Top 6 Use cases of Predictive Analytics in Insurance
Over the last decade, we have witnessed a titanic shift in the way businesses operate (thank you, Internet). As the “Digital Shift” continues to impact business models and industries of every kind, one industry playing catch-up is insurance.
The insurance industry, while not traditionally known for its tech innovation, has become keenly aware of the technological advances that threaten the incumbent businesses. In an effort to stay ahead and fight off companies looking to dis-intermediate traditional insurers, 66% of the legacy players are choosing to invest in and adopt their own AI and technological solutions at every touchpoint of the insurance lifecycle.
Solutions range from using car sensors, analyzing social media accounts, and flexible pay-as-you-go pricing models to multi-factored device access, IoT networks, and harnessing ‘big data’ using advanced machine learning and predictive analytics.
When it comes to data, with so much of it out there, insurers are looking at new ways of analyzing that data for a competitive advantage. Predictive analytics has stood out as a key area of focus for most of these companies.
The two main sources of revenue for insurance companies are underwriting profit and investment income. In these scenarios, predictive analytics has become the lifeblood of insurance businesses.
How has this played out?
10 years ago, we started to witness the benefits of digitization in the insurance industry through automation and increased efficiencies in underwriting operations. It’s a simple formula – streamlined online experiences benefit customers, increasing conversions, and subsequently raising profits. And internal operational automation adds efficiencies that save companies millions, if not billions, of dollars a year.
However, more recently, predictive analytics insurance software has begun redefining the insurance product roadmap. What used to be a traditional, rule-based framework is now transforming into a data-driven, automated, highly intelligent and predictive system.
This results in lower false positives, lower costs of physical verification and shorter decision cycles. In short, an increase in the number of customer conversions plus lower risk equals higher profits.
Let’s take a look at the top 6 use cases of predictive analytics in the insurance industry:
1. Product configuration
Armed with more granular data and predictive analytics modeling, actuaries can now build products better suited to dynamic business and market conditions, risk patterns and risk concentrations. For instance, in property insurance, periodic monitoring of variables like claim history in the neighborhood or construction costs helps to predict risk more accurately.
These algorithms are constantly optimized to showcase more relevant insurance products and pricing to customers.
2. Price optimization and underwriting
Up until now, it was difficult to customize policies at the individual level. However, with automated onboarding processes and 3rd-party data sources providing unique insights such as risk profiles and behavior of individual applicants, companies are able to create more targeted products and more accurate pricing.
For instance, insurance underwriters can now analyze the behavioral profile of the applicant and apply predictive analytics to make recommendations and offer customized insurance solutions.
With the introduction of telematics ((in-vehicle telecommunication devices), behavioral data of applicant is computed when underwriting premium rates for vehicle insurance. For example, does the driver slam on the brakes? Do they peel around corners? Do they park their car often in deserted locations?
By applying predictive analytics models, insurers can assess the likelihood of the insured in being involved in an accident, or have his/her car stolen; by matching behavioral data with external factors like road conditions or safe neighborhoods.
Ultimately, this helps tailor policies and premiums that protect the insurer as well as the insured.
3. Timely fraud detection
Insurance fraud has many faces – stolen identities to obtain a new policy, false payee information, false declarations, and so on. According to the FBI, the annual losses related to insurance fraud are as high as $40 billion, costing the average American family $400-$700 in increased premiums each year.
Predictive analytics insurance software crunches data like behavioral biometrics and correlates it against past customer records to detect fraudulent activity and suspicious behavior patterns.
4. Personalization of policies, premiums, and offers
According to a prediction of insurtech trend, the focus is on a digital-first approach that places the customer at the center of all processes. A KPMG report also stresses how customer satisfaction and retention is becoming a more important KPI than operational efficiency.
Predictive behavioral analytics is helping drive change in insurance business models, shifting focus to the customer. Innovative predictive behavioral models measure user intent, in real-time, and detect correlations with outcomes like risk and fraud.
Over time, user behavior is normalized, and sophisticated AI companies like ForMotiv are able to detect risky deviations from the norm. Companies are also using predictive behavioral analytics to create dynamic experiences for customers in an effort to reduce friction for ‘good’ customers and add friction for seemingly ‘bad’ customers.
You will come across a dense use of predictive analytics in property and casualty insurance. Aggressive customer segmentation and unique customer profiles for forecasting sales and demand pricing are popular applications. Insurers are able to offer highly personalized and relevant insurance experiences by analyzing customer data like behavior, attitude, trends, lifestyle details, and so on.
Maximizing customer satisfaction is now the name of the game and predictive analytics holds the key to achieving optimal CX and customer loyalty.
Personalized experiences allow companies to make targeted offers, policies, loyalty programs, and recommendations. Predictive behavioral analytics software is also able to detect if a customer is about to abandon an application. If an applicant is likely to abandon, insurers can spring into action by engaging the customer for positive outcomes by offering lower-priced premiums, FAQ’s, and live chat.
5. Risk scoring
An important use case of predictive analytics in insurance is determining policy premiums. For instance, in life insurance, wearable devices and activity trackers provide individual health data for ongoing assessments of the individual’s risk exposure.
Predictive analytics help determine risks and limit losses in the more advanced risk assessment tools. For example, if an applicant is changing answers on e-med questions, sources of income, or health history, companies can now be alerted of this behavior, in real-time, rather than waiting until there is a claim in the future.
This information helps underwriters monitor risk and price policies more accurately.
6. Claims prediction
The machine learning technology underpinning predictive analytics for insurance companies analyzes cognitive changes of an applicant during the on-boarding process, which can indicate the probability of future risks, for instance.
Risk forecasting is critical to the survival of insurance businesses in a cutthroat market. Claims prediction helps the insurer in various ways – charging competitive premiums to remain a step ahead of the competition, as well as minimizing financial losses from high and recurrent claims.
Insurance companies deploying software with embedded predictive analytics can transform applicant and customer data into actionable intelligence. Ultimately, this helps to increase the insurer’s market share, boost customer loyalty and retention.
Are you ready to implement predictive analytics in your insurance processes? Let’s chat.