Monday April 3, 2017
Processing close to 25% of all US credit card transactions, this global financial services leader has more than 120 million customers worldwide. To better anticipate and identify fraudulent activity, the company sought to analyze credit-card activity in real time; but their traditional multi-hop data architecture could no longer keep up with such hyper-speed analysis demands.
Historically, to analyze activity for risk and fraud related activity across regions, vendors, products, days, periods, currencies, and other dimensions and measures, analytics professionals had to move anonymized credit card activity data into multi-dimensional cubes on a regular basis. This process entailed moving raw card data into Hadoop, processing it via ETL into a data warehouse, structuring the data via SQL Server, and updating a cube via Microsoft Analysis Services. This multi-step process introduced not only recurring 4+ day cube rebuilds, but also obstructed analysts ability to identify and get ahead of fraudulent trends.
To achieve this objective meant being able to analyze transaction level card data in tandem with data collection, which meant all data had to be in the same place at the same time; enter Hadoop + AtScale. Now analysts access and query all the data as it lies in Hadoop, and analyze multiple years of credit-card activity side by side; not simply 1 annual quarter at a time (what fits in a single cube). When data related to fraudulent or risky activity is identified, drilling to credit card transaction detail is immediate; versus an IT ticket requesting transaction detail and a multi-hour or day wait. By removing the need to move data 3 times and wait 4-days to rebuild cubes, they are able to pinpoint fraudulent transaction almost immediately after they occur, and begin to use that information to predict and avoid future fraud.
In addition to faster risk and fraud analysis, eliminating repetitive data moves has meant they are better able to track and prove data lineage. As opposed to risking data interference with ETL and data movement they confidently accommodate regulators by quickly identifying the when, where and whom behind the life of data from transaction to analyzed and reported results.
With real-time, complete, governed, self-service access to all credit card data in Hadoop, this global financial services company is better able to track and update fraud protection algorithms, to deliver consistent protection and services to customers worldwide.