Fraud detection
Insurance fraud causes a 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 not an easy task.
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 first column of the data file should contain the information about whether an insurance claim is a fraud or not (e.g. 0: No fraud, 1: Fraud) from historical data.
- The following columns of the file must contain the values of historically recorded characteristics such as: Age, Gender, Marital Status, Employment Status, Income, Area of Residence, Premium, Number of Policies, Number of Complaints, Policy Type, Months Since Last Claim, Education, Customer Lifetime Value, Months since the start of the policy, Number of insurance claims, Amount of insurance claims, Application data, Policy data, Data from the accident form, etc.
Table 1. Sample table of user input data
|
Fraud |
Gender |
Age |
Education |
Number of Complaints |
Claim Amount |
Number of Claims |
|
Yes |
F |
19 |
MSc |
0 |
1000 |
1 |
|
Yes |
M |
32 |
BSc |
0 |
0 |
0 |
|
No |
M |
26 |
BSc |
2 |
2000 |
1 |
|
No |
F |
29 |
High School |
1 |
0 |
0 |
|
|
F |
32 |
MSc |
2 |
3000 |
3 |
System Prerequisites:
- Toolbox accepts xlsx or csv files.
- The first column should contain the data from the target variable (e.g. “Fraud”), the creation of which results from historical customer data.
- The target variable should not contain missing values.
- In case the user wants to make a forecast, he must enter the data of the customers for whom he needs the forecast, in the same file (excel or csv) as the historical data, provided that the first cell which is the target variable will not contain values (see 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 and the statistical methods used is extracted from the system.
- An excel is exported with the results of the segmentation.
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.
