Fraud is definitely top of mind for all banks. Steve Rosenbush at the Wall Street Journal recently wrote about Visa’s new Big Data analytic engine which has changed the way the company combats fraud. Visa estimates that its new Big Data fraud platform has identified $2 billion in potential annual incremental fraud savings. With Big Data, their new analytic engine can study as many as 500 aspects of a transaction at once. That’s a sharp improvement from the company’s previous analytic engine, which could study only 40 aspects at once. And instead of using just one analytic model, Visa now operates 16 models, covering different segments of its market, such as geographic regions.
Do you think Visa, or any bank for that matter, uses just batch analytics to provide fraud detection? Hadoop can play a significant role in building models. However, only a real-time solution will allow you to take those models and apply them in a timeframe that can make an impact.
The banking industry is based on data – the products and services in banking have no physical presence – and as a consequence, banks have to contend with ever-increasing volumes (and velocity, and variety) of data. Beyond the basic transactional data concerning debits/credits and payments, banks now:
- Gather data from many external sources (including news) to gain insight into their risk position;
- Chart their brand’s reputation in social media and other online forums.
This data is both structured and unstructured, as well as very time-critical. And, of course, in all cases financial data is highly sensitive and often subject to extensive regulation. By applying advanced analytics, the bank can turn this volume, velocity, and variety of data into actionable, real-time and secure intelligence with applications including:
- Customer experience
- Risk Management
- Operations Optimization
It’s important to note that applying new technologies like Hadoop is only a start (it addresses 20% of the solution). Turing your insights into real-time actions will require additional Big Data technologies that help you “operationalize” the output of your batch analytics.
- Engage customers in interactive/personalized conversations (real-time)
- Provide a consistent, cross-channel experience including real-time touch points like web and mobile
- Act at critical moments in the customer sales cycle (in the moment)
- Market and sell based on customer real-time activities
Noting a general theme here? Big Data can assist banks with this transformation and reduce the cost of customer acquisition, increase retention, increase customer acceptance of marketing offers, increase sales by targeted marketing activities, and increase brand loyalty and trust. Big Data presents a phenomenal opportunity. However, the definition of Big Data HAS to be broader then Hadoop.
Big Data promises the following technology solutions to help with this transformation:
- Single View of Customer (all detailed data in one location)
- Targeted Marketing with micro-segmentation (sophisticated analytics on ALL of the data)
- Multichannel Customer Experience (operationalizing back out to all the customer touch points)
Risk management is also critically important to the bank. Risk management needs to be pervasive within the organizational culture and operating model of the bank in order to make risk-aware business decisions, allocate capital appropriately, and reduce the cost of compliance. Ultimately, this means making data analytics as accessible as it is at Yahoo! If the bank could provide a “data playground” where all data sources were readily available with tools that were easy to use…well, lets just say that new risk management products would be popping up left and right.
Big Data promises a way of providing the organization integrated risk management solutions, covering:
- Financial Risk (Risk Architecture, Data Architecture, Risk Analytics, Performance & reporting)
- Operational Risk & Compliance
- Financial Crimes (AML, Fraud, Case Management)
- IT Risk (Security, Business Continuity and Resilience)
The key is to focus on one use-case first, and expand from there. But no matter which risk use-case you attack first, you will need batch, ad hoc, and real-time analytics.
Large banks often become unwieldy organizations through many acquisitions. Increasing flexibility and streamlining operations is therefore even more important in today’s more competitive banking industry. A bank that is able to increase their flexibility and streamline operations by transforming their core functions will be able to drive higher growth and profits; develop more modular back-room office systems; and respond quickly to changing business needs in a highly flexible environment.
This means that banks need new core infrastructure solutions. Examples might involve reducing loan origination times by standardizing its loan processes across all entities using Big Data. Streamlining and automating these business processes will result in higher loan profitability, while complying with new government mandates.
Operational leverage improves when banks can deliver global, regional and local transaction and payment services efficiently and also when they use transaction insights to deliver the right services at the right price to the right clients.
Many banks are seeking to innovate in the areas of processing, data management and supply chain optimization. For example, in the past, when new payment business needs would arise, the bank would often build a payments solution from scratch to address it, leading to a fragmented and complex payments infrastructure. With Big Data technologies, the bank can develop an enterprise payments hub solution that gives a better understanding of product and payments platform utilization and improved efficiency.