Modern society is continuously making technological advancements, leading to the exchange of massive amounts of real-time data. New digital technologies have made it possible to intelligently evaluate exponential data growth, and data science is considered a key technology in this context. Every industry is leveraging data science for its benefits, and the insurance industry is no exception.
However, data science application in the insurance sphere is still in an emerging stage. While actuaries have always been dependent on statistics for risk calculations and predictive analysis, insurance companies have not embraced data science as fast as other companies. This is why you need to adopt this modern trend to keep up with the market. To do that quickly, you can take OdinSchool’s Data Science Course to learn all aspects of data science.
Here are some data science use cases that we have compiled to show you how you can use them to grow your business.
Insurance companies lose millions each year because of frauds. According to Coalition Against Insurance Fraud, US insurance companies lose more than $80 billion annually to fraud, resulting in increased premiums for stakeholders.
Big insurers are beginning to incorporate numerous data sources to speculate potentially fraudulent claims. Predictive modeling, for example, is used to cross-reference claims against claims from related parties and data from social media platforms such as Twitter and Facebook to identify fraud instances.
Data science is equally helpful in finding good clients to keep as unmasking bad ones to weed out. Companies can use it to explore behavioral cues of a good customer and offer incentives or rate reductions to guarantee their loyalty.
An excellent example of a company that uses data science to fight fraud is Anadolu Sigorta. It is one of Turkey’s biggest players in the insurance industry and uses data science and AI-enabled technology that combines expert knowledge with analytics. Initially, Anadolu Sigorta would spend weeks looking into every submitted claim for signs of fraud. But after adopting a predictive model, it was able to identify fraud in real-time, and in just one year, it realized a 20% ROI and managed to save over $5.7 million that would have been lost to fraud.
Personalized Customer Service
We all like to be given personalized services to match our lifestyles and needs. This is why insurance companies can no longer ignore the mounting expectations of customers and have gone out of their way to provide risk management services that can be tailored to fit the real-time changes in a customer’s life.
Price and service optimization is a complex notion. It utilizes a combination of artificial intelligence and advanced analytics to extract insights from huge stacks of data regarding customer preferences, lifestyle details, and interaction behavior.
For instance, a health insurance company can offer more personalized services and pricing based on big data by considering a customer’s medical history and habits as portrayed by activity trackers. It can then use this information to develop a pricing model that fits into their clients’ budget and yet still profitable to the company.
All-State is an automotive company that gives personalized car insurance services to its customers. It uses a telematics program called Drivewise that closely monitors a client’s driving and calculates each customer’s premium based on factors such as abrupt braking, speeding, and time taken to complete a trip. They also have another program called Milewise that monitors a car’s mileage and lets customers pay their insurance based on the distance covered. This enables low mileage drivers to save on insurance.
One effective way to reduce operating costs in insurance is to automate manual processes such as claims handling and underwriting. Using data science technologies such as predictive analysis and machine learning can increase efficiency and channel your team’s focus towards higher-value activities.
Underwriters, for example, won’t have to spend time scrutinizing each policy application and can concentrate on analysis and business development instead. For claims that need to be reviewed, decision management technologies can expedite the process, improving operating speed and cost. This allows you to offer lower premiums to clients and hence stay competitive.
Better Claims Management
Data science plays a significant role in making insurance claims management more efficient, benefiting both the insurer and policyholder. Claim segmentation and triage analytics assess the level of complexity involved in each claim. Straightforward ones are fast-tracked for a quick settlement, and complex ones flagged for a more complex process. As a result, the insurer can kill two birds with one stone: The policyholders get more tailored, efficient customer service, and the insurer can save a lot of time by enhancing the claim management process.
An example of a company using data science to manage its claims is Fukoku Mutual Life. The Japanese life insurer incorporated a big-data-backed app into its medical claims processing. The app can automatically assess clients’ medical files and mine valuable information to calculate pay-outs based on the collected insights. As a result, the payout accuracy rates shifted positively, and staff production increased by 10%.
Risk assessment is a time-consuming process. You have to verify your customers’ information and assess the risks at the same time. However, data science has stepped in to make the whole process easy by paving the way for cognitive analytics and AI-enabled systems that can analyze clients’ policy documents and come up with an optimal amount for a given policy. The matrix model is widely used in this category. Its algorithms detect and combine data about individual risks, which vary by effect and nature.
Risk assessment revolves around behavioral patterns that are not likely accessible to actuaries. In the past, insurers would rate a client’s risk category based on a few demographic factors, but now thousands of factors go into it.
The adoption of data science in the insurance industry has opened doors for new sources of information to be used in analyzing complex customer behavioral patterns, making it easier to optimize marketing strategies, prevent fraudulent activities, reduce risks, and cut down on costs. We can generally say that data science has revolutionized the insurance industry.
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