Fraud Management In The Age Of Big Data
A recent WSJ Article highlights advances made in the area of fraud detection and management at Visa by using Big Data techniques. The company estimates that their models have helped identify at least $2 billion worth of annual fraud, and have also given it the chance to address those vulnerabilities before that money was lost.
Financial services companies with operations spanning retail banking and credit cards must not only implement highly sophisticated and automated business operations but also must ensure that their business activities are transparent to business process owners, auditors and others.
In addition to managing new sources and more streams of data, they also face new regulations that create a greater need for compliance. Their IT leadership teams are pressured to not only support evolving business requirements, but the also must improve efficiency and manageability, contain costs and speed products to market -- all while ensuring security.
Let us consider the fictional but highly representative example of such a firm drowning in big data -- unable to effectively store, manage and best direct the data onslaught. They know that, when utilized effectively, data can be one of the company’s most important assets. Data not properly managed can quickly become a liability and cost.
For example, here is a model reference architecture from an end to end infrastructure and application re-architecture for an organization that is considering a Big Data initiative in the area of fraud detection and prevention.
Business Challenge
The Federal Reserve defines credit card fraud as "Unauthorized account activity by a person for which the account was not intended. Operationally, this is an event for which action can be taken to stop the abuse in progress and incorporate risk management practices to protect against similar actions in the future."
Accordingly the various categories of credit card fraud include - application fraud, lost or stolen credit cards, counterfeit cards, and account takeovers.
Business Requirements
1. Integrate & cleanse data to get complete view of any transaction that could signal potential fraud2. Predict cardholder behavior to provide better customer service
3. Help target customer transactions for personalized communications on transactions that raise security flags
4. Deliver them the ways your customers want -- web, text, email and mail
5. Track these events end to end from a strategic perspective
6. Help provide a complete picture of high value customers to help drive loyalty programs
Design and Architecture
The architecture needs to consider two broad data paradigms -- data in motion and data at rest.Data in motion is defined as streaming data that is being sent into an information architecture in real time. Examples of data in motion include credit card swipes, e-commerce tickets, web-based interactions and social media feeds that are a result of purchases or feedback about services. The challenge in this area is to assimilate a huge volume of data and filter it, gather reason from it and to send it to downstream systems such as a business process management (BPM). Managing the event data to make sure changing business rules/regulations are consistently integrated with the data is another key facet in this area.
Data at rest is defined as data that has been collected and ingested in a form that conforms to enterprise data architecture and governance specifications. This data needs to be assimilated or federated with pre-existing sources so that the business can query it in a read/write manner from a strategic and long-term perspective.
NEXT: Reference Architecture
As Chief Architect of Red Hat's Financial Services Vertical, Vamsi Chemitiganti is responsible for driving Red Hat's technology vision from a client standpoint. The breadth of these areas range from Platform, Middleware, Storage to Big Data and Cloud (IaaS and PaaS). The ... View Full Bio
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