In every insurance company, accurate prediction and management of insurance claims are among the most crucial tasks that must be done with precision because they determine the pricing and cash outflows. However, if you are familiar with insurance claims, you will understand that these tasks come with many challenges, from entering and validating claims manually to damage inspection on remote sites, unorganized data storage, and so on.
However, with the rapid adoption of machine learning (ML) in the insurance sector, predictions and management of insurance claims have become easier. ML has helped reduce operational costs and improve efficiency because companies can now accurately assess risks, optimize the claim process, and detect fraudulent claims. Hence, in this article, you will learn about the role of machine learning in revolutionizing claims management and prediction in the insurance sector. You will also explore some real-world applications of machine learning in insurance claims.
Machine Learning in Insurance Claims
Machine learning has made automated claims processing possible from registration to claims settlement. This has increased operational competence and enhanced customer experience in many insurance companies.
Some of the common roles of machine learning in insurance claims prediction and management include:
Premium Prediction
One of the best features of machine learning for insurers is its insurance predictive analytics that ensure accurate premium prediction using real time data. With ML you can also integrate factors like environmental data, health metrics, and driving behavior into predictive models to determine the likelihood of a claim being made.
Predictive analytics in insurance refines this process and it is crucial for risk management and customer satisfaction, else you will be relying on traditional methods of premium calculations and you will have to bear the risks of inaccurate pricing.
Product or Policy Recommendation
Using historical data, including purchase history and similar risk profiles, AI algorithms can analyze a customer’s profile and recommend a suitable policy. This creates a smooth customer experience and increases the chances of a successful sale.
Underwriting Results
Before issuing insurance coverage to any person, the insurer has to determine the risks involved and the appropriate price to charge for the service, depending on the sector in which the company operates. The underwriter does this task using factors like medical records, credit history, customer lifestyle, financial stability, past claims, and so on to give an accurate price for the issued policy.
Thanks to machine learning apps, this process is a lot simpler, ML algorithms can combine data from all the sources mentioned above and detect patterns accurately to help the underwriting skip the bulk of the workload. It becomes easier for insurers to charge fairly for their services and prevent unexpected risks or miscalculations.
Automated Inspections
With ML, car insurers can now benefit from automated damage inspections using computer vision. A customer can simply upload an image of the damaged parts of their car, and with AI-based car damage detection solutions such as intelliarts.com, the photos will be processed automatically, and a detailed report of the replaceable and repairable parts of the car will be sent to both the insurer and the customer together with the estimated pricing for the repair.
Intelligent Document Processing
So much time is spent processing documents in insurance companies, from contracts to quote estimates, credit history, claim settlements, etc. They all require paperwork, and insurers have to do this daily.
Naturally, the process is prone to errors, and it consumes time and money, but with ML, all relevant information can be extracted seamlessly from any document, even from complex forms and transferred online for quick processing. As a result, document processing will be cost-efficient, fast, and error-free.
Efficient Data Management
Insurance companies deal with enormous amounts of data, which can be quite overwhelming. However, with the help of AI and ML, data can be shared, stored, and synchronized across various departments in an easy, secure, and very fast way. The entire process of data management, from data mapping to cataloging, can be automated using ML, which can greatly increase efficiency, save time, and reduce cost.
Fraud Detection and Prevention
AI fraud detection in insurance is crucial to flag suspicious insurance claims and uncover patterns that humans may overlook. ML can spot unusual connections between agents, customers, policies, and other data and quickly alert insurers so they can investigate.
Real-World Use Cases Of Machine Learning in the Insurance Industry
Lead Management
A predictive scoring model was created by the Intelliarts team, which can forecast the probability of a lead buying a policy; this can help insurers prioritize leads with high scores and effect a follow-up on them.
Automated Claims Processing
The Fukoku Mutual Life Insurance company handles claims data with accuracy and precision using AI and ML. The new technology helps them find and assess medical documents and calculate the payoffs from each case. This company is now able to save about $1M a year and boasts of a 30% increase in productivity.
Data Extraction
Tokio Marine has reduced processing time by 50% and human error by 80%, all thanks to an ML-based OCR it now uses to handle insurance claims.
Prize Optimization
The AXA insurance company uses deep learning to optimize pricing for their insurance services by creating an experimental neural network model with over 70 different risk factors. This model helps predict and improve pricing to prepare them for insurance cases requiring huge payouts.
Fraud Detection
Anadolu Sigorta, a leading Turkish insurance company, processes about 25,000 claims per month. Manually checking these claims for fraudulent activity can be draining, so they switched to predictive ML technology. They are now able to detect fraud in real-time, and as a result, their ROI improved by 201% in just one year.
Conclusion
Machine learning has so much to offer in predicting and managing insurance claims. It can enhance your understanding of claim costs, help you implement proactive management strategies, reduce expenses, conduct targeted investigations, accelerate claim settlements, allocate funds to claim reserves, etc. ML has many benefits, which is why many leading insurers today have adopted it and are enjoying amazing results, which you can have as well.
