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Ten “big data” trends transforming financial services
Publication date: 18 June 2012
Author: Neil Palmer (SunGard) and Michael Versace (IDC Financial Insights)
Financial services firms are consolidating data traditionally managed in silos in order to analyse risk exposure, comply with regulatory mandates, and use the data for multiple purposes. Traditional technologies such as relational database management systems make it challenging, if not impossible, to process growing volumes of data and make it accessible, actionable and flexible to changing needs in terms of queries and analytics. ‘Big data’ solutions that support evolving business and regulatory requirements by maintaining an ecosystem of large data sets will become invaluable in their ability to be used for multiple purposes and to answer any question months or years from now.
SunGard has identified ten trends shaping “big data” initiatives across all segments of the financial services industry in 2012. They are:
- 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 organisations.
- 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 behaviour 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 centralised 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 prioritise 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 emphasising challenges around data security and access control, and minimising impact on existing systems.
Big data is one important trend driving investments in enterprise analytics, and analytic excellence is core too much needed innovation in today’s financial marketplace. 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. Analytics and the ability to efficiently and effectively exploit big data, advanced modelling, 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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