The Operationalization of Machine Learning Models

Challenge

Process optimization – Efficiency gains through intelligent automation

The intelligent use of advanced analytics, automation and machine learning is becoming increasingly important for banks in fraud prevention, especially in the credit risk assessment process. This approach enables analysis of huge amounts of data more efficiently than ever before, unearthing of suspicious patterns and thus, identifying potential risks at an early stage. This, in turn, reduces effort, increases efficiency and lowers costs. However, the operationalization of ML models, in particular, presents companies with major technical challenges. This case study on automated earnings statement control in the credit risk assessment process shows how the above approach can be optimally achieved by using ACTICO Decision Management Platform – by the combination of expert judgement rule models and machine learning algorithms.

 

Digital Processes & Machine Learning

The high number of manual checks in the credit risk assessment process ties up human resources which could be better utilized for other, higher-value tasks.

The introduction of digital processes, Advanced Analytics (AA) and Machine Learning could reduce the number of manual checks and thus, significantly increase efficiency.

Solution

• Development of a statistical forecasting model based on machine learning algorithms, which evaluates the probability of fraud in a credit application and thus, enables a targeted management of questionable applications.
• Operationalization of the forecast model.

Impact

• Reduction of the number of manual checks of customer earnings statements by 80% using Machine Learning.
• Through this process automation, VW FS achieved an annual savings of over 1 million euros.