How Banks Utilize Big Data Technology
With the banking regulatory landscape undergoing a transformation, banks have realized the need for deploying Big Data technology to efficiently manage risks that come with growth of data.
Following are some cases that explain how Big Data technology has been put into practice to meet critical banking requirements:
Fraud Detection and Security
Address anomalies in detecting fraud by utilizing analytics, machine learning and Big Data technology to achieve a holistic view of clients, identify patterns hidden in data, cluster information, and recognize fraudulent activity from normal activity.
Compliance and Regulatory Reporting
Comply with a key provision of regulations like the Dodd-Frank Act, BCBS 239, MiFID II, Volcker Rule, etc. that requires generation of reports on a timely basis using data in raw format which additionally needs to be cleaned for use. Hence, provisions need to be made to address such challenges by utilizing Big Data technology.
Customer Segmentation and Personalized product offerings
Organize clients under various fragments to maintain sales, promotional advancements, and marketing campaigns by breakdown of data for analysis and utilizing Big Data technology for intelligence. This also enables personalized product and service offerings as per customer needs and trends.
Support new regulations and growing demand for better internal management support by devising a central, integrated finance, and risk management data platform that can rapidly and flexibly address new obligations.
What makes Big Data innovation so appealing for Banks and Financial Services?
The market for big data technology and services will grow at a compound annual growth rate (CAGR) of 23% through 2019, according to a forecast issued by research firm International Data Corp. (IDC) on Monday. IDC predicts annual spending will reach $48.6 billion in 2019. The industries with the fastest growth rates include securities and investment services (26 percent CAGR), banking (26 percent CAGR) and media (25 percent CAGR). 
Quite a bit of this money goes into the 3 key segments of any Big Data strategy:
- Storage and network services that support Big Data platforms
- The platforms themselves, which empower data to be analyzed
- The software applications that make sense of it – be it applications that enable substantial volumes of data to be analyzed and handled or those data-driven applications that give further, informative insight.
The result of this is more “off the shelf” solutions are becoming accessible and the expense of storing and examining data is falling – implying that the opportunity to expand Big Data platform is coming within the reach of the financial services industry.
Role of Big Data in overseeing financial regulatory reporting and managing data related challenges
Some of the greatest difficulties that organizations face with Big Data are dealing with the expansive volumes of data. The need of the hour is to organize it correctly and gain beneficial insights.
Regulatory restrictions facing Big Data
There are currently a number of regulatory restrictions that are affecting the utilization of Big Data. For example, select banks were under pressure to comply with Basel Committee on Banking Supervision (BCBS) 239, which plots 14 standards around risk aggregation and reporting practices. According to Rupert Brown, author at MarkLogic, BCBS 239 is “focused on understanding the provenance, lineage and classification of data and is probably the most significant regulation.”
The capability to consolidate and synchronize all appropriate risk data can establish the framework for more overarching and consistent analysis, empowering better business management and improved operating models.
In the meantime, regulators are frequently intervening in the details of the systems and processes through which banks oversee themselves. In many respects, the financial crisis did appear to illustrate to policymakers and the wider public alike, that the banking industry was unequipped for overseeing itself in an economically prudent and socially acceptable manner.
Responding to the complex regulatory agenda requires that banks simplify and streamline data management, data structures and processes. The upside needs to be cost reduction, expanded efficiency and enhanced returns on capital.
Banks are fast realizing the importance of technology and this has led to wider adoption of Big Data and other technology solutions by banks. In a recent whitepaper released by the German giant, Deutsche Bank stresses on the various advantages of cooperation with FinTech firms and explains how it sees the industry as an opportunity more than a threat. This shift in attitude is an important avenue for the rise of Big Data adoption seen in the finance industry.
The Complete RegTech Compliance Solution from Hexanika
Hexanika is a RegTech big data software company which has developed a software platform SmartJoin™ and a software product SmartReg™ for financial institutions to address data sourcing and reporting challenges for regulatory compliance.
Our innovative and unique algorithms and rules engine enable business users to manage the data effectively and the automated nature of SmartReg™ keeps regulatory reporting at pace with the dynamic regulatory requirements thereby catering to market needs efficiently.
Contributor: Kush Sharma
Evolution of Data Integration Post the Implementation of Dodd-Frank: https://hexanika.com/evolution-of-data-integration-post-the-implementation-of-dodd-frank-act/
Value: The lesser known V of Big Data: https://hexanika.com/value-the-lesser-known-v-of-big-data/
Hadoop in Banking: The Game Changer: https://hexanika.com/hadoop-in-banking-the-game-changer/
Commonly Used Hadoop Tools in Banking: https://hexanika.com/which-are-the-commonly-used-hadoop-tools-in-banking/
RegTech is the new FinTech: https://hexanika.com/regtech-is-the-new-fintech/
 CIO: IDC says big data spending to hit $48.6 billion in 2019
 A Review of BCBS 239: Helping Banks Stay Compliant: https://hexanika.com/a-review-of-bcbs-239-helping-banks-stay-compliant/
 Bobsguide: Big Data Challenges, Risks and Solutions