In today’s financial services industry, ever-growing and changing regulations constantly increase regulatory compliance and reporting time, efforts, and cost. BCBS¹ 239, RDARR², and CCAR³ are just a few of the regulations which the financial services organizations need to adhere to. Most of the banks globally are being subjected to hefty fines and penalties due to non-conformance with the above regulatory requirements. But why is regulatory compliance an issue? The pace of the regulatory change remains extremely dynamic. If we look at how compliance reporting is done today, we will find it to be a significantly manual, multi-step, rigorous process that lacks transparency. There is an inherent need to formalize this process in the financial services domain.
The size, complexity and continuous change that characterizes financial services data make regulatory compliance analytics a painful endeavor.The product and customer information sit in silos, preventing data integration and a comprehensive view of challenges, trends, and opportunities. Thus, compliance efforts and related analytical demands account for a larger percentage of financial services operational budgets. On the whole, the banking industry spends approximately $270 billion a year on compliance costs⁴ (Patnaik D., November 2017).
Data is classified into various types and includes reference, transactional, operational, and security data. They vary widely in terms of size, shape, frequency of change, and each of these data types needs to be managed in a disparate fashion. Banks may be required to perform additional analytics and reporting on varied subsets of their data each time a change is introduced in the compliance requirements. Banks are now looking forward to technologies that aim to streamline the laborious compliance requirements as traditional approaches require longer processing times to simplify and analyze the enormous data volumes necessary for regulatory reporting.
This is where big data comes to the rescue of banks and financial institutions. Compared to traditional approaches, the newer big data technologies can very effectively consolidate multiple, distinctive data sources and analyze huge volumes of data, reducing compliance analytics cycle times from weeks to days. And, bigger banking players in the global market are already using next-generation big data analytics based on Hadoop to deliver faster, powerful, and exceptional regulatory compliance analytics. Learn about big data file formats in our blog here.
Clairvoyant, a leading data and decision engineering company, can provide best-of-breed big data analytics solutions for compliance reporting. Clairvoyant delivers a state-of-the-art big data platform that can handle banking regulatory compliance's analytical and architectural challenges. It can help banks drive new insights and business improvement in varied compliance analytics and regulatory reporting areas. To get the best data engineering solutions for your business, reach out to us at Clairvoyant.
² Risk Data Aggregation and Risk Reporting
³ Comprehensive Capital Analysis and Review
⁴ Patnaik D. (2017, November) The Rising Cost of Compliance & How the Best Banks Respond
Credit Suisse. (2017, November) How Big Data Analytics Is Transforming Regulatory Compliance. Retrieved from: https://www.credit-suisse.com/corporate/en/articles/news-and-expertise/how-big-data-analytics-is-transforming-regulatory-compliance-201711.html
NTT Data Inc., 2014 Big Data Solutions Ease Financial Services Compliance & Reporting. Retrieved from: https://uk.nttdataservices.com/en/-/media/assets/white-paper/analytics-big-data-bfs-compliance-and-reporting-whitepaper.pdf
Patnaik D. (2017, November) The Rising Cost of Compliance & How the Best Banks Respond. Retrieved from: https://www.trupointpartners.com/blog/cost-of-compliance-and-how-the-best-banks-respond
Deloitte U.S. (2017, December) 2018 Banking Regulatory Outlook. Retrieved from: https://www2.deloitte.com/us/en/pages/regulatory/articles/banking-regulatory-outlook.html