Leveraging Data in Financial Services to Meet Regulatory Requirements and Create Competitive Advantage
0 likes1,264 views
The document discusses how financial institutions can turn regulatory compliance data into opportunities for competitive advantage. It provides examples of how anti-money laundering (AML) and customer data used for compliance can also power initiatives like cross-selling, improving the customer experience, and strategic capital planning. The document recommends a balanced approach between meeting regulatory requirements and building a flexible data architecture that allows data to be reused across business units.
Leveraging Data in Financial Services to Meet Regulatory Requirements and Create Competitive Advantage
- 15.
- 16. Turn Enterprise Data Challenges into Opportunities Meet Regulatory Standards and Leverage Compliance Data for a Competitive Edge June 2015
- 17. 17 Agenda • Introduction • Industry Data Challenges • AML Data as an example • Data Architecture Design & Implementation Approach • Beyond Compliance - what is the value proposition?
- 18. 18 Introduction Regulators have increased scrutiny on the financial services industry-fines have been in the millions The financial services industry is focusing considerable time and expense on capturing data to meet regulatory requirements Clients have often addressed these data challenges as one-off projects with the objective to comply with a single regulation We recommend implementation of a data program focused on a flexible data architecture that can be leveraged for revenue and competitive advantage
- 19. 19 Industry Data Challenges Regulatory burden – regulations will change and are unpredictable Disparate data – fragment and functional silos – Data quality collection from different system results in different levels of quality – Inconsistent and duplicate data across source systems – SME domain knowledge is not also available Limited Master Data – Lack full understanding of client relationships – Unable to report financials by ultimate parent (top customers) – Unable to report financials by customer – Limited ability to identify cross-sell / up-sell opportunities Inefficiencies in risk management/reporting Inability to view complete risk exposure Structured and unstructured data makes normalization difficult Challenges common to everybody in the industry
- 20. 20 Anti Money Laundering (AML) Data an Example
- 21. 21 Anti Money Laundering (AML) Data an Example Disparate Data - Inconsistent data • Different payment formats in applications performing the same function such as US ACH, SEPA, US Domestic (Fedwire), and International high value EFTs result in the need to customize ETL programs that write data to AML monitoring applications • Global/Regional ATM and non-standard branch operation procedures result in difficulty acquiring data required • This has resulted in our client missing critical location, reference, and party information AML Data Programs • Compliance programs focus on the specific data required by a tool to perform monitoring and case management as opposed to the holistic use of data The data requirements to comply with AML monitoring regulations is extensive. Firms often focus on the data required by the AML tool.
- 22. 22 Solution Implementation Document Data Requirements • Document business requirements to capture the data required to ensure regulatory compliance • Utilize the current state of AML data and document the functional mapping requirements to accurately place data in the AML monitoring record Develop Roadmap to Accumulate Required Data • Develop a roadmap to acquire critical data in order to ensure regulatory compliance • Utilize the roadmap to design a data architecture focused regulatory compliance and data reuse The investment in AML compliance programs are significant. The program’s focus should be a balance between meeting regulatory compliance and developing a data architecture that allows for re-use of the data
- 23. 23 Value of a Roadmap Roadmap Outcomes Project Execution Plans with Steps & Milestones Execution Options and Recommendations Project Budgets and Resource Plans Project Success Criteria System Tool Strategy Business Goals User Needs Regulatory/ Compliance Project Budget Vendor Management Technology Infrastructure Roadmap Drivers Roadmap Goals Retention of Institutional Knowledge Resource Allocation & Utilization Organizational Change Project Prioritization Project Rationalization Program Management Office & Governance A roadmap will provide a firm information on the prioritization, business impact, process and technology dependencies. The outcomes are a clear project direction and project portfolios that are supported by business and technology management.
- 24. 24 Design and Implementation Approach
- 25. 25 Data Strategy Building Blocks Building Blocks To High Quality Enterprise Data Governance & Stewardship Metrics Architecture Strategy A sound Data Strategy is enabled by a clearly defined and effective Governance and Stewardship structure, Metrics to monitor and measure data quality, and Conceptual Architecture diagrams that senior business staff can leverage to make informed decisions. A clear mandate from senior executives to execute on a strategy to achieve and maintain high quality enterprise data is a critical success factor. Technology systems and interfaces supporting enterprise processes Measurements to baseline data quality and monitor improvement Policies, procedures, and operating model to manage and execute Data strategy aligned to business strategy including mandate for high quality data
- 26. 26 Governance and Stewardship Best Practices Discover Data to be Governed •Collect data across LOBs •Discover Business and Tech sources •Merge and Validate Standardize Business Context •Data Dictionary •Define Data Elements •Define Values and Aliases •Prioritize Data Create a Logical Data Model •Conceptual Model •Illustrate Business Data •Illustrate Relationships Define Data Rules •Define Relationships •Define Constraint Rules •Define Rule Exceptions •Define Derivation Rules Establish Source and Users •Locate Data Sources •Prioritize & Select Sources •Locate Users •Know how users use data Cleanse, Maintain and Measure •One-time Data cleansing •Establish Quality Maintenance Practices •Monitor Quality for Adherence Governance Structure & Preparation Goals & Principles Roles and Responsibilities Process Our approach focuses on the following key areas of our Data Governance and Stewardship Framework EstablishGovernSteward Metrics Data Quality Stewardship Metadata Model Definition
- 27. 27 Next Generation Enterprise Data Architecture
- 28. 28 Leveraging Data for Revenue and Competitive Advantage
- 29. 29 Beyond Compliance – What is the Value Proposition? • To achieve compliance there is a minimum standard: - Data governance, integrity; repeatable, automated processes • To maintain compliance – pursuit must be strategic, sustainable and incorporated into the culture • The Cost of Compliance drives consideration to exit business or product lines, increases total cost of ownership and may restrict acquisition • Turn Data into a Profit Center – Engage the Business or Front-Office Stakeholders – Enable the Call Center to support and grow the relationship – Develop analytics and reports for broad usage
- 30. 30 Beyond Compliance – Cases and Opportunities Customer Data: Master Customer Data required for KYC Compliance: Party data allows compliance with AML regulations, suitability, Cross-Border and FACTA Opportunity: Improve cross-selling, total view of the relationship; increase customer satisfaction. A single customer golden data source may reduce breaks. Transaction Data: Client and Employee Surveillance Compliance: Aggregated transaction data may identify inconsistent or unsuitable activity for the customer segment. Identify activity out of the norm for an employee’s job type Opportunity: Bring the client advisor or marketing into the “case”. Improve revenue and customer satisfaction by addressing a change in customer needs Capital Planning: Projected Revenue & Risk for CCAR/DFAST Compliance: Enterprise business, finance, risk data to ensure management understanding and sufficient capital levels under stressed conditions Opportunity: Passing Fed reporting requires high-quality unified reproducible enterprise data. Leverage stress/scenario data for strategic decisions
- 31. 31 Key Considerations to Leverage Compliance Data • Identify golden source as single truth – reduce redundancy -> increase consistency • Fix “dirty data” at the source. Define rules to detect and prevent • Globalize toward enterprise data architecture/dictionary • Communicate data standards to increase adoption • Shorten project lifecycle while driving owner-operated reporting • Push data visualization and self-reporting tools to users • Track usage and data capitalization (ROI)