Claims prediction
Insurance claims are one of the most important factors in the turnover of insurance companies, as they are the main pillar of their expenses. Therefore, the accurate forecasting of insurance claims is a matter of major importance for a company’s viability, as based on these the annual financial budgets are prepared and premiums are priced.
Examples: An insurance company wants to forecast future claims in order to prepare their annual budgets more accurately, predict and prepare for future claims, and discover patterns that can enhance risk assessment of new and existing customers.
The problem
Insurance claims are one of the most important factors in the turnover of insurance companies, as they are the main pillar of their expenses. Therefore, the accurate forecasting of the insurance claims which an insurance company is called upon to indemnity is a matter of major viability, as based on these predictions the annual financial budgets are prepared and premiums are priced.
The purpose
Insurance claim prediction for a given sector (i.e. life, health, auto, property etc.) and for a given time period (year, half-year, quarter etc.).
The solution
With the use of machine learning and artificial intelligence models based on the historical data of a given period (i.e. previous year, six months, etc.) the amount of insurance claims for the next period can be predicted with great accuracy.
The benefit
Forecasting insurance claims can help insurance companies reduce costs, prepare their annual budgets, anticipate and be prepared for future claims, calculating their reserves more accurately, discovering patterns that can enhance the risk assessment of new customers or existing ones and lead to readjustment of their pricing policy.
Indicative presentation of the data needed:
- The first column of the data file should contain the customers' insurance claims based on historical data.
- The following columns of the file must contain the values of historically recorded characteristics such as: Age, Gender, Education, BMI, Marital Status, Employment Status, Income, Area of Residence, Sales Channel, Premium, Number of Policies, Number of Complaints, Policy Type, Months since last claim, Education, Customer Lifetime Value, Months since policy start, Number of insurance claims, Amount of insurance claims, Application data, Policy data, etc.
Table 1. Sample table of user input data
|
Claim Amount |
Gender |
Age |
Education |
Smoker |
BMI |
Submission to surgery |
|
1600 |
F |
19 |
MSc |
Yes |
32 |
No |
|
2500 |
M |
32 |
BSc |
Yes |
28 |
Yes |
|
0 |
M |
26 |
BSc |
No |
22 |
No |
|
600 |
F |
29 |
High School |
Yes |
27 |
No |
|
|
M |
27 |
BSc |
Yes |
230 |
No |
System Prerequisites:
- Toolbox accepts xlsx or csv files.
- The first column should contain the data from the target variable (e.g. “Insurance Claims”)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 forecast.
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.
