Big Data’s Increasing Role in Financial Services

Big data is a pretty popular buzzword in the tech space these days. And while it has well established implications for marketing, product development and client retention, financial services organizations are also looking at big data to support core business functions related to asset and trade management, risk management, regulatory compliance and information security.

In a world where data is growing by exabytes per year, financial services organizations that leverage big data find themselves at a significant competitive advantage. By aggregating and analyzing near real-time data from across departments, geographic locations and third party vendors, financial services entities are able to reduce operational risk, maximize profitability, ensure regulatory compliance and fight fraud and other information security risks.

1. Trading and operations

Big Data enables the aggregation and analysis of real-time data from multiple internal and third party sources prior to trade execution. While historically, trade analysis involved some level of “gut”, today, more and more algorithms are being written to capitalize on data processing, often within micro-seconds.

The development of real-time enterprise modeling and analytics platforms frequently aggregate data extracts from all operational, third party and non-traditional data sources, including news and social media. Business users and analysts are then able to explore the data and develop analytic business models. By building an enterprise-wide self-service analytics platform, users are provided with controlled access to explore data when and how they need it. As a result, users obtain readily available, actionable insight, leading to higher margins and greater profits.

While real time trade data is certainly of critical importance in this process, historical transactional data also plays a big role in trade operations. Institutions can leverage historical data and market movement information to feed trading and predictive models and forecasts. By analyzing past performance, better predictive analyses can be established for future business modeling.

Through big data optimization, the front office gains greater insight into trade risk and exposure, counterparty reliability and cash management. As a result, they have the potential to improve profitability, efficiency and cost-containment with greater ability than ever before.

2. Regulatory Compliance

But big data isn’t only a front office initiative. Regulatory compliance is a critical issue in the financial services world, and is changing rapidly. In an environment where 1) regulations change frequently 2) adherence is complicated by detailed requirements and 3) the penalties for violating regulations increasingly costly, there is greater and greater attention to this area of risk management.

In the regulatory arena, fines for non-compliance can add up to millions of dollars, increasing the desire for tools which protect against violations. As a result, investment in big data solutions which proactively defend the institution are seen as important strategic decisions.

Institutions can use big data to measure regulatory compliance by combining regulatory data with supporting documents, contracts, attestation and transactional information. By monitoring unstructured and unrelated content, including IM chats, emails, and telephone calls, and combining them algorithmically with trading activity data and documentation, compliance teams are forewarned of potential conflicts putting the trader and the organization at risk.

3. Fraud Management

A financial services organization gets exposed to different kinds of fraud that can result in millions of dollars of losses. Predictive analytics tools are used to build models to detect and prevent fraud. Data is correlated across different sources to identify fraudulent behavior across unrelated data points. While credit card fraud detection based on location, prior shopping history and expense patterns are known to many, these capabilities can also be used in asset management scenarios.

These solutions operate in real-time and utilize in-memory technologies to analyze hundreds of terabytes of data for a real-time transaction to detect fraud.

By correlating seemingly unrelated incidents to identify fraud with greater speed and accuracy, financial institutions are able to reduce exposure and reduce liability for fraudulent losses.

Conclusion

Big Data plays an increasingly important roll across the front, middle and back office of financial institutions. By taking advantage of previously unavailable insights, custodians are better able to both serve their customers while capitalizing on business operations and profitability.

To learn how big data can help your BFSI organization, contact SRI Infotech today.