Financial Services’ Big Data To-Do List

According to Neil Palmer, a partner with SunGard’s consulting services, “Financial services firms are consolidating data traditionally managed in silos in order to analyze risk exposure, comply with regulatory mandates, and use the data for multiple purposes.”

He says that traditional approaches, including relational database management systems are further compounding these challenges, making it nearly impossible for financial services firms to contend with their growing data in a way that’s simple to manage, accessible, integrated and flexible.

Palmer recently partnered with IDC Financial Insights research director, Michael Versace to lay down the list of the top trends that are having an impact on the future of big data in financial services. The ten items are as follows:

• Larger market data sets containing historical data over longer time periods and increased granularity are required to feed predictive models, forecasts and trading impacts throughout the day.

• New regulatory and compliance requirements are placing greater emphasis on governance and risk reporting, driving the need for deeper and more transparent analyses across global organizations.

• Financial institutions are ramping up their enterprise risk management frameworks, which rely on master data management strategies to help improve enterprise transparency, auditability and executive oversight of risk.

• Financial services companies are looking to leverage large amounts of consumer data across multiple service delivery channels (branch, Web, mobile) to support new predictive analysis models in discovering consumer behavior patterns and increase conversion rates.

• In post-emergent markets like Brazil, China and India, economic and business growth opportunities are outpacing Europe and America as significant investments are made in local and cloud-based data infrastructures.

• Advances in big data storage and processing frameworks will help financial services firms unlock the value of data in their operations departments in order to help reduce the cost of doing business and discover new arbitrage opportunities.

• Population of centralized data warehouse systems will require traditional ETL (extract, transform, load) processes to be re-engineered with big data frameworks to handle growing volumes of information.

• Predictive credit risk models that tap into large amounts of data consisting of historical payment behavior are being adopted in consumer and commercial collections practices to help prioritize collections activities by determining the propensity for delinquency or payment.

• Mobile applications and internet-connected devices such as tablets and smartphone are creating greater pressure on the ability of technology infrastructures and networks to consume, index and integrate structured and unstructured data from a variety of sources.

• Big data initiatives are driving increased demand for algorithms to process data, as well as emphasizing challenges around data security and access control, and minimizing impact on existing systems.

As Versace noted, “Big data is one important trend driving investments in enterprise analytics, and analytic excellence is core to much needed innovation in today’s finance industry. Business analytics applied to relationship pricing, capital management, compliance, corporate performance, trade execution, security, fraud management and other disciplines is the core innovation platform to improving decision making.”

He says that for this industry, analytics and the ability to efficiently and effectively exploit big data and advanced modeling, in memory and real-time “decisioning” across channels and operations, will distinguish those that thrive in uncertain and uneven markets from those that fumble.

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