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Andy Efstathiou

Andy is the Banking Sourcing Research Director at NelsonHall where he has global responsibility for Retail and Commercial Banking BPS, Capital Markets BPS, and RPA, AI, and FinTech services in Banking, including consulting, design & deploy, and operations support.

Reference Data Management BPO Market Assessment and Forecast

Market Analysis

by Andy Efstathiou

published on Jul 27, 2012

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Report Overview:

NelsonHall's Market Assessment for Financial Industry Reference Data Management (RDM) BPO is a comprehensive assessment of the Financial Industry RDM BPO market consisting of 62 pages.

RDM BPO is curently bought as part of a larger engagement (typically 10% of an engagement) and has been driven by the need to provide "quality assured" data. Key challenges in RDM are to improve data management efficiency and apply analytic capabilities to make use of the data

Who is this Report for:

NelsonHall's Market Assessment for Financial Industry Reference Data Management (RDM) BPO is a comprehensive assessment of Financial Industry RDM BPO operations and vendor offerings and capabilities designed for:

  • Sourcing managers monitoring the capabilities of existing RDM services suppliers and identifying vendor suitability for RFPs
  • RDM BPO vendor marketing, sales and business managers looking to benchmark themselves against their peers
  • Financial analysts and investors specializing in the support services sector

Scope of this Report:

The report provides a comprehensive and objective analysis of Financial Industry RDM BPO operations' offerings, capabilities, and market and financial strength, including:

  • Identification of the industry's strategy, emphases and new developments
  • Breakdowns of revenue and overall revenue growth and profitability trends
  • Analysis of strengths, weaknesses and outlook
  • Analysis of relative strength within each service sector
  • Analysis of offerings by service line across IT outsourcing, BPO and professional services.

Key Findings & Highlights:

Key challenges in RDM are to improve data management efficiency and apply analytic capabilities to make use of the data including:

Account set-up:

  • Legacy platforms are structured in a product centric view, which makes a customer centric data view very difficult to create
  • FIs differentiate themselves, in part, by articulating differing definitions of customers (what constitutes a family, what constitutes a class of customer) and differing customer/product interactions (permissions granted, pricing provided). In an era of consolidation differing definitions must be resolved (if they are understood)
  • Regulations are continuously changing as to what data must be collected for each type of account (AML, KYC, LEI)
  • Cycle times (esp. how long it takes to open an account) impact likelihood of acquiring an account (winning the sale)
  • Customer data is subject to privacy regulations, which vary by market
  • Analytics can provide insight into cross sell opportunities

Securities pricing:

  • Heterogeneous security types have entirely different data architectures, making standardized processing of RD difficult
  • Multiple reporting sources , which are expensive, have conflicting data, which needs to be resolved
  • Lack of benchmarking for many emerging venues/securities
  • Activity spikes at end of day, end of quarter, makes capacity planning and utilization difficult
  • Reporting deadlines makes 24 hour processing critical to reducing TAT
  • Apply analytics to support risk management

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