Detecting fraud before payment

Insurance fraud, welfare benefit fraud, identity fraud…

Every year, attempted fraud affects 7 out of 10 businesses and the cost is high: over 5% of turnover of organisations according to the French Association of Certified Fraud Examiners.

Key point: documents are the preferred raw material of fraudsters.

In more than half of cases, fraud is based on a forged document (modification of the beneficiary, a date, an amount…) or on the creation of a fake document.

Detection methods by sampling, afterwards, made necessary due to lack of resources and time, allow a lot of fraud to pass by undetected.

Big data statistical approaches also have their limits: unless the fraud patterns have not been found, the fraud continues.

Only systematic detection, in documents and data, and before any business processing, can effectively limit financial losses and legal and reputational risks.


of insurance companies say that they check less than one quarter of the supporting documents that they receive from insured parties

Assurance en mouvement for ITESOFT, Towards a new digitisation era in insurance


of identity documents presented to open a bank account are fake

Fellowes Study


Fraud, compliance: the same fight

Customer relations is an increasingly regulated field: LCB-FT directives, Eckert, Sapin II, Solvency II, Basel II compliance, etc.

Impact for managers: ensuring the compliance of the supporting documents submitted by customers/users in the different processes (subscription, contract lifecycle, payout, claims, etc.). Time-consuming work that also lengthens the time for responding to case files.

Only the use of automation makes it possible to meet the growing regulatory obligations without harming customer experience.

However, can we comply with the legal regulations on the basis of fraudulent information? Responding to the challenge of compliance also involves considering the challenge of fraud.

This explains the importance of a global approach based on the digitisation of processes by embedding robots for:

  • Intelligent Document Processing (document collection, data extraction, compliance checks, comprehensiveness…)
  • Detection of attempted fraud (detection of data inconsistency and document alteration)

Obligations are therefore met and managers, assisted by the robots, no longer waste any time working on non-compliant or fraudulent case files.

1) Euler Hermès Barometer 2020


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