HPC in Fraud Detection in the Credit Card Business
The number of credit card transactions exceeded 60 billion in 2007, or over 2 thousands transactions per second. This is not really incredible at roughly 10 transactions per head per year for the world as a whole. With this volume of data moving through the grid, how is fraud detection keeping up?
For both internet vendors and brick-and-mortar vendors, authorization of credit card payments needs to complete on a real-time basis. To be effective, the fraud risk score also needs to be available at the same time as the authorization. Even after a delay as short as one minute, the customer will have walked. To mitigate and eliminate fraud proactively, major authorization vendors are delivering the transaction risk score as part of the authorization message.
One solution utilizes a score card system and this is where HPC comes into play. Once a new transaction event arrives in the system, its risk level can be evaluated against the scoring rules. HPC provides the infrastructure support in two phases: firstly, during the data mining phase to derive better scoring rules; secondly, during the production phase to provide the real-time performance.
As early as April 2006, the super-computing centre Los Alamos National Laboratory in New Mexico, USA, published a paper "Iterative Algorithm for Finding Fraudulent Patterns in Transactional Databases".[1][2] The joint-research between the laboratory and Citigroup benchmarked the effectiveness of different algorithms in detecting frauds via pattern detection. The techniques can be expanded to help detect criminal behaviour and terrorist activities.
Credit risk and fraud management systems that include more data types and more analytical models have an edge in protecting against customers and reducing fraud losses. Credit risks and fraud can be reduced at both the account level and the transaction level.
06 Mar 2009, Kenneth Young
[1] www.lanl.gov/orgs/t/publications/research_highlights_2006/docs/RH06_Berman_iterative.pdf