Data is one of the greatest assets of the insurance industry. Before digital transformations swept the business world, underwriters and claims adjusters were the original data-driven decision makers, gathering information to assess a customer’s risk score or evaluate potential fraud. Algorithms have accelerated the speed and complexity of analytics in insurance, but some insurers have struggled to implement the framework necessary to keep their underwriting, fraud detection, and operations competitive.
The good news is that we have a clear road map for how to implement data analytics in insurance that garners the best ROI for your organization. Here are the four steps you need to unlock even more potential from your data.
Step 1: Let Your Business Goals, Not Your Data, Define Your Strategy
As masters of data gathering, insurers have no shortage of valuable and illuminating data to analyze. Yet the abundance of complex data flowing into their organizations creates an equally vexing problem: conducting meaningful analysis rather than spur-of-the-moment reporting.
It’s all too easy for agents working on the front lines to allow the data flowing into their department to govern the direction of their reporting. Though ad hoc reporting can generate some insight, it rarely offers the deep, game-changing perspective businesses need to remain competitive.
Instead, your analytics strategy should align with your business goals if you want to yield the greatest ROI. Consider this scenario. A P&C insurer wants to increase the accuracy of their policy pricing in a way that retains customers without incurring additional expenses from undervalued risk. By using this goal to define their data strategy, it’s a matter of identifying the data necessary to complete that objective.
If, for example, they lack complex assessments of the potential risks in the immediate radius of a commercial property (a history of flood damage, tornado warnings, etc.), the insurer can seek out that data from an external source to complete the analysis, rather than restricting the scope of their analysis to what they have.
Step 2: Get a Handle on All of Your Data
The insurance industry is rife with data silos. Numerous verticals, LoBs, and M&A activity have created a disorganized collection of platforms and data storage, often with their own incompatible source systems. In some cases, each unit or function has its own specialized data warehouse or activities that are not consistent or coordinated. This not only creates a barrier to cohesive data analysis, but can result in a hidden stockpile of information as LoBs make rogue implementations off the radar of key decision-makers.
Before you can extract meaningful insights, your organization needs to establish a single source of truth, creating a unified view of your disparate data sources. One of our industry-leading insurance clients provides a perfect example of the benefits of data integration. The organization had grown over the years through numerous acquisitions, and each LoB brought their own unique policy and claims applications into the fold. This piecemeal growth created inconsistency across their comprehensive insight.
For example, the operational reports conducted by each LoB reported a different amount of paid losses on claims for the current year, calling into question their enterprise-wide decision-making process. As one of their established partners, Aptitive provided a solution. Our team conducted a current state assessment, interviewing a number of stakeholders to determine the questions each group wanted answered and the full spectrum of data sources that were essential to reporting.
We then built data pipelines (using SSIS for ETL and SQL Server) to integrate the 25 disparate sources we identified as crucial to our client’s business. We unified the meta-data, security, and governance practices across their organizations to provide a holistic view that also remained compliant with federal regulation. Now, their monthly P&L and operational reporting are simplified in a way that creates agreements across LoBs – and helps them make informed decisions.
Step 3: Create the Perfect Dashboard(s)
You’ve consolidated and standardized your data. You’ve aligned your analytics strategy with your goals. But can your business users quickly obtain meaning from your efforts? The large data sets analyzed by insurance organizations can be difficult to parse without a way to visualize trends and takeaways. For that very reason, building a customized dashboard is an essential part of the data analytics process.
Your insurance analytics dashboard is not a one-size-fits-all interface. Similarly to how business goals should drive your strategy, they should also drive your dashboards. If you want people to derive quick insights from your data, the dashboard they’re using should evaluate KPIs and trends that are relevant to their specific roles and LoBs.
Claims adjusters might need a dashboard that compares policy type by frequency of utilization and cost, regional hotspots for claims submissions, or fraud priority scores for insurance fraud analytics. C-suite executives might be more concerned with revenue comparisons across LoBs, loss ratios per policy, and customer retention by vertical. All of those needs are valid. Each insurance dashboard should be designed and customized to satisfy the most common challenges of the target users in an interactive and low effort way.
Much like the data integration process, you’ll find ideal use cases by conducting thorough stakeholder interviews. Before developers begin to build the interface, you should know the current analysis process of your end users, their pain points, and their KPIs. That way, you can encourage them to adopt the dashboards you create, running regular reports that maximize the ROI of your efforts.
Step 4: Prepare for Ongoing Change
A refined data strategy, consolidated data architecture, and intuitive dashboards are the foundation for robust data analytics in insurance. Yet the benchmark is always moving. There’s an unending stream of new data entering insurance organizations. Business goals are adjusting to better align with new regulations, global trends, and consumer needs. Insurers need their data analytics to remain as fluid and dynamic as their own organizations. That requires your business to have the answers to a number of questions.
How often should your dashboard update? Do you need real-time analytics to make up-to-the-minute assessments on premiums and policies? How can you translate the best practices from profitable use cases into different LoBs or roles? Though these questions (and many others) are not always intuitive, insurers can make the right preparations by working with a partner that understands their industry.
Here’s an example: One of our clients had a vision to implement a mobile application that enabled rideshare drivers to obtain commercial micro-policies based on the distance traveled and prevailing conditions. After we consolidated and standardized disparate data systems into a star schema data warehouse, we automated the ETL processes to simplify ongoing processes.
From there, we provided our client with guidance on how to build upon their existing real-time analytics to deepen the understanding of their data and explore cutting-edge analytical solutions. Creating this essential groundwork has enabled our team to direct them as we expand big data analytics capabilities throughout the organization, implementing a roadmap that yields greater effectiveness across their analytics.
Greg Marsh is a Data Engineer Manager at Aptitive. In his role, Greg facilitates the discovery of business insights from data. From “Big” data like IoT streams or classic relational ERP information, Greg helps companies to unlock the power of their data.