Federated data governance and standardized taxonomy for BCBS 239 compliance
Client: A Japanese financial holding firm that provides services to individuals, institutions, and government customers across the globe
Industry: Capital Markets
Business need addressed: BCBS compliance with enhanced data aggregation capabilities to meet the demands of high-frequency risk reporting, improve the data quality of the reports, and enable the company measure its performance against its risk tolerance while reducing the overall cost of managing and governing data
Genpact solution: Established a central data management function, standardized data taxonomy and developed a federated data governance operating model, which will provide consistent, validated, and well governed data for reference, trade, and other domains
Business impact: Enhanced BCBS 239 compliance will enable the financial holding firm to lower its total cost of ownership while reducing error rates
A leading Japanese financial holding firm needs to become BCBS 239 compliant and transform its risk data management and reporting. The company requires nimble data architecture and IT infrastructure that can enhance its risk data aggregation capabilities and risk reporting practices. Genpact is implementing a federated data governance operating model and a standardized data taxonomy to help risk-proof the business, optimize cost, and improve customer satisfaction.
Like most of the global banks, the financial holding firm was overwhelmed by the sheer volume of regulation coming their way. With the introduction of BCBS 239, which is focused on effective risk data aggregation and reporting, the company was required to transform its risk data management processes, which were suboptimal. The inefficient data management process had costly implications:
Approaching BCBS 239 deadline
The financial holding firm, a Domestic Systemically Important Bank (D-SIB), needs to be BCBS 239 compliant by March 2017.
Difficulty in harmonizing massive volumes of data
Over the years, the company has collected enormous volumes of data, which were not optimally managed and therefore hindered the company’s ability to uncover hidden risks. Moreover, process-related, reporting and technological barriers combined with data-quality issues made it difficult to bring the risk and financial data sets together.
The company needed to align its IT strategy with BCBS239 requirement for long term gains. It was an overwhelming task that involved coordinating across risk, finance, trade, event, and operations and each function had its own requirements, issues, and objectives to follow.
Complex and ambiguous individual accountability
Lack of standardized data taxonomy was resulting in ambiguous and complex individual approval system and suboptimal performance. The company needed to improve individual accountability in order to adhere to the rules laid out by the Financial Conduct Authority (FCA) and Prudential Regulation Authority (PRA).
Standardizing data taxonomy and implementing a uniform data architecture and governance structure, across the business, was an immense challenge facing the company.
- Dealing with the regulatory compliance alone was costing the company 70% of its “change the business” budget
- The inefficient data management process were leading to financial losses, higher costs, and, low customer satisfaction
Genpact is leveraging its risk management and data modeling capabilities, as well as its deep-seated knowledge of industry best practices, to develop and implement an operational environment that can meet the objectives of BCBS 239 and enable data governance at the enterprise level.
A data governance operating model has been proposed to establish a central data management function and a federated data governance structure.
New data governance operating model will help the financial holding firm achieve
- Standardized data taxonomy
- Agile and scalable data architecture to manage future growth
- Facilitate future regulation requirements with minimum impact on the existing systems and business operations
- Uniform and normalized data
- Better data governance, providing regular insights to the board and senior management
- Reduce downstream system reconciliation
The engagement started with an assessment phase in early 2015, which encompassed detailed functional scope document (FSD) reviews by key stakeholders across IT, change management, risk, credit, and operations, and led to the creation of a technical design. Genpact will consolidate the disparate book structures existing across the financial holding firm to facilitate global risk data aggregation. Reference data system and its distribution will be centralized to enable enterprise-wide integration. Genpact is also developing an application to create and maintain reference data books hierarchy to facilitate seamless information exchange and interpretation across the firm.
Genpact is leveraging its offshore teams for development, implementation, and user acceptance testing. These teams will conduct multiple user acceptance testing (UAT) preparatory sessions to secure user confidence before the actual UAT, which is planned for May 2016. The UAT will be rolled out in several phases covering multiple regions: Europe-Middle East-Africa; Asia (excluding Japan); US; and Japan.
The data management process transformation will help the financial holding firm become BCBS 239 compliant and achieve competitive advantage. The company will realize the following benefits:
Improved decision-making throughout the organization
The ability to provide accurate and complete information to the right people at the right time will enable the company to report key information so that senior management can make well-informed decisions based on the insights.
Improved ability to identify risk
Strong risk data aggregation capabilities will help identify, monitor, and manage risks. The risk management reports will reflect the risks in a reliable way.
Improved understanding of data definition and traceability
Multidomain data management capability, including an enterprise data dictionary and business glossary, will enable standard data definitions, help reprocess bad data, and boost good data sources.
The data management process transformation will help reduce the probability and severity of lossesresulting from suboptimal risk management.
Improved capability to fix data-quality issues at source
By providing consistent, validated, well-formed, and well-governed data for reference, trade and analytical results data domains.