Insurers have been leveraging the art of predictive analysis for centuries. However, these methodologies were more manual, intuition-based, and prone to human errors. With increasing digital transformation through the insurance sector, technology is revamping predictive analytics to make it more effective and accurate. In this post, we will briefly go over the concept of predictive analytics in insurance and witness a few use cases in action.
What is Predictive Analytics in Insurance?
Predictive analytics is a branch of data analytics that deals with interpreting and analyzing data to generate forecasts regarding the risks and probabilities of the events that take place in the future. From its very definition, it becomes abundantly clear that predictive analytics in insurance underwriting can find immense usage in several applications.
Predictive modeling in insurance utilizes techniques like data mining, statistics, artificial intelligence, machine learning, deep learning, and more, to analyze and comprehend data. These findings are then available in the form of highly detailed reports that highlight the level of risks and other factors that may govern policy formulation and underwriting.
4 Use Cases of Predictive Modeling in Insurance
The following are some of the use cases of predictive analytics in insurance:
1. Policy Optimization
Traditionally, policy pricing followed a tiered approach where insurers would adjust the customer against specifications that they deemed them fit. However, as personalization takes the center stage, one can no longer follow the one-size-fits-all model.
With predictive analytics in underwriting, agents can now customize policy plans by tapping into granular customer data. They can gain insights through analyzing historical data to understand customer preferences, price sensitivity, and behavioral signals. Similarly, the policy can accommodate external dynamic factors, such as market conditions, associated risks, risk concentrations, etc. Resultantly, the insurers can then adjust the premiums on a case-to-case basis.
2. Risk and Fraud Detection
Whether you wish to address customer fraud or broker fraud, predictive analytics in insurance can seamlessly prevent both.
For instance, predictive analytics software can collect customer data to extract behavioral information that one can correlate with fraudulent or high-risk activities. It can flag down suspicious customer patterns and alert the insurance agencies in real-time. In fact, businesses can maintain a record of such instances to assign risk scores against problematic clients.
On the other hand, insurance agencies can also use it to tackle internal fraud and application manipulation. It serves as a forensic tool that points towards broker fraud to illicitly maximize commission. Businesses can make use of predictive modeling in insurance to implement transparency and accountability amongst all its agents.
3. Insurance Claims Management
Insurance claims management is a highly data-intensive limb of insurance processing and involves several variables and input points. By using predictive analytics in insurance claims management, insurers can use it to automate, detect fraud, extend self-servicing options, and offer faster payouts. It streamlines and standardizes the end-to-end process with due consideration for disruptive exigencies. Furthermore, such a predictive analytics platform can easily integrate with existing or legacy software, which furthers your attempt at digitalization.
4. Dynamic Customer Engagement
According to a KPMG report, customer experience has emerged as the most vital KPI to measure insurance business performance.
We have already gone into the specifics of how predictive analytics in insurance underwriting will set the stage for customizing services and optimizing policies. That, by itself, lays the foundation for a heightened customer experience and subsequent customer loyalty.
However, there are other ways through which companies can use predictive analytics in insurance to drive customer engagement. For example, predictive analysis can help comprehend user intent when they approach customer service. Similarly, predictive analytics-driven automation can streamline the claims settlement process and make it painless. As a matter of fact, one can even leverage predictive analytics to understand application abandonment issues and take corrective measures to fix them!
All in all, such proactive measures will improve customer engagement levels.
With the maturation of data-driven analytical technology, predictive modeling in insurance is bound to grow at an accelerated rate. At the same time, embracing predictive analytics in insurance could be the key to thriving in this highly competitive market. Companies that exploit this positive trend will naturally manage to be successful in increasing their market share while boosting customer loyalty.