Revisiting Client Data Challenges and the way ahead
In the wake of multifarious regulatory requirements and stringent risk management processes, Client Data is no longer limited to being an evidence of the client’s existence. Presently, financial institutions see an urgent need for multi-domain synthesis of client data that envelops identification of clients with various address types, country of operations and risk, distinct industry classifications, standard/ risk/ regulatory hierarchies, economic and non-economic control, role, contractual agreements, contacts and servicing managers, product and services, fees and schedules, and the list goes on.
Risk management issues: Organizations fail to aggregate the risk exposure up to the ultimate parent due to unavailability or persistence of erroneous ownership data. The primary challenge is to bring all clients existing in siloes across various systems under one radar for scrutiny. It is also crucial to link clients to the issuers of instruments of interest to achieve the exposure calculation.
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Data quality issues: Basic issues such as duplicate entities, wrong client-account hierarchy, roles, classification codes, irregular linking of agreements etc. are very common.Besides, data validity and timeliness of amendment processing is also a key concern due to the absence of proactive data quality management processes.
Lack of comprehensive view of client relationships: The client relationship view is broadly considered as a sales responsibility and maintained manually. Often seen that it takes months to create a comprehensive list to run a campaign. Also, a 360-degree view of the client are always tentative.
Slow in reaction to regulatory compliance: There are more than 20 regulations in 2017- 2019 that require complex client data management. Certain new regulations require specific entity classifications, ownership and dependency structure. Besides the challenges in sourcing additional data sets, in absence of a common regulatory strategy banks are facing data quality and governance issues.
Difficulty to contain revenue leakage: Federated client onboarding systems, unstructured roles, unsystematic fees and schedule capturing, and maintenance process hinders a clear view of the client billing profiles, leading to revenue leakage.
High total cost of ownership: Client data management is expensive owing to continuity of legacy data, regional data hubs, business-centric client data management systems, numerous onboarding platforms, multi-point data entry and higher dependency on operation processes.
Non-inclusion of non-entity data: Individual clients are typically managed by the Wealth and Asset Management business. Without a combined platform, it is difficult to establish the relationship within and with corporates, as required by certain regulations.
What are we doing wrong?
No holistic approach to maintain synergy between business functions that use the same set of data for different purposes.
Siloed approach to regulations that have need for similar information leads to multiple data hubs and definitions without governance and data consistency.
Lack of participation from the business owners with the data processing group bears on their ability to produce clean and valid data fit for their purpose.
No full-proof data consolidation within legacy systems created through multiple mergers and acquisitions without complete consolidation of business applications.
Lack of single Client Onboarding (COB) system: Due to a number of business units, varied requirements, geographical separation and tight integration with legacy systems, COB consolidation is still a nightmare for many.
Data governance not activated either due to absence of an established CDO office or the activation process is yet to be completed.
Lack of enterprise data strategy, data standards, architecture definitions and integration standards are prominently missing at organization level.
A systematic approach to client data
We need to get answers to the following questions before revisiting client data and strategize next steps
- What is the common definition of client data across the organization?
- Which lines of business(LOBs)require client data?
- Which data set do each of the LOBs require?
- What is the purpose of each data attribute for the business?
- What should be the sourcing strategy?
Considering the answers to the questions, Identify the current systems within the client ecosystem
- Score the level of integration among the systems managing various data domains
- Define data quality requirements alongside the governance team
- Create the expected gap within systems, people and quality
- Build a roadmap to achieve the target state
It is encouraging to see that data owners and senior management have started to strategize the roadmap to attain data maturity. Data vendors are gearing up their data collection and distribution process to support these initiatives, while many fintech companies are offering point solutions to address some of the areas discussed.
About the author
Sukant Kumar Paikray
Global Practice Head – Data Management, Industry Advisory Group, Wipro
Sukant has more than 26 years of experience in finance and technology industries. He was part of thefinancial services industry for 11 years and in 2002, moved to the technology industry to leverage his expertise in various capital market domains. Prior to joining Wipro, Sukant was a domain leader with Oracle Financial Software Services.