The claims management space has been evolving drastically with the introduction and integration of AI/ML and predictive analytics. By incorporating data-driven approaches to identifying patterns, assessing risk, and making predictions related to claims processing, insurers can gain deeper insights and predictions that result in more accurate claims assessments, quicker decision-making, and better resource allocation.
In this article, we’ll analyze how predictive analytics platforms like Ventiv Predict enable firms to tap into the power of ML predictions for claims management.
Key Components of Predictive Analytics
Predictive analytics in insurance claims management refers to the use of advanced ML and data analysis techniques to anticipate outcomes related to covered policyholder losses or policy events. Crucially, the successful implementation of predictive analytics in claims management involves several key components:
Data Collection
Insurance carriers gather and centralize data from various sources, including claims reports, customer data, external databases, and even unstructured data like photos or documents. Automated data intake and mobile technologies can help to reduce errors and shortcomings in manual data collection processes.
Data Preprocessing
The collected data is cleaned, normalized, and transformed to ensure it is of high quality and ready for analysis.
Model Development
Predictive models are constructed using various machine learning algorithms, statistical methods, and artificial intelligence. These models are trained on historical data to predict outcomes, such as claim severity or fraudulent activity.
Model Validation
The accuracy and reliability of predictive models are validated by assessing their performance against historical data and using metrics like recall (e.g., how many claims the ML model correctly predicts over the total amount of observations), precision (the degree of accuracy of these predictions), and F1-score—a measure that combines recall and precision.
Deployment
Once validated, predictive models are integrated or embedded into claims management systems to support claims adjusters in making data-informed decisions. It’s worth noting that—once deployed—ML models must be continuously trained to maintain their predictive abilities.
Continuous Monitoring
Predictive models require continuous monitoring and maintenance to adapt to changing data patterns and remain accurate over time. In turn, predictive software can provide inference services that deliver a continuous level of accuracy and precision.
ML Predictions for Claims Management
Generally speaking, ML algorithms enable enterprises to query the future based on historical data; to this end, predictive models for claims management should enable the following functions:
Case Reserving and Resource Allocation
Predictive models for claims management should predict claim costs on an ongoing basis, with prediction updates occurring in real-time as new data is added to claim files. These dynamic insights enable claims professionals to easily determine the probable cost of a claim and set reserves accurately. Similarly, predictive models can help insurers allocate resources more efficiently. By predicting claim volumes and the complexity of incoming claims, insurers can ensure that the right number of adjusters are available to handle claims at any given time, preventing bottlenecks.
Claim Severity Scoring and Prediction
With premiums surging and claims volumes rising in an increasingly competitive insurance landscape, firms require accurate mechanisms for prioritizing specific cases over others. To this end, predictive models for claims management should anticipate which claims have the potential to exceed a certain cost threshold. Predictive models can assess claim severity by analyzing various attributes of a claim, such as injury types, property damage, and medical costs. This allows insurers to allocate resources effectively, prioritize high-value claims, and provide a more streamlined claims experience for policyholders. By identifying costly and complex claims early, claims professionals can take proactive steps to anticipate and mitigate the severity of these cases.
Identifying/Measuring Litigation Propensity
Simply put, litigation is inefficient and expensive; in claims management, formal litigation is a major cost driver that can escalate quickly. With predictive models and analytics, firms can anticipate the likelihood of litigation for each claim—this early identification allows for improved litigation prevention measures and faster claims settlements.
Identifying/Measuring Subrogation Propensity
With predictive analytics, firms can minimize the number of missed recovery cases by more accurately identifying subrogation cases. Using AI/ML, claims professionals can automatically detect both known and unknown subrogation indications in claims information.
Forecasting Claims Durations
Last but not least, predictive analytics enable firms to accurately forecast the duration of a claim. By anticipating the expected length of a claim from first notice of loss (FNOL) or first report of injury (FROI), a predictive analytics platform can help to drastically improve the claim management process. As new updates to the claim are received, the model continuously re-estimates the duration for continuous accuracy.
In short, predictive analytics is transforming the way insurers analyze and manage claims, offering benefits ranging from improved case reserving and resource allocation capabilities to more accurate claims duration forecasts. Looking ahead, predictive analytics in claims management promises to deliver even more efficient, customer-centric, and innovative processes that better serve policyholders' needs while protecting carriers against undue losses and risk exposures.
Contact Ventiv Technology today to learn how Ventiv Predict can position your firm to better mitigate risk in its claims management processes.
