Fraud prevention for vehicle financing in the banking sector

A financing institution can use advanced analytics and machine learning to significantly reduce manual efforts in financing processes.


Reduction of salary statements to be checked manually by 56%


Consistent fraud detection rate


Optimisation of the internal process


In order to prevent fraud, the salary slips of financing recipients are now checked manually on a random basis according to a heuristic. In order to optimise internal processes, the number of salary statements to be checked manually is to be reduced. The fraud detection rate should not deteriorate in the process.


A machine learning model is being developed to estimate the probability of fraud in financing transactions. Self-disclosure data and information on the vehicle and financing product form the data basis. The optimal threshold value for triggering transactions for manual verification is determined.


The number of salary slips to be manually checked can be reduced by over 50% - and the fraud detection rate remains the same. The model is implemented in the production system.

Are you interested in your own use cases?


An automotive company would like to visualise various market-specific data in order to create a Competitive analysis for the US market.


There will be a interactive and Flexible application, including of different maps with two different views implemented.


Relevant markets are identifies, analyses and visualises. The dealer or the respective sales department have the possibility to compare the direct competition with their own product and to visualise the relevant data.

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