INSURANCE & FRAUD
Claims fraud detection: investigate the right claims first
Claims fraud detection with TIMi identifies, in a flow of insurance claims, those with a high probability of being fraudulent or exaggerated, and shows the factors behind each score.
Suspicion score
Each claim gets a score, so adjusters know where to look first.
Free text included
Declarations and expert reports feed the model, not only the structured fields.
Explained per claim
Each score comes with the factors behind it.
HOW IT WORKS
How claims fraud detection works.
Three steps, in one engine, on a standard server with no cluster.
1. Prepare the data
Anatella gathers the structured claim data (amount, type, declared circumstances, policyholder history) and the free text of declarations, then structures them into variables.
2. Build the model
Modeler compares the profile of claims already confirmed as fraudulent with claims in progress, combining structured data and signals extracted from text.
3. Score and explain
Each claim gets a suspicion score with its factors, such as an inconsistency between declared circumstances and objective data, or a profile close to proven cases.
Anatella
ModelerHOW IT DIFFERS
What is specific to insurance claims.
The principle is the same as for general fraud detection. The data is not.
Use the free text
A large part of the useful information sits in the free text of declarations, not only in structured fields. Anatella includes text-mining operators that turn raw text into variables Modeler can use, next to the numeric data.
Check the language
Text processing has to be adapted to the language or languages of the declarations. This is a point to validate for each deployment.
Separate fraud from exaggeration
A model can be trained to distinguish levels of suspicion, from an exaggerated amount to characterized fraud, depending on the cases available in your history.
EXPLAINABILITY
A score says examine. The factors say what to examine.
A suspicion score alone says a claim should be reviewed, not why. A white-box model shows the factors behind the alert for this claim, so adjusters focus on the lead identified instead of reviewing the whole file.
For claims teams
Handling time per suspicious file goes down, because the triggering factor is already identified.
For transparency requirements
A model that justifies each result variable by variable gives you the documentation to answer transparency expectations such as GDPR and the AI Act. The compliance assessment itself stays with your legal team.
For model owners
Fraud patterns change over time. Modeler’s built-in Drift Report shows when a model loses accuracy, so you update it when needed instead of on a fixed calendar.
DATA
What data a claims fraud model uses.
Anatella connects to more than 80 sources, so this data can be gathered without prior custom development.
Claim data
Type, amount, date, coded circumstances.
Free text
Declarations and expert reports.
Policyholder history
Past claims, contract seniority.
Confirmed fraud cases
The learning base.
PROOF
Closest references.
We do not yet have a published customer story on insurance claims fraud. These two deployments cover related building blocks: behavior-based fraud detection and text mining on national databases.
Recovery costs cut by half
A predictive model flagged suspicious behavior from the first month after a subscription was activated.
Criminal patterns detected in national databases
Text mining applied to national databases, with data refreshed in under 6 hours each day.
FAQ
Common questions.
How is this different from generic fraud detection?
The modeling principle is the same. Claims fraud detection relies more on combining structured data with the free text specific to insurance (declarations, expert reports).
Do we need a claims expert to read the results?
The model produces a score and its explanatory factors. The final decision to investigate or to refuse coverage remains a business decision, usually validated by an adjuster or an expert.
Can it handle declarations in several languages?
Text processing has to be adapted to the languages of the declarations concerned. This is a point to validate for each deployment.
Can it flag exaggeration rather than pure fraud?
Yes. The model can be trained to distinguish levels of suspicion, from an exaggerated amount to characterized fraud, depending on the cases available in the history.
SEE FOR YOURSELF
Test it on your own data.
Run TIMi on your own data with a free trial, or talk to us about your case.
