How Do Insurers Predict Risk?

Finding new tools to automate risk analytics and assessment are a hot topic in the insurance industry. Since day one, the need to predict risk accurately has been essential in the insurance industry, but in the wake of the great online migration and COVID-19, the issue is becoming more complex. With the rising complexities of this challenge, we need a complex solution. So, how do insurers predict the increase of individual risks for online applicants? Using AI technology is the most efficient way to predict risk. 

This article highlights which stages data is collected, how each stage impacts decision-making, and how AI expedites the entire process. 

Key Takeaways From This Article

  • The risks to consider across different types of policies
  • Which processes can be automated
  • The tools that automate risk assessment
  • Where to get started

Where do we see risk management in insurance?

Risk management’s job is to minimize the impact of these risks. This can feel like a tricky dance between speculative risk, avoidance, reduction, acceptance, and the transferring of risk. 

Risk avoidance is just like it sounds — avoiding risky activities. If an applicant checks the boxes associated with a riskier lifestyle, then they’re considered a higher risk and may need higher premiums. 

Risk reduction alludes to safety measures that can be taken to reduce, yet never fully eliminate, impeding risks. Your applicants may display different levels of acceptance to risk reduction that are worth noting. 

Risk acceptance is the reality that someone has to take responsibility for the losses that can happen. Customers seek out insurance for this very reason. 

Transferring risks to insurance companies is why we have any insurance industry at all. The question, however, is how do insurers predict the increase of individual risks? 

Human-led risk assessments are flawed for a number of reasons. Firstly, we all carry implicit and unseen biases. Humans face burnout from high caseloads that inhibit our ability to think critically. Another word for this is decision-fatigue. This is why AI and machine learning are crucial to the future of risk assessment.

Risks to Consider Across Types of Policies

No matter what type of insurance product you’re selling, the expectation of risk must strike a balance with the expected outcomes stacked against it. Assessing each kind of risk starts with the initial data collection for each insurance product. 

In this next section, we’re unpacking the risks to consider during the data collection processes of auto insurance, health insurance, and property insurance.

Auto Insurance Risks

Accident history: Does this applicant’s history indicate reckless driving? Have they filed fraudulent claims in the past? Have they been involved in an incident while under the influence? Were any of the recorded accidents staged?

Vehicle safety metrics: How does this applicant respond positively to basic safety measures, like wearing a seatbelt, driving the speed limit, respecting school zones, etc.? Are they willing to install a safety monitoring system into their vehicle to receive a lower quote?

Age/Location of the driver: Is the primary driver considered young or inexperienced? Is the primary driver being misrepresented? Do they live in an area with more drastic weather conditions?

Car usage: How often is the vehicle driven and maintained? How many owners has the vehicle had?

Health Insurance Risks

Smokers amnesia: Does this applicant have a history of tobacco use that’s misrepresented? Does their lifestyle pose a risk of becoming a smoker?

Underlying conditions: Does this applicant suffer from mental health illnesses or pre-existing conditions such as asthma, diabetes, heart disease, HIV, or obesity? Have they represented their information accurately?

Claims forgery: Is this claim realistic? How often does the customer submit claims? Does this need to be further investigated?

Property Insurance Risks

Geographic location: Is the location of the property prone to natural disasters? Ex: hurricanes, tornadoes, beachfront, cliffside, remote, flood zones, high wildfire areas, etc.

Pools: Does this property have a pool or a pond that’s not closed off by a fence?

Theft: Does this property have a higher probability of theft? Is there a security system installed?

All Insurance Products

Digital body language: How did this user physically interact with your digital forms and applications? Did they hesitate during any questions? Did they jump in and out of different tabs? Was auto-fill used?

Agent behavior: Did an agent go back in to change answers? Did they “coach” an applicant through the questions? Do they have a history of “gaming” the system?

The Problem with Manual Risk Assessment

Once upon a time, spreadsheets and piecing-together tracking software were “good enough” solutions to predict risk. If the risk department was lucky, they might’ve even received automated reports from the efforts of their manual tracking. But as customers continue to prefer online processes over what was once the brick-and-mortar way of conducting business, these outdated assessment methods simply cannot keep up. 

As a result, risk managers have had to resolve gaps in their data collection to adapt to the modern customer. They’ve done this by pulling more data from more sources and hiring larger teams of employees. But the biggest challenge remains, and that is how to eliminate the tedious elements (and errors) of manual processing. This brings us back to the main question: how do insurers predict the increase of individual risks without increasing manual labor. The answer is still AI. 

Data collection powered by AI gives insurers a “peek behind the curtain” while an applicant fills out an application. Because at the end of the day, an online applicant can give you all the information they think you need to access risk, but unless you see how they’re behaving behind the screen, insurers can only *hope* they’re being truthful. Unfortunately, hopefulness doesn’t play well with accurate risk assessment. 

What manual processes can be automated?

We’ve seen pushback against AI for fear of losing the “human touch” in processes, whether that’s executing nuanced strategies or fear of robots taking away our jobs. After all, how much can AI really automate without a human reviewer? 

These are valid concerns, but we need to consider how AI-powered models — overseen by humans — can have a positive impact on risk prediction. AI-powered models have the potential to directly impact every stage, from the online application to claims processing, and all the critical steps in between. 

Here are a few ways AI is optimized for automation in insurance:

Detecting fraud: Intelligent predictive models can help insurers understand their applicants in ways that were previously impossible, making it possible to prevent and detect fraudulent behavior in your systems. 

Predicting risk: Insurers can instantly predict if a user is a liability, a high-risk customer, low intent searcher, or displaying bot activity. 

Underwriting: Underwriters can reduce bias, rates of human error, and ultimately boost the insurer’s bottom line with straight-through processing.

Claims processing: Insurers and underwriters can analyze user behavior during digital claims to predict their intent and create better outcomes.

Optimize operations: Predictive models also can help manage resources, set pricing, predict client retention, predict turnover, and predict revenue and expenses. 

Tools that automate risk assessment

For an automated risk assessment tool to predict risk in real-time, it needs to be able to react in real-time. Without real-time analytics, risk is only assessed in hindsight, which often is no longer helpful. 

For example, if your online customer used an auto-fill setting to commit fraud during the application process, you wouldn’t be able to detect it unless it was monitored in real-time. Or, what if your user was displaying ‘fidgety’ online behavior with the intent to receive a better quote or commit misrepresentation? If you didn’t have “eyes” on their real-time actions, their application would go through processing, and you’d never know. 

Real-time AI risk assessment tools allow risk signals and scoring that can be consumed offline or in real-time to dynamically intervene when an application shows signs of risky behavior. This allows you to further qualify an applicant before approving their policy.

It’s easier than ever for risky and fraudulent applications to sneak past the current defenses of insurance companies’ risk and fraud departments. That’s why our team at ForMotiv developed the only real-time, first-party intent AI software for insurance companies. Our “digital polygraph” analyzes user behavior and accurately predicts whether it’s a risky or fraudulent application while the user is still in the application. 

But we don’t stop there —  we enable carriers to dynamically add or remove friction tailored to the individual user. As carriers move towards consumer complete and accelerated underwriting, having the ability to intuitively triage a risky application can make the difference between a profitable quarter, or one in the red.

So, how do insurers predict the increase of individual risks? With the right AI tools. Schedule a call and we’ll show you how it all works together.