Advanced Analytics For Insurance Industry: 6 Major Use Cases
The insurance industry is fraught with danger. Navigating tricky claims procedures, pricing and promotion, risk mitigation, cash repression, natural perils, and ensuring compliance are some of the industry’s most complex challenges. Insurance companies have traditionally relied on statistics and data to drive their decisions, owing to the massive amount of daily data generated in this industry.
Incorporating advanced analytics to achieve business goals has resulted in a paradigm shift in how insurance industries operate. Advanced analytics aids in big mining data for actionable insights that can be applied to various business use cases. Insurers are increasingly relying on advanced analytics to protect their businesses from risks and identify new growth opportunities based on customer data.
6 Major Use Cases Of Advanced Analytics In The Insurance Industry
1. Detection Of Fraudulent Claims
Every year, insurance companies suffer massive losses due to fraudulent claims. According to Gartner, the annual losses from insurance claims fraud are estimated to be $40 billion.
Many insurers also estimate that 10 – 20% of claims are fraudulent and that less than 20% of fraudulent claims are detected.
Data science advancements have enabled the detection of fraudulent activities, suspicious claims, and behavioral patterns using predictive analytics that incorporates statistical models for efficient fraud detection. These models use historical data on fraudulent activities to arrive at specific conditions that predict the possibility of fake claims.
When someone claims with a history of making false claims, the system halts the claim process and recommends an investigation into the case. Predictive modeling techniques also analyze fraud patterns and screen for false claims.
Allianz Insurance in the Czech Republic saved approximately US$4.5 million annually by reducing the number of fraudulent claims paid. Poste Assicura in Italy estimated savings of 5 to 10% of claims using advanced analytics.
2. Detecting And Mitigating Risk In Real-Time
Because the insurance industry’s underlying nature involves risk, advanced analytics is used to conduct real-time risk analysis, allowing organizations to be quick on their feet in a volatile risk environment.
In the case of vehicle insurance, for example, the ability to accurately assess the risk posed by a specific driver enables companies to formulate a competitive and profit-making premium. Cars with internet access can continuously transmit a large amount of data.
Insurance companies can now obtain minute details such as braking behavior and vehicle speed. Using advanced analytical modeling, insurers can determine how likely a driver will get into an accident by comparing information about how the driver acts with information about how other drivers act.
A waste management company in the UK, one of our customers, used advanced analytics to make drivers safer and lower insurance payouts. By combining telematics and tachograph infringement data with weather data and harnessing the data set with machine learning, the company predicted the likelihood of drivers having incidents. The solution helped the company drastically reduce the number of accidents and, as a result, the insurance payout.
3. Personalizing Marketing Strategies And Targeting Specific Customer Groups
In the insurance industry, personalization is not a new concept. Customers are eager to receive services tailored to their specific needs and lifestyle and seek personalized offers, policies, loyalty programs, and recommendations.
Insurance companies face the challenge of engaging their customers and effectively communicating with them in an era of extensive digital communication.
Advanced analytics is used to extract insights from an extensive database that contains information on customers, such as demographic data, preferences, attitudes, lifestyle details, interests, and belief systems, among other things.
This enables insurance companies to provide highly personalized and relevant experiences. A hypothesis/model on personalization and marketing strategies is formulated using data acquired from various digital platforms, which are then tailored to fit customers’ needs. Several analytical tools and mechanisms assist businesses in achieving this goal.
Personalizing offers, policies, prices, recommendations, and marketing ads contribute to the success of acquiring customers and, as a result, increases a company’s insurance rates.
4. Influencing Customer Behaviour
Insurance companies have also used advanced analytics to analyze telematics data and influence customer behavior. For example, health insurance companies can collect and analyze data generated by IoT devices and wearable technology, such as fitness trackers, to track variables that determine a person’s health and assess risk.
Insurance companies can comprehensively assess their customers’ health and encourage them to take better care of their health by monitoring their behavior and habits, thereby mitigating the risks involved. Insurance companies can offer additional services and discounts to encourage customers to use fitness monitoring devices.
A renowned life insurance company, John Hancock Financial, offers its customers premium discounts and a free Fitbit wearable monitor so that customers can work to reduce their premiums by presenting an evaluation of how they are progressing on their unhealthy and risky behaviors.
5. Lifetime Value Prediction
Customer Lifetime Value (CLV) is predicted using customer behavior data to determine the profitability of the customer for the company. Behavior-based predictive models analyze all customer data and forecast customer buying and retention.
These models provide insights into the likelihood of customers’ behavior in the maintenance or surrendering of a policy. CLV can also be used to develop market strategies because it reflects an important customer characteristic.
6. Claims Prediction
The insurance industry is highly interested in predicting the outcome of future events. Accurate claim prediction aids in risk mitigation, competitive advantage, and financial loss reduction.
Advanced analytics drives some of the most complex processes in developing financial models, which have many variables influencing the outcome. Algorithms are created to recognize relationships between many variables and to detect several key parameters required to build a customer portfolio.
Forecasting future claims assists insurance companies in developing competitive and optimal premiums and improving pricing models.