Over the years, detecting frauds during the insurance claim management process was said to be quite a taxing process for the insurance companies and it came along with unpredictable patterns and typical challenges.
Again just to get some favors from the insurance company, many people tried to do illegal activities with proper planning just under the name of insurance cover. Some of such activities included false cause of accidents, exaggerated presentation of damages which were fake, pretended incidents and more.
It is therefore important to build detection models which work on to come up with the right balance between investment of false alert detection and loss prevention savings. The most practical way to achieve it is by using artificial intelligence so as to improve the situation in the Insurance industry.
- Senses false or misleading claims through intelligently processing different data sets.
- Brings down the impact of false alerts and the loss that comes from it.
- Its analytics helps to improve predictive accuracy
- Misrepresentation of incidents
- Soliciting excessive cover
- Over-exaggerating the incident impact
- Issues with customer retention followed by tedious investigation or delayed payouts
- Inconsiderate payouts causing decrease in profits
- Other policyholders encouraged for delinquent behaviors
- Process efficiency compromised due to high premium costs and deceit
As per the report revealed by FBI, American insurance companies received around $1 trillion as premiums annually and around $40 billion fell under the total cost of insurance fraud annually. This number itself shows an urgent need to have intellectual capability which can detect potential frauds and process clean claims rapidly.
- Coming up with specific rules which can help to know the need for further investigation
- Look for fraud indicators in order to make decisions on such activities
- Checking claim values and examining scores to understand the need for any investigation
Limitations of such techniques
- In order to identify fraud indicators a lot of manual work was needed and the insurers had to set the new thresholds regularly.
- Limited number of heuristics parameters was contained and the rest which had potential power in detecting frauds were excluded.
- Proper model for fraud investigation was missing.
- Limited parameters and context were used and this created a limited understanding of the scenario.
- Based on the feedback from the investigators the model was changed.
In order to tackle the issues rising from manual techniques, insurance companies started to use machine learning along with AI algorithm to learn from data sets. Once the frauds were identified through machine learning technique, new organized model was developed to tackle fraudulent claims.
When it comes to customer satisfaction experience, speed and efficiency play key role in insurance industry. Customer satisfaction can be improved with AI by:
- Accelerating claim settlement rate
- Control of fraudulent activities
- By right role of insurance agents