Though not full-fledged, insurance companies have been using predictive analytics for the longest time, especially in forecasting customer behavior. The process involved collecting information from various communication channels, interactions with clients, agent feedback, smart home systems, and more.
But of late, insurers have identified that better claims management and clearer underwriting services are the key improvements that predictive analytics can bring to the insurance industry. Big Data and technologies like AI, ML, and Predictive Analytics are also assets that can significantly improve ROI. With the application of AI, ML, and behavioral intelligence, it turns the sphere into a data-driven, predictive, and efficient system.
Insurance companies especially are overflowing with data but yet are struggling to make sense of it. In fact, they are using only 15% of their structured data, stored in traditional databases. The opportunity is immense to bring in ML and Predictive Analytics to mine insights not only from structured data but also unstructured data like social media, audio, and video content.
The industry is already on course for a tech-driven shift, moving from a “detect and repair” to a “predict and prevent” model because of a new wave of deep learning techniques. The next five years will see the adoption of predictive analytics in insurance in the following core areas that will prove to be a competitive advantage for insurers.
Pricing & Risk Management
Auto insurance is already beginning to leverage telematics to drive usage-based insurance and that will be one component of predictive underwriting. Predictive underwriting will take many new variables for a more accurate risk assessment.
The use of AI and predictive analytics will speed up underwriting and due diligence by combing through data from multiple sources. Data analytics in the insurance sector will not only use connected devices but also identify the current trends and risks; and also assess individual risk through behavior signals and underwrite accordingly, all in the shortest time possible.
Identifying customers at risk of cancellation
Acquiring a new customer can be five times costlier than retaining one. If customer retention can be increased by just 5%, it could push up profits by a minimum of 25%. Another clinching reason for the adoption of predictive analytics in insurance is this – the success rate of upgrading an existing customer is 60-70% while selling to a new customer has a success rate of only 5-20%.
Insurance carriers are losing millions of dollars because of churned customers. Insurance predictive modeling has already shown initial success in improving customer retention. The right approach is to have multiple predictive models, each one predicting one dimension of customer behavior that precedes a cancellation. Insurance companies can monitor customer behavior and gather intelligence during agent-client interactions. For instance, this can be managed with the help of solutions like AI-powered call analytics that alert you whenever someone is unhappy and likely to churn. Attributes like gender, age, occupation, income, policy selected, credit score, and other variables are already a part of data science in the insurance industry.
Enhanced Fraud Detection
P&C insurance carriers estimate a 10% business loss due to fraudulent claims. Criminals are constantly evolving their techniques through complex schemes such as staged accidents, disaster relief falsification or even premium diversion by agents. Research shows that insurance fraud in the United States leads to about $80 billion in losses every year. That’s an astounding number that makes insurance companies highly interested in detecting deceptive activities and preventing them in time. With predictive analytics, it has become possible.
Insurance predictive modeling for proactive fraud detection can identify patterns and anomalies in big data to highlight the characteristics of a fraudster. By analyzing social media and internal platforms for signs of fraudulent behavior or factors that may increase it, predictive analytics can forecast which customers might be untrustworthy.
Improving Underwriting Processes
Evaluating risk and determining the right pricing for each client is something insurance companies constantly face. Hence, any tool that helps perform this task in a more efficient manner and with higher accuracy is useful.
In the past, insurance companies would rely on a few variables to determine how to price a policy. Now, with the advance of predictive analytics, endless amounts of data points can be analyzed. Thus, helping underwriters come up with policies that are catered to each unique customer and maximize potential revenue.
Better Claim Management
Finally, predictive analytics can streamline the process of insurance claims management by automatically detecting claims that need to be prioritized, might require urgent attention, or have questionable validity. As a result, insurers can always focus on the most pressing tasks and deliver a better service to consumers.
