Use an AI model to predict the authenticity of an insurance claim
Requirement
Build an AI model to
predict
if an insurance claim is genuine.
Use case
An insurance application contains a form
—
Fraud Detection. To avoid being
inundated
by false insurance claims, whenever an entry is made to the form,
an AI model automatically predicts if the submitted claim is fraudulent.
Steps to follow
|
Form
|
Form Link Name
|
Field Type
|
Field Name
|
Field Link Name
|
|
Fraud Detection
|
Fraud_Detection
|
Single Line
|
Incident Severity
|
Incident_Severity
|
|
Currency
|
Total Claim Amount
|
Total_Claim_Amount
|
||
|
|
|
Dropdown
|
Authorities Contacted
|
Authorities Contacted
|
|
|
|
Number
|
Months As Customer
|
Months_As_Customer
|
|
|
|
Radio
|
Is Fraud Claim?
|
Is_Fraud_Claim
|
2. Add a
prediction field
with
the
field name and field link name
Likely to be Fraud
and Likely_to_be_Fraud,
respectively, to the
Fraud Detection
form with the following details:
Click
Add New Action, and add the following snippet:
- disable Likely_to_be_Fraud;
4. Import training data to the
Fraud Detection form. This helps the prediction field to analyze the data in the form and
use machine learning to predict a field value for all future records.
Sample training data
is attached as
a
file to this article.
5. Navigate to
AI models
under
Microservices and
retrain
the
Likely to be Fraud
model. By doing so, the AI model will be trained by considering the training data that we just imported.
See how it works
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