Risk assessment
Risk assessment is about detecting which customers are likely to announce an insurance event which consequently will lead to an indemnity payment by the insurance company. It is a significant prediction for insurance companies because claim payments control affects company’s profitability.
Examples: An insurance company wants to control loss ratio for health covers by measuring the possibility that a customer will produce loss. Risk assessment process can be used to identify these customers in order to categorize them into a special pricing group.
The problem
Insurance claims considered as one of the most important factors for the profitability of insurance companies. Therefore, the estimation of the number of clients that are intend to procced to a claim announcement, contributes to the proactiveness and preparation of the company in order to keep claims in low levels. Additional advantages for the company may be considered as the prevention of “highly potential risk” clients from demanding an indemnity and a more reliable forecasting of future claims.
The desideratum
Predicting the percentage of customers that intend to announce a claim in a given period, typically monthly, quarterly or yearly and/or for a given sector (i.e. life, health, auto, property etc.).
The solution
With the use of machine learning and artificial intelligence models that will rely on the historical data of a given period (i.e. previous year, six months, etc.) a profile of the customers that intend to announce a claim can be recognized.
The benefit
The usefulness of the predictive model lies not only in identifying customers that are going to announce an insurance claim but also in identifying the main factors related to this decision for each individual case (e.g. specific lifestyle habits). This knowledge help insurance companies to anticipate the risk of factors that most related to a claim announcement and also help them to predict more accurately their future costs.
Indicative presentation of the data needed
- The first column of the data file should contain the customers' claims statement history based on historical data.
- The following columns of the file must contain the values of historically recorded characteristics such as: Age, Gender, Education, Body Mass Index (BMI) , Marital Status, Employment Status, Income, Area of Residence, Education, charges etc.
Table 1. Sample table of user input data
|
Insurance claim |
Gender |
Age |
Education |
Number of Complaints |
Residence Area |
Smoking state |
|
1 |
F |
19 |
MSc |
0 |
Attiki |
1 |
|
0 |
M |
32 |
BSc |
0 |
Crete |
0 |
|
0 |
M |
26 |
BSc |
2 |
Makedonia |
1 |
|
1 |
F |
29 |
High School |
1 |
Corfu |
0 |
|
|
M |
35 |
BSc |
0 |
Peloponnesus |
0 |
System prerequisites:
- Toolbox accepts xlsx or csv files.
- The first column should contain the data from the target variable (eg “Insurance claim”), 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 reasonable period of time has passed 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 forecast results in case the user wants to make a forecast (see Prerequisites 4).
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.
