Watch our predictions come true! Fraud Detection with Self-Acting Data Mining for Mercedes-Benz Bank AG

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Amie Greer
6 years ago
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1 Watch our predictions come true! Case Study // Fraud Detection with Self-Acting Data Mining for Mercedes-Benz Bank AG

2 Management Summary Keywords fraud detection, data mining, self-acting data mining Goals Check the existing data basis to assess how fraud analyses can be used Determine and document typical fraud patterns Forecast fraud probability with different types of contract Approach Use the cost-efficient analysis approach self-acting data mining to establish automatic fraud forecasting in a project with a short duration Results Identification of previously unknown suspected cases of fraud and fraud patterns Automatic recording of the fraud-relevant influencing factors Great reduction in work involved in checking contracts manually Significant ROI after just four months as a result of failed attempts to defraud Initial Situation In its many guises, obtaining credit by false pretenses leads to losses running into millions of euros. And this topic becomes even more important in the light of numerous legal requirements. For example, Section 25c of the German Banking Act requires banks to have sufficient control mechanisms and IT systems that are suitable for preventing, detecting, and reporting possible misuse of financial services. Data Mining for Fraud Detection: The Challenges Every 90 seconds, Mercedes-Benz Bank concludes a leasing or financing contract and only a fraction of these contracts can be checked manually for possible fraud. To increase the rate of fraud detection, contracts need to be automatically preselected. Simple reporting solutions or rule-based systems do not provide satisfactory results because, for example, new types of fraud are not discovered for a long time, since only known patterns can be recognized. While classic data mining approaches raise the rate of detection, they are costly, complex, and time-consuming to implement. While classic data mining approaches raise the rate of detection, they are costly, complex, and time-consuming to implement. Fraud prevention is becoming ever more important. We are glad that thanks to mayato we have been able to further increase our efficiency in fraud forecasting. Dr. Dietmar Exler Chief Operating Officer

3 The Advantages in Practice of Self-Acting Data Mining With the innovative new approach known as self-acting data mining, based on the KXEN Analytic Framework, mayato set up highly automated fraud forecasting in an all-time short project. The mayato project process which is designed to ensure analysis efficiency rapidly leads to valid analysis results, while the costs remain reasonable. The advantage of this method is that it enables you to predict a project s success at an early stage. After just a few weeks, it s possible to tell whether further investments in analyses make sense. Analysis and modeling Another advantage of self-acting data mining lies in the fact that time-consuming standard tasks are highly automated: From a total of more than 260 existing variables, it was possible to identify the 30 characteristics most relevant for cases of fraud even though many of the variables were highly intercorrelated. This auto focus on just a fraction of the data reduces the amount of work involved in maintaining and procuring the data, and reduces the calculation time. In the analysis phase, there was no need to adjust data granularity, or to manually parameterize the data, either. Validating the models A comprehensive validation of the forecast models using a subset of over 60,000 contracts testified just how powerful the solution is. As well as providing confirmation of already known cases of fraud, the fact that a large proportion of contracts could be classified as free from suspicion made this solution entirely convincing. By extending the data basis and refining the modeling, it will be possible to increase the model quality even more in the future. Calculating and applying the forecast models takes just a few seconds and is surprisingly intuitive. I didn t expect the forecast to be so accurate. Markus Tendies Fraud Prevention Employee Applying the Models With the fully automated and targeted selection of potentially fraudulent contracts, it was possible to reduce manual checks to a minimum. In total, more than 250,000 contracts were analyzed in just a few minutes. In terms of practicality, it was essential that the forecasting technique used did not place high demands on the data quality: Outliers and missing values do not lead to a significant reduction in the quality of the results. The project is a resounding success for us: The first automatically identified cases of fraud emerged as having a great potential for loss. We have already managed to recoup the project costs many times over. mayato excels with its outstanding data mining expertise and reliable project planning, making the company an ideal partner for us. Monika Schwarz Fraud Detection Officer Additional Practical Benefits In addition, the key fraud indicators can now be displayed separately in detail for each contract type at the touch of a button. These validated results disproved and differentiated a number of prevailing assumptions. For instance, it became clear that private and commercial leasing and financing contracts have very different influencing factors in terms of fraud relevance, and that some key figures and variables such as certain types of product or contract durations play a more important role than had previously been assumed.

4 With total assets of 18.8 billion in 2008 and a contract portfolio for leasing and financing of more than 17 billion, Mercedes-Benz Bank is one of Germany s leading automotive banks. It employs more than 1,300 people at nine locations. About mayato mayato GmbH empowers companies to capitalize on their information. Together with our customers, we develop and implement solutions in the areas of financial analytics, customer analytics, industry analytics, and IT security analytics. A team of experienced process and technology consultants operates out of our offices in Berlin, Bielefeld, Mannheim, and Vienna. They analyze and optimize your business processes and work with you to determine the requirements for technical implementation. They assist you in selecting the right tools, develop successful strategies, and conceptualize tried-and-true modern architectures. And of course, mayato consultants also help with the practical side of implementing your chosen solutions. Technical standards and governance enable economical, effective projects and efficient operations in the long term. Analysts and data scientists from mayato use these solutions on your behalf to establish connections between data from many different sources and to forecast trends and events. They devise convincing business cases and produce tangible monetary benefit from your processes and applications. Your employees learn how to use state-of-the-art data analysis methods, how to tackle data quality issues, and how to interpret and visualize results. Working with mayato future-proofs your company for the age of big data. mayato GmbH was founded in Among our customers are renowned large and midsize companies from a range of industries. As a partner of several software providers, mayato is committed to remaining neutral and first and foremost to delivering its own high-quality services. For more information, visit

5 Watch our predictions come true! Contact us // mayato GmbH Am Borsigturm Berlin info@mayato.com +49 /

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