Our AI and Data Science Case Studies:
Experience from over 1,600 customer projects
Fraud prevention at banks through efficient verification of salary slips
In the past, our client, a reputable financial institution, faced a significant fraud prevention challenge. In order to prevent fraud, salary slips from financing recipients were randomly and according to a heuristic manually checked. This method, although effective, was Labour and time intensive and burdened the staff.
The client's main objective was to reduce the number of payslips that needed to be manually verified without worsening the fraud detection rate. A solution was needed that was efficient, accurate and compatible with the company's current technology infrastructure.
Using machine learning to improve fraud prevention
We have developed a bespoke, data-driven model for our client that replaces the traditional manual Process optimised. At the heart of the solution was a machine learning model for the Estimation of the probability of fraud in financing transactions. We used self-disclosure data as well as information on the vehicle and financing product as a database.
To determine the optimal threshold to trigger operations for manual review, we performed extensive simulations. This data-driven, artificial intelligence-assisted approach enabled us to develop a model that Highly precise and efficient is. Machine learning has allowed us to not only automate the salary verification process, but also maintain fraud detection rates.
Productivity increase and consistent fraud detection rate
Through the implementation of the model we developed, the Salary statements to be checked manually reduced by over 50% become. This meant a significant productivity increase for the financial institution and a consistent fraud detection rate - an excellent result that met our client's expectations. After a thorough validation phase, the model was successfully implemented in the productive system.
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Key Account Manager | Alexander Thamm GmbH