Anomaly & Fraud Detection in Car Insurance
Anomaly and fraud detection in car insurance can be a critical task for insurance companies to minimize losses and ensure fair pricing for their customers. A common approach is to use statistical modeling and machine learning algorithms to detect anomalies and fraudulent claims.
Examples: A Car insurance company wants to detect and investigate only the most suspicious cases and save time and money from investigating every claim. Using data from historical cases the most suspicious incidents can be showcased.
The problem
Insurance fraud causes huge financial loss to insurance companies every year and is undoubtedly one of the most important challenges they have to face. Insurance fraud takes various forms, such as overcharging, false declaration, concealment of information, etc. and its detection is a difficult task.
The purpose
Insurance fraud detection i.e. the set of activities undertaken to prevent money from being obtained through false pretenses.
The solution
With the use of machine learning and artificial intelligence models that will rely on historically recorded and confirmed insurance fraud attempt data, correlations and patterns between historically suspicious activities will be identified and based on these it will be easier to identify attempted fraud in the future.
The benefit
The use of machine learning tools allows insurance companies to quickly and efficiently detect cases of fraud.
This mainly entails the following:
- Reduction of damage arising from insurance claims
- Reduction in the cost of handling compensation claims
- Cost reduction from expert services
- Enhancing the company’s competitiveness in the market, as well as customer retention, because the resulting cost from the insurance fraud is passed on to all the insurance company’s customers, (due to the increase in the claims ratio) through the increase in the insurance premium.
Indicative presentation of the data needed:
- The mandatory fields are: Customer number, Policy number, Maker, Model and Vehicle Claim Amount, which are derived from historical data.
- The optional fields contain the values of historically recorded characteristics such as: Age and Gender
Table 1. Sample table of user input data
|
Customer number |
Policy number |
Age |
Sex |
Maker |
Model |
Vehicle Claim Amount |
|
32111 |
95220 |
19 |
F |
Mercedes |
ML350 |
14889 |
|
32112 |
95221 |
32 |
F |
Cintroen |
C3 |
2900 |
|
32115 |
95224 |
26 |
M |
Cintroen |
C2 |
1800 |
|
32124 |
95225 |
29 |
M |
Peugeot |
206 |
2200 |
|
32125 |
95226 |
32 |
M |
Toyota |
Corolla |
4500 |
System Prerequisites:
- Toolbox accepts xlsx or csv files.
- The names of the variables must necessarily match the names as listed in table 1
Output:
After the data has been entered by the user and after a short period of time for its automatic analysis:
- A report of the results is extracted from the system.
- An excel is exported with the results of the outlier detection.
Note: For any clarification you need regarding the content of the use case or any information related to the collection or validity of your data please contact us.
