Analytics help fuel growth & detect fraud in the financial sector

Financial services institutions (FSIs) are among the most data driven of all businesses. Operating under strict regulatory environment, commercial banks and insurance companies are required to store and process many years of transaction data. The pervasiveness of electronic trading has also meant that the capital market firms both generate and act upon hundreds of millions of messages and data every day.

Today, many local banks have already implemented some form of business intelligence to support the data management.

However, the co-existence of structured and unstructured data has become a massive concern. Financial services provider are looking for more advanced analytics capability to embrace the new sources of big data to harness the insights in real time and stay ahead of competitions, especially in the areas of better credit risk managements.

Sentiment Analysis and Brand Reputation
Whether looking for broad economic indicators, market indicators, or sentiments concerning a specific organization or its stocks, there is a mass of data on the web that can be harvested, from traditional as well as social media sources.

or example, sentiment analysis by studying social media is finding increasing acceptance. Hedge funds, such as Derwent Capita, are basing their strategies on trading signals generated by twitter feed analytics.

On-Demand Risk Analytics
‘On-Demand’ risk analytics is also now in demand by global banks. The objective is not just a faster measurement and reporting of risk, but also measurement across asset classes.

Aggregation of global positions, pricing calculations, and Value at Risk (VaR), all fall within the realm of Big Data.

This is due to the mounting pressure to speed these calculations up well beyond the capacity of current systems. Technologies like Java based in-memory data grids help enable faster calculations by allowing real time access to in-memory positions data and Map Reduce style parallelization.

Rogue Trading Detection
Rogue trading continues to be a huge threat for the financial institutions. Deep analytics that correlate accounting data with position tracking and order management systems can provide valuable insights to detect signs of rogue trading, which are not available using traditional data management tools.

For example, in couple of well-known cases (UBS and Société Générale), inconsistencies between data managed by different systems could have raised red flags if found early on. This might have prevented at least a part of the huge losses incurred by the affected firms.

Fraud Detection
Fraud detection involving cards, debit, and wholesale payments is also quickly becoming a big data problem. For instance, consider the potential of correlating Point of Sale data (available to any credit card issuer) with web behavior analysis and potentially with other financial institutions or service providers such as First Data or SWIFT, to detect suspect activities. Big Data tools help financial institutions refine their models to predict, identify, and prevent fraudulent activity.

Investigation and Compliance (e-Discovery)
To detect malpractices in fund allocation, investigators have to go through records of all the interactions associated with a financial transaction such as a trade order – including emails, phone transcripts, text messages, contracts, etc.

Retention of these records has been mandated for years, but the difficulty in associating them with the corresponding transactions has caused regulators to look for a far greater degree of correlation between structured transaction records and unstructured interaction records.

Big Data solutions help create this relation by bringing disparate levels of structure into the holistic data management platform.

Data driven decision making tools are helping financial institutions grow their business by reducing risk and meeting regulatory needs. Financial firms are slated to be one of the fastest adopters of Big Data & Analytics solutions.