Data Analytics in the new era of capital markets

In 2015, organisations in the business finance solutions space are likely to spend significantly on business intelligence and analytics.This data goldmine can help organisations unlock hidden opportunities and insights.

In today’s real time capital market scenario, analytics play an important role in enabling better-quality, high-speed trading decisions and outcomes. Data analytics and risk management will be a fundamental change agent, driving competitive advantage by realigning business models for firms. Risk management will be the key in reducing operational, credit or market losses. We expect analytics to be deployed for identifying opportunities and risks in the client portfolio, as well as identifying potentially attractive clients.

1.    Predictive analytics harness data to gain real-time intelligence

Our experience in the capital market space illustrates that the financial services industry lacks real-time operational intelligence for better decision making, and to prevent system failures and catastrophic trading errors. To address this, firms would have to implement predictive analytics platforms, which would enable them to be more preventive about potentially damaging failures.  The volume of data in legacy systems can be tapped more effectively to uncover hidden patterns, relationships and dependencies.  IBM SPSS Statistics, IBM SPSS Modeler, Revolution Analytics, Statsoft are the major analytics platforms, which will be considered by both the buy and sell side players in the capital market space. 

2.    Managing unstructured data through Big Data technologies

Post sub-prime crisis in 2008 and flash crash in May 2010, regulators came down heavily on the transparency of OTC trades, which resulted in an increased number of data sources, the volume of market data increased significantly into the petabytes range, which needs to be analysed in real time scenarios. However, the increase in data is not a major issue for financial institutions to handle. The bigger challenge arises from the dramatic increase in unstructured data in the overall data volume. We expect firms to set aside a bigger proportion of IT spend on big data technologies, such as data grids, compute grids, massive parallel processors, in-memory databases, NoSQL, specialised databases, Hadoop and Netezza. We expect firms to go for unified analytics platforms to collaborate between structured and unstructured data and move away from traditional RDBMS towards specialised non-relational databases, such as Hadoop. In order to reduce IT costs we are already witnessing a gradual shift of storing and managing humongous data from physical infrastructure to the use of cloud storage.

3.    Risk Management – no more cost center but revenue driver

Organisational silos are preventing effective implementation of risk management practices. With the increasing pace in regulation, competition and customer expectations, and lack of access to required credit transaction and reference data for reporting and analysis; inconsistent counterparty data across business applications – no single source of the truth, invalid VaR and IRB risk management processes are making it more complex for  firms to do business effectively in this volatility market. To overcome the aforesaid challenges firms need to establish effective data integration and transformation programmes; need to design robust counterparty Master Data and reference data Management System which would enable consistent and accurate counterparty information to improve credit risk exposure analysis and reporting.

4.    Compliance and regulatory reporting

Effective data management is a challenge for organisations increasingly burdened by series of regulations such as, Dodd-Frank, EMIR, MiFID, Sarbanes-Oxley and Basel III. Data Analytics will play a big role in firms meeting their compliance requirements, from dealing with unstructured data, mining for market surveillance, cross-referencing of key sets of data in order to facilitate trade reconstruction and reporting.  We believe that in an environment of heightened scrutiny of data quality for regulatory reporting and where ad-hoc reporting is on the increase, speed and accuracy have lent an argument for Data Analytics.

5.    Meaningful and quality data helps in decision making

Data quality problems are widespread across financial institutions and they stand in the way of realising the full potential of data, including some of the benefits highlighted above. With ever increasing volumes, velocity and sources of data, managing data quality is also becoming more complex. We expect financial institutions to deploy processes and tools to address the data quality problem with some firms even going ahead and establishing centralised data quality management functions.

Source: bankingtech.com

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