The insurance industry is highly data-centric, and insurers possess vast amounts of data that hold potential insights which are trapped in legacy systems and databases. This presents immense opportunities for insurers to solve business challenges by leveraging modern prescriptive analytics tools. This current era of analytics allows insurance companies to understand the state of the business, and also provide accurate insights into what is likely to take place in future.
The three dominant types of data analytics which categorizes all forms of analytical models are descriptive, predictive and prescriptive analytics. Each of these methods offers a different insight and is selected based on the business need.
Prescriptive Analytics - A Definition
Prescriptive analytics is a type of predictive method used to evaluate future decisions in order to generate recommendations based on the computational findings of algorithmic models, before these decisions are actually made.
For better decision options and improved prediction accuracy, the prescriptive model can continually improve itself by taking into account new sets of data to re-predict and re-prescribe. These analytics insights can help in preventing fraud, limiting risks, improving efficiency, meeting business goals and driving customer loyalty.
For example, a predictive model can determine weekly insurance sales numbers, but lacks the ability to suggest how to increase them. However, prescriptive analytics recommends help in terms of optimizing marketing campaigns, taking specific sales actions, customizing product offerings etc. for improved sales outcomes.
Business Use Cases of Prescriptive Analytics in Insurance
The insurance industry can use prescriptive analytics to solve business problems including those that are conventional in nature (e.g. catastrophe modeling, agent performance, claims fraud) and others that are modern (e.g. personalized marketing, omni-channel customer experience, optimizing claims cost).
The following are samples of the usage of prescriptive analytics in the insurance industry value chain.
Tools and Techniques for Prescriptive Analytics
To make the data actionable, insurers must understand the business questions that are trying to be solved to achieve the goals of the organization, and rethink business processes to better use analytics. Also, this involves using analytics tools to drive decisions and actions that improve business performance.
Accomplishing machine-based decision-making or generating automated recommendations requires specific and unique algorithmic models that allow computers to make decisions based on statistical data relationships and patterns.
There are modern decision management platforms which help address the challenges which insurers are facing in terms of accumulating, analyzing and making the data actionable. The decision management platforms are defined based on following types:
- Models based on business rules management systems:
These models help in real-time decision making by executing a large number of rules. Business rules management systems help manage and automate repeatable business decisions.
- Models based on mathematical optimization:
Mathematical programming models consider the variables (decisions) and constraints (business rules) to recommend a solution that generates the optimum result. The solution is further evaluated according to an objective variable (business goals) that determines the criteria to be considered.
Mindtree, being a strategic IT partner for leading global insurers, has an industry-focused insurance analytics framework that comprises a quick guide repository of industry-standard data models, a detailed list of key performance indicators across insurance functions as well as a pre-built visualization reporting dashboard. This framework serves as a basic tool kit for an insurer to help kick-start any basic analytical engagement initiative in order to prep up for the journey of predictive and prescriptive analytics. A glimpse of this framework is articulated below:
Data analysis has emerged as a key differentiator in the insurance industry and Big Data analytics are dominating the minds of insurance carriers, as they strive to stay ahead of the competition in today's industry. Prescriptive analytics in insurance is in a unique position because of the importance data has always held in the industry.
To conclude, insurers should adopt advanced prescriptive analytics tools to predict not just what is likely to happen based on past events, but also how they can change the course of the future to run their business efficiently, sustain profitability, and create competitive advantages.