Abstract
Payment fraud can be defined as an intentional deception or misrepresentation that results in an unauthorized benefit. Fraud schemes are becoming more complex and difficult to identify. It is estimated that industries lose billions of dollars annually because of fraud. The ideal solution is where you avoid making fraudulent payments without slowing down legitimate payments. This solution requires that you adopt a comprehensive fraud business architecture that applies predictive analytics.
Payment fraud is a significant problem for industries, such as banking, property/casualty insurance, and tax revenue. Technological advancements can help you move from a post-payment to pre-payment fraud detection architecture. IBM® System z® supports these advancements as well as providing traditional mainframe benefits. This IBM Redbooks® Solution Guide describes a fraud detection solution on System
z.
Contents
Payment fraud can be defined as an intentional deception or misrepresentation that results in an unauthorized benefit. Fraud schemes are becoming more complex and difficult to identify. It is estimated that industries lose billions of dollars annually because of fraud. The ideal solution is where you avoid making fraudulent payments without slowing down legitimate payments. This solution requires that you adopt a comprehensive fraud business architecture that applies predictive analytics.
Payment fraud is a significant problem for industries, such as banking, property/casualty insurance, and tax revenue. Technological advancements can help you move from a post-payment to pre-payment fraud detection architecture. IBM® System z® supports these advancements as well as providing traditional mainframe benefits. This IBM Redbooks® Solution Guide describes a fraud detection solution on System z.
Figure 1 gives an overview of predictive model processing.
Figure 1. Predictive model processing
Did you know?
To move from post-payment to pre-payment (real-time) detection requires that the detection system is moved closer to the payment system. Until recently, the state-of-the-art solution was to insert an interrupt into the transactional system, which would call the analytic system for a judgment of the transaction. However, latency (delay) is part of the process because of the resources and time that is used moving back and forth from one system to another. Because of this limitation, most payers perform this type of in-line detection on only a small sample of payments. This potentially allows some fraudulent payments through. Another problem is that stale data feeds the models because this solution relies on snapshots of the data.
Two technological advances make real-time and in-transaction pre-payment fraud detection a reality. One is the IBM DB2® Analytics Accelerator (IDAA), which directly links the analytics data with the operational data, significantly increasing the freshness of the data. The other is the IBM SPSS® Modeler 15 Real-time Scoring with DB2 for z/OS®, which allows the scoring of a payment to be made directly within the OLTP system, with only a small latency penalty, versus making calls for scoring at run time to web services.
Business value
Organizations can use real-time scoring to directly incorporate the newest and most relevant data into the decision making process in real time. You can use this scoring to proactively and repeatedly reduce costs and increase productivity. For example, you can complete the following tasks:
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