Insurance companies are in the business of risk. Their profits depend on accurately assessing the chance of future losses across their pool of policyholders so they can set appropriate premiums. However, with advances in data collection and analysis, insurers now have more sophisticated ways to drill down and estimate risk at the individual level as well.
So how exactly do insurance companies predict if an individual’s risk profile is likely to get worse over time? Let’s examine the data sources, analytical models, and other factors that come into play.
Data Used in Risk Prediction
Insurers rely on a vast array of data points about individuals to fuel their risk assessment models. Some of the key data sources include:
Historical Data
An individual’s history, in terms of past insurance claims, forms the foundation for predicting future risk trajectories. Details on previous accidents, large medical expenses, disasters befalling properties — these provide actuarial evidence of potential losses.
Insurers also gather historical data through application forms, including demographics like age, address, vehicle details, medical history, and additional background information. Financial records and credit reports also offer snapshots of an individual’s past habits and conduct. All this constitutes the baseline data for risk prediction analysis.
Real-Time Data
Increasingly, insurers are using real-time streams of data from monitoring devices to get dynamic updates on behaviors that could indicate emerging risks.
For auto insurance, telematics devices installed in cars provide ongoing transmission of driving data, like vehicle location, mileage, speed, acceleration, and hard-braking alerts. Such data can reveal risky driving behavior as it happens.
In health insurance, wearable step trackers and heart rate monitors offer insurers up-to-date monitoring of exercise patterns, sleep abnormalities, chronic conditions, and other lifestyle risk factors that could impact policy costs.
Home insurance providers are beginning to deploy Internet-connected sensors that can track temperature, humidity, vibrations, and other characteristics for early detection of situations like pipe leaks that could lead to bigger problems down the line.
External Data
In search of additional predictive signals, insurers are mining nontraditional external data sources like social media platforms, satellite imagery databases, public records, and even weather forecast data.
Online behaviors — like displaying extreme opinions or frequently sharing risky personal exploits on social media — can indicate potential personality traits or lifestyle choices that bear on an individual’s risk levels.
Meanwhile, local weather patterns, changing neighborhood demographics, crime rates, and foreclosure trends in a policyholder’s area also generate risk indicators beyond the individual’s behavior or history.
Advanced analytics helps uncover relevant correlations between all these external data streams and insurance outcomes that would otherwise remain hidden when examining internal data alone.
Models for Risk Prediction
Making predictions requires analytical horsepower to process these mountains of historical, real-time, and external data points on individuals. Insurance companies rely on a range of techniques, from traditional statistical actuarial methods to cutting-edge machine learning and AI systems.
Statistical Models
Actuaries have long used mathematical models to conduct complex statistical analyses estimating future contingencies. Mapping out thousands of historical risk scenarios helps them forecast losses for certain risk classes and pinpoint factors most relevant for pricing plans.
However statistical modeling has limitations. It requires actuaries to manually identify meaningful trends in simple datasets. These models usually examine policyholder groups in aggregate rather than make individualized assessments. However, actuarial science still underpins modern insurance risk modeling.
Machine Learning Models
Machine learning (ML) has become the engine fueling individual risk prediction today. ML algorithms can autonomously sift through oceans of granular policyholder data to detect intricate patterns and relationships between variables that humans could never unearth manually.
Drawing insights from vast datasets, ML models like neural networks and support vector machines can make inferences and train themselves to accurately forecast risk trajectories at an individual level over time. Unlike rules-based statistical modeling, machine learning keeps adapting as new loss data comes in.
Artificial Intelligence Systems
The most sophisticated risk prediction tools today integrate predictive machine learning models with artificial intelligence capabilities. AI systems can ingest streams of real-time monitoring data along with historical and supplemental datasets.
Leveraging natural language processing and data visualization tools, AI assimilates complex nested variables, detects previously unknown correlations, provides explainable predictions, and refines its risk calculations continuously based on outcomes. With feedback loops integrating new data, AI achieves a level of dynamism and individual-level precision not possible previously.
With AI, insurers can now update policyholders’ risk scores not just annually upon renewal, but assess risks in near real-time. AI looks beyond demographics and buckets of similar policyholders to make highly customized risk predictions tailored to ever-evolving evidence on each customer.
Key Influencers: Drivers of Evolving Individual Risk
Armed with rich datasets and advanced analytics, what are some of the pivotal factors insurance companies evaluate today to determine whether an individual’s risk profile is worsening over time?
Driving Behavior
For auto insurers, real-time driving data from telematics and connected car sensors plays an outsized role in predicting shifting risk levels. Harsh acceleration and braking, high speeds, inconsistent journey times, and frequent late-night trips – all can betray risky driving habits and elevated accident proneness.
Progressively deteriorating driving patterns — like more instances of speeding or increasingly distracted driving — set off alarm bells for insurers that a policyholder’s risk warrants premium hikes or coverage adjustments.
Health Indicators
Likewise for health insurers, granular lifestyle data flowing in from wearables and medical devices can indicate worsening medical risks facing individual policyholders even before claims materialize.
Daily step counts declining over months, heart rate variability spiking, blood sugar control worsening, escalating blood pressure variability — these present telltale health risk indicators that could inform significant premium increases if unaddressed.
Risk-Taking Personalities
Incorporating psychological risk metrics remains controversial but some insurers integrate personality assessment into underwriting, including risk-tolerance scores measuring impulsivity and sensation-seeking tendencies.
Research shows that risk-prone personalities engage in more gambling, substance abuse, rule-breaking, and disease-causing behaviors. Even social media feeds can reflect poor impulse control or uncontrolled risk-taking through harmful patterns detectable via AI.
While judging personalities raises ethical issues, behavioral data can signal patterns pushing individuals into higher-risk categories over time.
Scaling Risk Prediction Responsibly
As insurers’ ability to monitor policyholders and model individual risks keeps growing exponentially, their approach requires thoughtful governance.
Regulations Around Usage of Data
In response to public concerns over data privacy, insurers face stricter regulations around how they gather, access, apply, secure, and retain different categories of personal data for risk modeling purposes.
Global reforms like GDPR and sector-specific rules on ethically handling sensitive health data require insurers to implement safeguards like consent forms and anonymization to prevent unauthorized access or breaches. Getting risk prediction right requires obeying privacy rules.
Transparency Around Models
Responsible insurers also ensure transparency for policyholders around what exact data sources feed into their risk algorithms and which predictive models interpret the data. Keeping customers informed is key to maintaining trust.
Offering online portals depicting evolving risk scores, explaining shifts in premiums, and allowing customers to validate or contest data fosters openness. Explainable AI tools even decipher how individual data points impact final projections.
Market Forces Driving Innovation
Regulatory standards aside, competition also keeps propelling innovation in accurate but conscientious risk prediction. In a dynamic marketplace, insurers vie to sharpen risk assessment tools to win customers with more customized pricing without compromising privacy.
The most sophisticated data analysis will matter little if policyholders feel exploited or offended by intrusive monitoring or misuse of their data. Maintaining personal trust alongside technical capabilities guarantees leading insurers will continually enhance how they predict risk.
The Road Ahead
As cutting-edge technologies like the Internet of Things, wearables, and advanced analytics infiltrate the insurance sector, insurers’ ability to estimate risk and underwrite policies on an individualized basis will keep reaching unprecedented levels of accuracy and automation.
But with great power comes great responsibility. Insurers must transparently handle the explosion of policyholder data at their disposal responsibly while advancing innovation in risk modeling techniques. Getting this balance right will ensure this industry can equitably evolve its ability to predict and price risk in our information-rich future.
Conclusion
Predicting changes in risk levels facing individual customers is an escalating priority driving innovation in the insurance industry today. Rich historical datasets, real-time monitoring data, and even nontraditional external data streams provide fuel. Sophisticated analytical engines in the form of AI and machine learning models provide the horsepower to process these mounting piles of data into actionable insights on individual policyholder risks.
Key indicators like deteriorating driving patterns, declining health metrics, and increasingly visible risk-taking personality traits offer signals to insurers that certain policyholders warrant premium hikes due to heightening loss probabilities ahead. However, enacting responsible data governance, ensuring transparency, and focusing on consumer trust and ethical standards.
