Data Management

03:30 PM
Drew Rockwell
Drew Rockwell

How Big Data Analytics Helps Financial Institutions Navigate Today’s Regulatory Maze

As firms work to meet new regulatory reporting requirements and keep pace with constant change, it all comes down to harnessing data.

Financial services firms face an increasing regulatory burden as a result of evolving directives and legislation. Whether it’s Dodd-Frank, CCAR, or BCBS 239, it’s clear that financial institutions are under pressure to establish more comprehensive, accurate, and timely reporting methods. On top of that, many firms need to comply with overlapping and changing regulations -- each with unique requirements -- in every jurisdiction where they do business. The regulatory landscape is increasingly complex.

As firms work to meet new regulatory reporting requirements and keep pace with constant change, it all comes down to harnessing data. These new requirements demand more data and analysis, but firms continue to struggle with data availability, system functionality, and data integrity.

Firms require a transparent and comprehensive view of their data and the ability to assess and extract vital insights from data on demand. This exposes the issue that, while the regulatory landscape has changed, the analytics tools firms are using have not. Spreadsheets and other end-user-developed applications are inadequate and cannot withstand the volume, complexity, governance, and transparency demands. With the rules still changing, large-scale legacy systems are too costly to update continually.

Comply with big data
The solution to this regulatory reporting nightmare is simple -- better big data management and analytics tools. We regularly hear about how big data and analytics are uncovering insights in other industries. How can they help financial firms adhere to new compliance regulations? Firms have the data there, but without the right enabling tools they’re unable to extract and report the insights needed to meet regulatory requirements. Here are the top four requirements for effective compliance with evolving regulations:

Speed: By pulling your data from disparate sources into one view, patterns or potential issues can be recognized faster. But to ask an analyst to monitor data continuously and manually is impractical. Issues will be missed. With the right analytics capability, data can be automatically integrated and monitored with a finer-toothed comb for defects, anomalies, and outliers. These red flags can quickly be raised before they become business-impacting issues. This process is a vast improvement over legacy methods that cannot uncover problems until weeks or even months go by, after the potential for severe damage has been done.

Another result of automation is the reduction in manual interventions or reconciliations that need to be done to create reports. With enabling technology to analyze its big data, one financial firm reduced manual adjustments by 1.5 million per month across all enterprise financial data. That means analysts can spend more time on other projects versus adjusting data sets that automation can handle.

Accuracy: Data quality is of the utmost importance. Reporting needs to be 100% accurate, because compliance failure has serious consequences, such as sanctions, fines, and loss of credibility. By governing big data, firms can run tests on their aggregated data, using different rules, to check data quality. Transparency and automation enable anomalies to be found. Compliance teams -- and regulators -- can begin to trust the data they’re working with.

Control: Financial firms struggle with the ability to achieve data consistency across the business -- the same data access and analysis on a repeated basis. IT’s data governance responsibilities need controls that enforce data standards, establish reusable and distributable data management and analytical assets, and enable automated data aggregation, analysis, and reporting. This ensures stability and increases operational efficiency, while the control over data enables compliance teams to pull what they need, when they need it.

Comprehensive view: In order to meet requirements and provide business insights, compliance teams must have a complete view of their data -- from end to end, not just events -- and a view of the data transformation. Spreadsheets often hide actions taken on data.

A better method would consist of exposing every step and providing a transparent view of the data through the entire process, not just one part or selection. New regulations especially emphasize transparency and analytics on big data, and can ensure that trades and investments, especially derivatives, are known to regulators, investors, management, and other key stakeholders.

Big data in action
Let’s look at a real-world example. At one brokerage and banking firm, whom we will call X Capital,  disparate data sources were cumbersome and difficult to monitor. Data management and analytics tools enabled X Capital to integrate and analyze data sources covering trading systems, decision support systems, CRM systems, network logs, and operational logs. The new comprehensive view of its data did more than provide the firm with better accuracy and control. Analytics enabled the firm to replace a legacy system update that was estimated to take more than a year at a cost of nearly half a million dollars. This move saved the firm months in implementation time and hundreds of thousands of dollars in costs.

Keeping up with the compliance regulations that are making up today’s financial landscape is burdensome, but today’s technology brings new opportunity. By harnessing big data using powerful analytics capabilities, financial reporting can be more timely, accurate, controlled, and comprehensive -- addressing regulators' needs.

Drew Rockwell is Chief Executive Officer of Lavastorm Analytics. Drew joined Lavastorm in 2002 to lead its transformation to a global analytic software and services company. He has 30+ years of executive and management experience in the communications and software industries. ... View Full Bio
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