10 Trends Shaping Big Data in Financial Services

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:

  1. Companies require larger market data sets and deeper granularity to feed predictive models, forecasts, and trading throughout the day.
  2. 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.
  3. Financial institutions are ramping up their enterprise risk management frameworks to help improve enterprise transparency, auditability, and executive oversight of risk.
  4. Financial services companies are looking to leverage large amounts of consumer data across multiple service delivery channels to uncover consumer behavior patterns and increase conversion rates.
  5. As they make significant investments in local and cloud-based data infrastructures, emerging markets like Brazil, China, and India are outpacing Europe and America..
  6. Advances in big data technology will help financial services firms unlock the value of data in operations to help reduce costs and discover new revenue opportunities.
  7. Traditional data warehouse systems will need to be re-engineered using big data technologies to handle growing volumes of information.
  8. Predictive credit risk models that tap into large amounts of payment data are being adopted in consumer and commercial collections practices to help prioritize collections activities.
  9. Mobile applications, tablets, and smartphones are creating greater pressure for company networks to consume, index, and integrate structured and unstructured data from a variety of sources.
  10. Big Data initiatives are driving increased demand for algorithms to process data, emphasizing challenges around data security and access control, and minimizing impact on existing systems.

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Source : http://www.fx-mm.com/15989/blog/ten-big-data-trends-transforming-financial-services/

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