Since the pandemic, insurance companies around the world have come under scrutiny. It is becoming increasingly difficult for many insurers to predict and classify claims. This has allowed people with malicious intent to commit fraud without being noticed. AI data analytics is key to reducing insurance fraud detection rates and the cost of fraudulent claims against insurance policies.
Insurance companies are now actively using artificial intelligence (AI) and algorithms to detect fraudulent activity and unusual claims. Insurance companies can continuously monitor fraudulent activity using AI and ML-based technology. This allows them to automate important tasks such as claims management.
The role of AI data analytics in the insurance industry
Insurance companies are inundated with customer data. It's not the data that's the problem, but the way it's analyzed and updated, and the time it takes to decipher patterns without automation.
AI helps insurance companies detect fraud before it happens, connecting data sets that might otherwise be disconnected. Insurance companies have better control over fraud prevention when AI and data analytics are used properly. AI data analytics provides context to patterns and behaviors that may be new to the system. Insurance companies can use customer profiles to act as investigators and quickly identify fraudulent activity. AI data analytics also helps identify legitimate claims and provide seamless customer service.
Insurers can use the information to make better business decisions and gain a more accurate and contextual overview. AI-based data analytics can free up resources to process complex data and apply it to complex investigations.
Fraud is a problem in all insurance industries, including home, auto, and health insurance. While most insurance companies are aware that insurance fraud is a problem, they lack the resources and expertise to investigate fraudulent claims.
The most common types of insurance fraud are:
In this case, the insured unfairly increases the amount of the claim. So it can happen that someone for a stolen vehicle a damage of 10.000 dollars claimed, even though it was only worth half as much.
False claims are when an insured person lies about an event that never happened, z. B. An accident, theft or other false claims.
If an insured makes multiple claims for the same incident or date of service, they may make duplicate claims.
Problems with traditional fraud detection methods
Traditional fraud detection methods rely on developing heuristics that focus on fraud indicators. With these heuristics, fraud detection can be done in two ways. In some cases, rules can be established to determine if an investigation is needed.
In other cases, a checklist of scores for fraud indicators would be created. These scores are compared to the value of the claim to determine if the case should be forwarded for further investigation. The thresholds and indicators used to make the decision are statistically tested and recalculated periodically.
These approaches are not without limitations. They rely heavily on manual intervention.
- We are limited to using a set of parameters that we know heuristically, but we also need to be aware of how other attributes can influence our decisions.
- We are not able to understand the context-specific relationships between parameters (geography and customer segment, sales process, insurance) that may not be representative of the entire scenario. Industry experts have pointed out that there is no such thing as a "typical model" and that it is therefore important to find the right model for each context.
- The model needs to be calibrated periodically to reflect behavioral changes and ensure it adapts to survey feedback. This calibration can be difficult.
How AI data analysis helps with fraud detection
Machine learning and AI tools can detect patterns that are not easily visible to the human eye. Reviewing insurance claims helps identify fraudulent or non-fraudulent claims. She is constantly monitoring customer behavior and analyzing patterns. AI first looks for patterns in insurance claims when a customer submits a claim. AI flags unusual claims and flags them for further investigation if it finds any. It automatically analyzes all claims and reduces fraud without human intervention.
AI algorithms can spot suspicious patterns in millions of insurance claims, allowing insurers to quickly analyze them. AI data analytics is a revolutionary technology that allows insurance companies to improve processes, reduce overhead costs and detect fraud.