ML For Improved Risk Assessment In Insurtech

Traditional risk assessment methods in insurance can be effort-intensive and prone to errors. They do not always consider all the criteria that determine the features and pricing of a particular insurance plan, too. Machine learning (ML) poses new opportunities for insurance professionals to revamp risk assessment as we know it.

By tapping into a larger pool of data, including sensor data generated by IoT devices, unstructured data existing in the form of non-editable documents and graphics, and even social media posts, insurance companies can take a more precise and unbiased approach to risk assessment. Here’s how it works.

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Traditional vs. Machine Learning-Assisted Risk Assessment in Insurance

Current State Of Risk Assessment In Insurance

Agents turn to various insurance technology (Insurtech) tools for risk assessment. These include insurance policy administration and underwriting systems, claims management systems, fraud detection solutions, and risk management information systems, among others.

Depending on the Insurtech solution’s specifics and type of insurance, such tools can help evaluate the following risk factors:

Auto insurance. To calculate car insurance pricing and coverage, an agent might look into an individual’s location, registered traffic accidents and violations, miles driven annually, vehicle type and its safety features, a driver’s age, and more.
Health and life insurance. For medical and health insurance, several different factors come into play. An insurance professional may consider an applicant’s age and gender, current health status and medical history, lifestyle, and occupation to determine the terms and price of an insurance policy.
Property insurance. For commercial property, the list of deciding factors may include the location and type of property, the nature of business operations conducted on the property, and the implemented safety and security measures. Individuals looking to insure residential property are required to provide information about its location and proximity to areas prone to natural disasters, including coastlines and floodplains. Other criteria affecting insurance price and coverage may span the age and condition of the property, the type of building materials used in its construction, the presence of safety features, such as fire alarms and sprinkler systems, and previous claims history.

All this information needs to be kept, analyzed, and interpreted via the Insurtech tools cited above.

And here’s where difficulties may arise:

Data abundance. The amount and unstructured nature of data at insurance agents’ disposal makes it challenging to perform risk assessment effectively. Recent studies indicate that insurance companies only use 10-15% of the information they can access when making risk assessments and underwriting decisions.
Outdated risk assessment models. Most insurance companies cannot keep their risk assessment models up-to-date, failing to consider emerging risk factors. Such factors include natural disasters triggered by climate change (think wildfires in Maui), cyberattacks, whose average cost now exceeds $4.45 million for medium-sized and large enterprises and a lack of political and economic stability around the globe.
Subjectivity. While many aspects of risk assessment are quantitative and are enhanced or partially automated using traditional Insurtech tools, the process still includes elements that rely on human judgment, which causes potential bias and error. Hence the differences in insurance pricing and liability amount for the same risk, which, depending on the company, may fluctuate by 24% and 233%, respectively, as in this example from McKinsey.

Let’s investigate how insurance professionals may mitigate these and other challenges by enriching Insurtech software solutions with machine learning capabilities.

How ML-Based Insurtech Tools Improve Risk Assessment

A subset of artificial intelligence, machine learning processes large volumes of structured and unstructured data and, as it says on the tin, learns for it.

These qualities make the technology particularly suited for risk assessment, which can be improved in several ways:

Prediction accuracy. Feeding off historical data, ML models can identify policyholders who are likely to file claims soon. Similarly, the technology can anticipate risks based on environmental conditions, make sound judgments regarding occupational risks and hazards, and reflect these concerns in an applicant’s insurance score. This information, among other things, can help agents develop tailored plans for individual clients and customer groups. And ML’s superpowers stretch far beyond historical data processing! For example, insurers can read information off telematics devices installed on transportation trucks and offer better insurance rates and features to safe drivers. Many insurance companies consider physical activity and health data gathered via smartwatches and fitness trackers, rewarding clients who pursue a healthy lifestyle.
Fraud detection. Fraudulent claims are a significant issue in insurance, causing the industry $308 billion in annual losses in the USA alone. Machine learning can spot anomalies or patterns consistent with fraudulent behavior in historical and real-time claims data. For instance, if a particular individual frequently claims losses just below their deductible, intelligent algorithms can flag this behavior for further review by human agents.
Data sourcing and integration. Traditional Insurtech tools for risk assessment might fail to incorporate unconventional data sources, such as the Internet of Things solutions, third-party customer analytics platforms, and social media. Machine learning algorithms can act as middleware, connecting a company’s Insurtech systems with each other and external sources, leading to more comprehensive risk profile assessments.
Underwriting efficiency. Intelligent algorithms can analyze vast amounts of data, identifying complex patterns that may be overlooked by human underwriters. This helps reduce the chances of implementing underpriced or overpriced policies. Furthermore, ML-assisted claims analysis and verification speeds up the underwriting process, which leads to greater profitability and a better customer experience.
Customer retention. In the insurance sector, the cost of attracting a new customer exceeds the expenses associated with keeping an existing client by a ratio of one to seven. By feeding ML models the data from customer relationship management (CRM) and insurance claims management software, agents can gain a better insight into customer behavior and feedback, addressing issues proactively.

Despite the big promise machine learning holds for insurance risk assessment, companies eyeing the transformative technology should be aware of the unique challenges it entails.

For instance, some of the data used for risk assessment and underwriting, such as sensor readings from connected fitness trackers, might be personal. Depending on your target market, your data analytics efforts might thus be undermined by the existing data privacy and security regulations.

Additional issues stem from the preferred approach to model training. Advanced machine learning solutions that process unstructured data with little supervision from human experts often fail to explain how they arrive at particular conclusions. Known as “black boxes,” such models require additional explainability layers and may be tricky to implement.

The good news is, that an insurance company can successfully navigate these challenges by hiring ML consultants and adopting an iterative approach to model training and implementation.