The adoption of big data in the banking industry has become more of a necessity than a matter of choice. The banking industry is slowly replacing the traditional ways of banking with more technology-driven methods. Along with increased efficiency amongst banks, big data is helping banks in making more informed decisions and managing risk in real time. By combining the transactional, behavioral and social data of customers, banks are simplifying their daily working procedures and maximizing the value of unstructured datasets within a small time frame.
Post 2008 crisis, the changes in the regulatory framework for banks have been extensive and complicated. The new rules have increased pressure on banks that struggle to meet the regulations as quickly and as promptly when asked.
In March 2012, Big Data Research and Development Initiative was introduced under the Obama administration with the aim to improve federal government’s ability to make meaning out of the large amount of data. However, missing from the list of participants was any agency accountable for regulations of the financial services industry.
With introduction of many regulations like the Dodd-Frank Wall Street Reform and Consumer Protection Act in USA, Solvency II in Europe and Basel III for global central banks, the role of big data becomes significantly relevant and constructive to regulatory compliance procedures. The Dodd-Frank Act, adopted for regulations of banks extensively runs in 14,000 pages and with 71.2% finalized, it becomes important for banks to adapt to technologies that will help them comply with the new reforms on time.
With fraudulent crimes like anti-money laundering, trading abuse and other banking scandals, the Dodd Frank Act and Financial Industry Regulatory Authority (FINRA) regulations require banks and other financial institutions to monitor banking activities closely. Real-time fraud detection can be done through big data usage that will also help to identify anti-banking activities. Digital Reasoning's cognitive computing platform can not only sieve through structured and unstructured data, but also successfully locate any traces of insider trading or other suspicious activity. IBM’s big data and analytics platform enables banks to manage credit risk and avoid situations of default.
Besides detection of banking crimes, data analytics can help in building new compliance reports and performing stress tests. The annual stress tests by regulators require banks to aggregate data that is scattered across applications, databases, lines of business and separate legal entities. Hence, updating data and sourcing the adequate data are crucial to the stress-testing process. As banks use processes that feed data into a variety of models, they should use internal and external data to run these models from regulators’ perspective.
According to Deloitte, the requested information in many banks is not defined properly, and since information is managed in silos, it becomes a time-consuming task to produce it. This usually leads to a lot of manual shifts and extensive rounds of corrections. Automation and reliance on big data analytics for regulatory framework across banking industry can assist banks in meeting the regulatory requirements with the least error on time. Banks can also be alerted beforehand in case of any discrepancy in the actual and the expected requirement and take the essential action to correct it before the regulatory deadline.
The high cost of meeting the regulatory requirement is also burdening many banks. Besides, violations of the requirements are expensive and sometimes unaffordable for smaller banks that generate lesser capital than bigger banks. Extensive regulations are causing many small banks to shut down and in many cases leading to a growing number of mergers. So far, Dodd-Frank has come with a cost of $21.8 billion and 60.7 million paperwork burden hours, according to American Action Forum. The cost is likely to increase as more regulations in the Act get finalized. Data analytics will prove more cost beneficial in the long run in comparison to billions of dollars in fees and fines over small errors or oversights.
The effectiveness of Dodd-Frank also depends on regulators’ ability to use information that grows exponentially within a day. Banks should be proactive in adopting the new technological trend so that they are well-prepared for any future and sudden change in regulations. Having said that, organizations providing big data analytics should look at ways to provide the technological revolution to smaller banks at a reasonable cost. Without a time lag, both small and big banks will find it easier to comply with the revised version of different regulations by adopting data analytics in the working system.