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Improving organization's financial strength through transparency in bank operations

Updated On : February 2017

Controllers & Regulators, shareholders, and internal business stakeholders want better transparency into bank operations in order to improve organizational financial strength. Also, controllers & managers wants that financial institutions to establish suitable levels of:

  • Risk data aggregation
  • Robust Comprehensive Capital Analysis and Review (CCAR) processes
  • Strong liquidity monitoring measures
  • Thorough and ongoing recovery and resolution planning process

Universally, several banks/financial institutions struggle to manage data with the rigor required to satisfy these demands, and plenty of them fail to support stronger collaboration between departments like Finance & Risk. Initially, this might have been invulnerable. Though data is the mainstay of both risk and finance, the two sections have factually consumed and outputted it very inversely.

According to a fresh research done by the Global Association of Risk Professionals and SAS, less than half of the managers surveyed said their financial institutions know-how on data management was satisfactory or week. For organizations that has dearth of this know-how, they are being deprived of understandings that we feel could help them enhance business decisions and, in many cases, can leave them open to additional regulatory examination. The risk and finance functions will play a very important role in determining and solving these deficiencies.

Our recommendations

By focusing on the four key components of data management, banks/financial institutions can improve their data management capabilities across the organization as well as among the risk and finance teams. Below we discuss specific recommended activities related to each component in more detail.

Data ownership and stewardship

  • Empower CDO with full support from C-suite
  • Create data governance council
  • Align risk & finance teams
  • Oversee the execution of data policies & procedures

Data architecture

  • Define, consolidate & standardize the blueprint for data sourcing to a "single point of truth" across the risk & finance functions
  • Standardize definitions & classifications of products, customers and other variables
  • Reduce data replication

Metadata management

  • Develop standards & systems to manage the source, quality, consistency, usability, security & availability of data
  • Define consistent business information model
  • Define standards of continually refine data attributes & better manage how data is sourced, collected & stored

Data delivery

  • Assess reporting needs across the risk & finance functions
  • bPartner with business to enhance reports
  • cPartner with IT to take benefit of leading technologies

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