its own set of problems.
One downside to this approach is that some people are going to resist change. New assignments and severance packages need to be available to ensure that folks who can’t follow the company into this new era aren’t left in positions where they can impede progress. Hiring processes should also be altered to ensure that new employees show up ready to be part of a data-centric business culture.
The culture change toward data analysis is a long one that calls for commitment. It takes time to bring in skilled professionals to set up systems and make choices about what processes need to be used. Similarly, stakeholders need to be patient in order to allow the benefits of monetization of data to begin to flow into the company. As the culture shifts and processes are refined, though, you’ll begin to see a discernible uptick in profits.