Across continents, financial services companies including retail, commercial, investment and private banking firms are emerging from the financial crisis to new realities that will force them to change, consolidate or die. The first is overall decreased levels of lending activity and low rates that cut into margins. Second, there is the new regulatory and risk-capital regime with more not less regulation likely in future. Besides regulatory pressure, there are internal drivers for increased focus on risk management. Third the balance of power is shifting from institutions to the customer armed with more information, increased awareness of their own risk and needs, and demanding more responsive products and services. And last there is increased competition from other established banks but also new providers such as crowd sourcing, boutiques, captive finance, and global expansion ambitions of emerging market players.
Having re-stabilized their balance sheets and met mandated obligations, many bank CEOs know what needs to be done but are struggling to implement them. CIOs have to readjust the culture and practice from an era of excess and home-built proprietary systems to a focus on agility. Learn how Data Virtualization for financial services It is helping banks quickly evolve to the era of cloud, mobility, big data and customer-focus across a broad spectrum of projects, such as:
An innovative commercial bank has doubled its market share in less than a decade by focusing relentlessly on the end-to-end customer experience, relationship and tailored product and service offerings. In this case, when using data virtualization for financial services you go beyond simply integrating CRM and banking applications to capturing and incorporating less-structured information on multi-channel usage patterns, feedback across all touch points and even business planning and future needs data. To make this actionable required delivering canonical data that could be leveraged in different systems of engagement including web and mobile applications, relationship manager tools and closed-loop survey and feedback systems.
The Basel Committee on Banking Supervision has issued 14 principles for evaluating, managing and reporting risk, which form the basis of a compliance mandate set to take full effect in 2016. Risk aggregation is a difficult task since typical large banks have several risk entities - market, credit, counter-party risk and so on, and several divisions in many countries. Data virtualization applied in financial services can quickly "roll-up" risk exposure across these layers without the cost/effort of moving data to provide a near real-time, consolidated view of risk. It also allows standardization of risk calculations to be done once and only once against a common risk data model to provide reliable and accurate reporting.
Advanced analytics requires the leverage of ALL the data to algorithmic patterns rather than using a constrained data set. Banks now have access to a stream of customer data and usage patterns of banking services from credit card transactions to mobile banking and networks of people and businesses. While preserving privacy, banks are using advanced analytical insights in several ways - mass customization of products and services, fraud detection, contextual marketing offers, and predictive intervention to retain unsatisfied customers. To do this well requires data virtualization to enable a logical combination of enterprise data with fast-moving big data, without incurring the cost of large scale data replication.
In the age of the customer, banks that are slow to release newer and upgraded applications that effectively engage the customer through multiple modes of interaction, often lose them. One financial institution found that it had multiple application development teams - web, mobile, front office, back office, reporting & BI etc. - that were all accessing the same data. By creating a separate data services team that released and maintained several enterprise data services, they could speed up new application delivery by 100-300%.
When two major Japanese Banks came together to form a mega-bank, the new management team had a mandate to operate like one bank in 6 months and fully integrate in 2 years. The first goal would have been simply impossible to meet without the use of data virtualization to provide unified view of key customer and product data. This also helped accelerate the prototyping of target integrated systems and provide an abstraction layer to ease migration of data from one system to another or to entirely new cloud systems without affecting normal operations.