Centre for Excellence

  • ISSA Guidelines:
  • Error, Evasion and Fraud in Social Security Systems

Centre for Excellence

  • ISSA Guidelines:
  • Error, Evasion and Fraud in Social Security Systems

Error, Evasion and Fraud in Social Security Systems -
Guideline 13. Developing the institutional capacity for a massive use of external data

The institution applies data mining and data matching techniques on databases including large volumes of external data in order to increase the capabilities for error, evasion and fraud detection and analysis.

The growing importance of digitalization and computerization for the delivery of services by social security institutions heightens the specific risks of EEF related to ICT. For instance, experience shows that cases of EEF often originate in mismatches in the data communicated by benefit recipients or contributors to different social security institutions, to public services and even to some private services (financial services in particular). To address these risks, data mining and data matching increases the capabilities for analysis and detection.

Furthermore, sharing and integrating pooled information, such as that on income, contributions paid, benefits received, family profile, residence, etc., simplifies service delivery while reducing the risk of fraud. This approach also enables to reduce the risks of not adjudicating due benefits and of making the wrong calculation of the contributions amount to be paid by employers.