Financial Companies Latch onto Big Data to Help Mitigate Risk
The Big Data market is expected to grow to more than $53 billion by the year 2017. Of this $53 billion, the market growth for it within the financial sector is an estimated $20 billion – further proving Big Data is a necessary component of the growth and profitability of the financial sector.
As the financial industry is using these large, information-rich data sets to reveal patterns, trends and associations in consumer behavior and interactions. This is a recent shift from previous years when big data was dismissed as an IT industry buzz word – with most business executives not understanding or taking the time to understand it.
“The industry is coming to terms with the importance of gathering increasingly detailed data surrounding the consumer,” said Paul Francis, Business Intelligence Practice Leader at DISYS. “And now, tapping into what the data has to offer is a key initiative.”
At Digital Intelligence Systems, LLC (DISYS), Banking, Financial Services and Insurance IT experts are deploying solutions in the area of Big Data that are proven, repeatable and compliant with financial industry regulations. DISYS implements low-cost, robot-enabled ongoing support and proven experience within Big Data with an estimated 30 percent cost savings over previous manual processes. DISYS is dedicated to reducing client’s costs and to increasing efficiency while reducing risk and enabling flexible solutions.
Now, with more than 5 quintillion unique pieces of information being created every 2 days across the globe, agile, repeatable Big Data processes are more important than ever. Every industry is looking for ways to capitalize on their data and interpret it in a way that boosts competitive positioning and increases margins through improved efficiency.
A recent financial institution survey says 60 percent of financial institutions in North America believe Big Data analytics offers a significant competitive advantage and 90 percent believe successful Big Data initiatives will define the financial industry frontrunners in the future.
“We have come a long way in being able to interpret the information Big Data provides and the next step is translating that to ideas that better serve banking customers,” Francis said.. “The current use of this information holds the key to the future success of banks across the globe.”
The financial services industry has been slow to the draw on implementing strong Big Data plans. While there is a lot of discussion around the topic, it has only been within the last few years banks have started to consolidate and utilize many of the internal data elements like debit and credit transactions, purchase histories, banking business service channels and loyalty to craft targeted campaigns that grow market share and increase profits.
“One of the biggest opportunities for Big Data usage within finance is in the area of risk management and fraud detection,” Francis said. “With the use of multi-channel insight provided by Big Data, institutions are better able to quickly identify changes in banking behavior beyond what is normally occurring with a client’s account.”
In the risk management area, BofA moved from a shared-services model to dedicated ‘Grid Computing,’ driving operational efficiency by early detection of high-risk accounts. Benefiting the bank in several ways, it reduces its loan default calculation time for a mortgage book of more than 10 million loans from 96 hours to just four hours. The bank is also able to process ad hoc jobs at three times the speed of the previous environment.
“Proper, efficient implementation of Big Data allows the financial sector to save time and money in so many ways,” Francis said.. “These manual, cumbersome, repeatable processes become automated and are done in a fraction of the time.”
This added insight and monitoring of Big Data through key algorithms provides huge advantages in mitigating risk and managing credit exposure. It also allows for timely intervention when necessary. In short, with Big Data, spotting a relatively small number of fraudulent transactions in a sea of legitimate payments becomes less difficult – despite the sizeable shift in behavioral patterns towards electronic and mobile payments.
Checking customers’ names against a sanctions blacklist can become highly complicated in a world where a bank has multiple customers with same or similar names. Each search runs the risk of flagging a false positive, thus embarrassing the bank and ruining an otherwise strong client relationship. By using Big Data techniques, this reputational risk can be mitigated and managed.
“There is no doubt that for these reasons alone, Big Data is extremely valuable for financial institutions and the consumer,” said DISYS Managing Director Mike Rutkowski. “When consumers feel safe and secure with their financial institution, banks gain the loyalty of the customer.”
DISYS dives into the digital banking and Big Data in-depth in its whitepaper, “Robotics Process Automation: Optimizing Today’s Banking Workforce.”