The insurance sector has traditionally considered itself a data-driven enterprise where risk assessment and premium setting depend on statistical models. Although there is now a lot of data available on volume, variety, and velocity, it offers great potential to improve risk assessment and pricing. With big data analytics, insurers can build more accurate risk profiles, offer more personalized pricing, detect fraud sooner and run more efficiently.

 

 

Key Ways Big Data Analytics Can Improve Risk Assessment

Big data analytics for IT solutions for insurance brings many opportunities to enhance pricing accuracy, detect fraud earlier and operate more efficiently.

 

More Accurate Risk Profiling

Insurers are now using big data analysis to build more accurate risk profiles of individual customers. This is also known as precision underwriting.

Predictive analytics applied to a wider range of internal and external data allows insurers to better estimate future claim costs for any given risk. This enables policies to be priced more appropriately for each customer's true risk level.

A good example would be analyzing IoT-connected car data to see if someone drives too fast, brakes too hard or drives too late at night. These driving habits can then be used in premium pricing models.

 

Personalized Premiums and Offerings

Not all customers in a pricing group are equal in risk. With big data analytics, it is possible to do much finer segmentation of risks across a customer base. In other words, policies and premiums can be tailored to fit more closely an individual customer's circumstances.

It can also be tailored and cross sold to customer micro segments. For instance, if data analytics tells us that a customer is low risk, we can offer them more products.

The end result is greater pricing accuracy and competitiveness. Customers also benefit from fairer premiums and policy options targeted to their needs.

 

Detecting Emerging Risks

Big data analytics is helping insurers detect new risk trends earlier. Searching thousands of online data sources allows new issues to be identified before they escalate into major problems. For example, reports of a new type of car fault appearing on forums that could lead to accidents. Early detection means risk models can be updated faster.

Insurers can also get real time insight into emerging risks from social media data. Social channels come to life during catastrophic events like bushfires or floods with reports coming in far quicker than official channels. The faster notification lets claims teams plan ahead.

 

Fraud Detection

Insurance fraud costs the industry billions each year. In 2023, this number was $308 billion. Big data makes it much harder for fraudsters to hide.

Sophisticated identity verification technology helps insurers spot fraudulent claims earlier. Analysis can also detect organized fraud rings that previously went under the radar.

Network analysis of relationships between claimants allows suspicious patterns to emerge. Unusual correlations between claimants, locations, timing, injuries, vehicle repairs and more can indicate fraudulent activity.

Real-time anomaly detection models also find claims that fall outside normal parameters. These claims can then be flagged for immediate investigation.

 

Operational Efficiency

Processing and analyzing big data sets requires considerable computing power. As such, most insurers now have cloud-based analytics platforms. Cloud infrastructure allows vast amounts of data to be stored, accessed and analyzed efficiently and cost-effectively.

Many insurers are also adopting Artificial Intelligence (AI) technologies. Human actuaries do not see these patterns, but machine learning algorithms can. It's Natural Language Processing (NLP) that quickly scans thousands of documents to pull out the key information. Initial customer inquiries and more productivity are handled by chatbots.

An approach based on technology makes it more efficient, lowers the cost and provides better information.

 

Challenges in Leveraging Big Data Analytics

While opportunities abound, insurers also face major challenges in leveraging big data:

 

Data Quality

Data veracity and quality are crucial for accurate analytics. Yet big datasets often contain incomplete, biased or misleading data.

Insurers must carefully vet data sources to avoid decisions based on bad data. Rigorous governance procedures for collecting, validating and processing data are essential.

 

Data Security

Insurers store highly sensitive customer data, which makes them a prime target for cybercriminals. Storing data in the cloud can increase this risk.

Strict data security measures are critical when working with big data. Data access must be limited to essential staff and encrypted to prevent leaks. Ongoing security testing and employee education also help address insider threats.

 

Legacy Systems

Many insurers use outdated IT systems that cannot handle large datasets. Migrating analytics to the cloud is the preferred approach, but gaps between old and new systems must be carefully managed during the transition.

APIs now allow legacy systems to exchange data with cloud platforms. But, workflows often need redesigning to leverage analytic insights.

 

Talent Shortages

There is a huge demand for data scientists and AI experts in many industries. Insurance companies find it equally hard to compete for this talent due to lower salaries than tech firms.

Insurers are exploring partnerships with InsurTech startups, universities and specialist vendors to access analytics expertise. Competition for talent will remain fierce as AI transforms business.

 

Algorithm Bias

AI algorithms can replicate and even amplify existing biases in data. For example, biases around risk linked to race, gender or profile.

To address this income, algorithms must be audited for bias before implementation. Ongoing analysis of model decisions is also critical to address unfair outcomes.

 

Regulation

Insurers face complex regulations covering data privacy, pricing fairness and more. New big data approaches can conflict with traditional compliance thinking.

Open collaboration with regulators is required to update legislation for the big data era. Insurers also need robust frameworks to ensure compliance by design in analytics activities.

 

Real-World Examples of Big Data Analytics in Insurance

Many insurers are already seeing positive results from big data analytics:

 

Allstate

US insurer Allstate has an analytics platform that holds trillion miles of driving data. Advanced analytics help uncover new risk indicators to improve pricing accuracy.

Allstate has also reduced home insurance fraud by 50% using big data. Link analysis detects connections between claimants that indicate organized fraud.

 

Aviva

UK insurer Aviva uses IoT smart home data and AI to assess home insurance risks. Data on a home's temperature, security, and utilities usage helps with price policies. Aviva also provides tips to lower premiums, such as upgrading locks or insulation.

Algorithms dynamically reassess a property's risk rating daily based on the latest data, keeping risk profiles and pricing current.

 

Progressive

US auto insurer Progressive has long been a pioneer in usage-based insurance. Its Snapshot program collects driving data with a plug-in device to personalize premiums based on actual driving behavior. Now includes vehicle data such as location, acceleration, and speed.

Improved telematics collect more sensor braking patterns. This allows extremely accurate underwriting and personalized pricing.

 

Lemonade

InsurTech startup Lemonade uses big data analytics to deliver personalized policies and handle bot-based claims. Applicants complete a chatbot risk assessment for accurate quotes. Policies are then digitally delivered in 90 seconds.

AI bots handle claims through user smartphones to process payments in 3 minutes. Document and social data analysis also detect potentially fraudulent claims for further investigation.

 

ZhongAn

Chinese online insurer ZhongAn has an AI-based risk analysis platform holding 20 billion data points on 520 million customers. Data sources include online shopping sites, social media and banking partners.

Deep learning algorithms crunch this data to deliver customized policies covering flights, shipping, healthcare, cars and phones. Automatic fraud detection also occurs during claims.

 

The Future of Insurance is Data-Driven

The insurance industry already runs on data. Big data analytics now offers a step change in improving risk assessment accuracy and business efficiency.

Insurers slow to adopt will soon feel the impact from early movers reaping better profits through precision underwriting.

Looking ahead, the Internet of Things and the ever-expanding data universe will widen the gap further. Insurers best able to extract insights from this endless flow of information will win customers and drive future success.

So, while regulatory concerns, legacy systems and talent shortages pose challenges, expect to see big data analytics becoming integral to competitiveness. Companies combining rich data, AI-based analytics and a personalized customer experience will define the future of insurance.