How Kenway Consulting improved risk rating and regulatory compliance processes through an enterprise-wide data governance operating structure
Industry: Financial services
Solution: Data governance operating structure
Client: Fortune 500 global financial services provider
A global financial services provider lacked visibility into their client risk rating due to inconsistent client records. The organization had incomplete and disjointed client information across their customer relationship management (CRM) system and their separate banking service platforms (e.g. wealth management, asset management, tax, etc.). Due to these shortcomings, the organization was unable to accurately aggregate client holdings, understand risk exposure at a client or portfolio level, or identify potential compliance risks. Compounding the operational challenges this posed was impending regulation the financial services provider was subject to federal anti-money laundering (AML) and know-your-client (KYC) regulations that would also require standardized and consistent client information. To address these issues, Kenway collaborated with the client on an enterprise-wide data governance framework to properly oversee the creation, ingestion, maintenance, and consumption of its client information.
Data governance consists of the policies & standards, procedures, and resource roles & responsibilities necessary to ensure that an organization's data is accurate, timely and of high quality.
Policies & standards: Clearly defined rules that dictate how data must be entered, transformed, and connected
Procedures: Actions required to cleanse historical data, update systems, and deploy operational processes to adhere to policies
Roles & responsibilities: Resource alignment and management frameworks to provide sponsorship, direction, and operational support for data governance procedures
A data governance program enforces the integration of company strategy, standards, policies, and communication.
The lack of an effective data governance framework around client data resulted in incomplete risk ratings and the inability to meet upcoming regulatory compliance requirements.
Inconsistent & incomplete client data
Historically, the financial services client had leveraged separate systems to address individual banking services. Each system had different required fields, different definitions of a client, and did not share unique identifiers to match data across systems. The organization's inability to connect client records impeded their ability to accurately aggregate holdings across products, allocate revenue and profitability, and, ultimately, understand their risk exposure at individual client and portfolio levels.
In addition, these same shortcomings in client data governance across the organization's systems made it difficult to comply with KYC regulations to verify customer identities, and with AML regulations to ensure that their credit and deposit activity did not aid money laundering. Without a consistent, holistic view of customer information, these reports would be both inaccurate and time-consuming to generate.
In order to build a data governance operating structure, Kenway guided the client through their data governance methodology to design, instantiate and operationalize organizational data governance.
In order to properly perform the client risk rating process, regulators require specific data with precise definitions. Through a series of workshops, interviews, data analysis investigations, and data journey mappings from creation to user consumption, Kenway guided stakeholders across the organization to determine the underlying issues preventing them from effectively complying with data regulations.
Through this analysis, Kenway found that the root cause of the organization's client data issues was a result of multiple versions and definitions of "party" data stored in its CRM and account opening systems. These systems served a diverse set of use cases from customer relationship management to account servicing. As each business unit employed its unique requirements to these client- and account-centric systems, several unique views of "client" resulted. The fragmented requirements around client data meant that:
From a data governance perspective, this required the organization to determine the operating model to uniquely identify clients and deploy a unique identifier policy. Kenway worked with the organization to identify a definition and policy for legal entity, along with the associated requirements for source systems enhancements. Additionally, Kenway determined that a centralized data store of mastered client data had to be created to address these gaps.
Define the policies needed to achieve the required outcomes
Kenway developed a legal entity identifier policy for the client through collaboration with a variety of stakeholders across the organization. This policy defined the unique identifiers required for each legal entity record in the account opening systems and the CRM platform, creating consistency and alignment for client information across all of the platforms. This provided the necessary standards to ensure that client records had complete and accurate sets of data. Additionally, this enabled the matching and merging of legal entity records across all platforms, as each record included the information required to map legal entity records across systems.
Implement a data governance operating structure
Once the legal entity identifier policy was finalized, Kenway defined the necessary procedures to ensure proper implementation and long-term success. Kenway and client stakeholders mapped out the plan to remediate existing client data issues, and designed process and technology changes to proactively address future data needs. This covered understanding processes surrounding the creation of client data to the definition of legal entity roles and relationships, through the high-level design of a client and account data hub that enabled downstream systems to accurately utilize the mastered client data. From a functional perspective, Kenway defined these activities and the roles and responsibilities needed to properly enter, review and modify data throughout its lifecycle, which provided clear accountability for ongoing data quality.
Our deliverables
The Master Data Management solution
Kenway helped the client develop the business case to implement a master data management (MDM) solution which would manage key client and account data in the required form, with the required attribution - in order to satisfy the client's regulatory requirements. The business case highlighted that the MDM solution would deliver significant return on investment by streamlining processes with a centralized, accurate and complete set of client and account data. Furthermore, the establishment of a data stewardship function to profile and cleanse data on an ongoing basis ensured that these efficiencies would permeate into the future. This initiative relied on the policies and procedures aligned with the data governance operating structure, using it as the foundation for the technical and process modifications to actively manage data moving forward.
Master data management benefits
How can Kenway help your organization?
Kenway focuses on the people, processes and technology surrounding your data ecosystem to create the best solution for your organization. Kenway's capabilities focus on defining and implementing processes to help you govern your data from the point of origin to the point of consumption, and to the point of retirement. By taking this approach, we believe that data can be managed in a way that minimizes cost while maximizing the organization's ability to ensure data quality.
Kenway's data governance framework will:
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