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Single View of Customer in Banking

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Single View of Customer in Banking

  1. 1. Single View of Customer in Banking
  2. 2. Deloitte & Touche Rajeev Krishnan, Ashish Mehta, Victor Bocking 2
  3. 3. Table of Contents Introduction ....................................................................................................3 Why do Banks need a Single View of Customer? .................................................4 How do Banks typically approach establishing a Single View of Customer? .............7 What is a Master Data Management (MDM) Solution? ..........................................8 Which MDM Architecture Style is most suited for a Bank?.................................... 10 How can the key challenges in implementing MDM be addressed? ....................... 12 What makes Banking MDM implementations uniquely challenging? ...................... 15 Conclusion .................................................................................................... 16 3
  4. 4. Introduction In the past, the management of a bank’s customer (person/client/counterparty) data was largely at a tactical level driven by the need to comply with KYC, MiFID, AML, Basel II and a host of other regulations. Retail and Commercial banks today have realized that they need to get much more strategic in their use and management of customer data, if they are going to be able to continue to drive growth, improve customer experience, better manage operational risk and exposure, as well as drive operational efficiencies. Although most banks and financial institutions are already moving from a product-centric view to a customer-centric one, they are challenged with incomplete, incorrect, and fragmented customer data. Banks undergoing mergers and acquisitions have experienced additional fragmentation of customer data, as well as increased complexity in their customer platforms that cross lines of business, corporate functions, and geographies. Banks with a fragmented view of customer information face the following challenges: 1. Lower Rate of Revenue Growth – The lack of a 360 view of the customer across channels, across products, across regions and across lines of business results in a barrier to realizing value from customer-centric cross-sell and up- sell initiatives. 2. Higher Operational Risk and Exposure – The difficulty in managing the security and privacy of customer information results in increased reputational risk; an inability to aggregate loan and credit exposure for a customer; as well as inefficient front-line response to service customers and manage risk and fraud 3. Higher Operating Costs – Customer information applications are typically the most expensive applications in the bank. Duplication of functionality and data increases operating costs. 4. Impacts to Customer Experience - Through inconsistent channel support, the inconsistent application of business rules and incomplete data synchronization, customer experience is getting negatively impacted. For most banks, successfully achieving a Single View of Customer is not a simple task and will require both strategic and tactical approaches. Tactical remediation approaches to improve customer data and applications can help to deliver value in the short term, but a structured, well conceived solution strategy (people, process, 4
  5. 5. technology), combined with a solid business case that executives will support and fund is required to deliver sustained long term value. The purpose of this paper is to present these approaches. Why do Banks need a Single View of Customer? Most Banks have business growth strategies that require an integrated view of the customer to realize. For example, today most banks, irrespective of their market segments are focused on creating customer intimacy and moving towards relationship based banking. However, at the same time, increased M&A activities further contribute to the challenges of achieving this by increasing the fragmentation of customer data. It is not uncommon for banks to have their customer information in 10’s of databases and 100’s of versions of 1000’s of spreadsheets in each line of business. Clearly an approach is needed to manage this. The three major business drivers for a Single View of Customer are: 1. Revenue Growth — Examples include increasing cross-sell and up-sell rates, and increasing customer retention through better customer experience. 2. Cost Reduction — Examples include reducing reconciliation requirements, improving the speed and effectiveness of processes such as order to cash, reducing the need to maintain multiple redundant customer data stores, etc. 3. Risk Management — Examples include compliance with cross-industry or industry-specific compliance and risk management regulations, such as Know Your Customer and Basel II. 5
  6. 6. 1. Revenue Growth and Customer Experience Banks have long recognized that customer-centric solutions help to achieve cross- functional business imperatives that are aimed at bolstering customer profitability. Banks’ reliance on electronic client information has generally faced challenges with scalability and integration between different line of business applications such as retail, wealth, credit card, insurance and private banking. Achieving a holistic view of customer information generally takes one of the following forms: • Single View of Customer - Create “golden” customer records by reconciling names, addresses, emails, phone numbers, and other data from disparate sources for a single view of a customer. • 360-degree customer view - Expanding the Single View of Customer to include the customer’s products across lines of business (checking, mortgage, IRA, auto loan, etc.) • Extended customer view - Expanding the 360-degree customer view to reflect the bank customer’s network of people, business, and product relationships can help customer teams pursue long-term, multi-generational value and support wealth management growth strategies. Case Study: Banks are turning to technology solutions, such as Master Data Management (MDM) to help achieve a single view of customer. One of the largest banks in Europe 6
  7. 7. integrated its centralized customer information file (CIF) with a transaction MDM customer hub to support the real time approval of wealth management and credit product sales applications. The implementation not only created additional sales opportunities by providing information from all lines of business for up selling and cross selling their products, but also helped with choosing and recommending the right product for their customer, resulting in higher customer satisfaction. This solution also helped reduce operational costs for application processing, and reduced credit risk by providing a unified view of the customer’s relationship with the bank. Consistent and improved customer experience has been one of the greatest challenges for some banks due to increased M&A activities. Mergers and acquisition activities have increased substantially in the last decade, especially during the global recession of 2008. Case Study: One of the largest banks in North America had over 90 acquisitions resulting in the increase of their assets to 100’s of billions of dollars. The acquired banks had different business processes, data governance models, and technologies, making it very difficult to produce an integrated customer view. To address these challenges the bank chose a MDM solution to help reconcile customer master data across the acquired entities, thereby realizing a single integrated view of customer. 2. Cost Reduction and Operational Efficiency Banks are continually facing challenges with regards to reducing operational costs, and costs associated with addressing regulatory requirements. Achieving a Single View of Customer can help contain these costs. It is not uncommon for a single business unit to use 10’s of applications, and 100’s of spreadsheets to: reconcile regulatory compliance requirements, process credit applications, for internal or external reporting and to manage credit risk data. Enabling a single view of the customer can reduce operational costs for application processing. Case Study: A leading Canadian bank had major challenges in the approval of new applications in their wealth management group even if the applicant was an existing customer in good standing with the bank. The bank had more than 40 dispersed applications across its various business units and numerous others from where customer information had to be reconciled before an application could be approved and the 7
  8. 8. client on-boarded. The bank decided that a MDM strategy was critical to improving operational efficiency in this area by creating a single view of customer. Some of the areas in which costs savings can be achieved through a Single View of Customer are – • Reduced Failures or Delays for Orders, Trades, Confirmations, Settlements and Payments through elimination of incorrect account – client linkages • Increased Straight Through Processing (STP) levels and Program (algorithmic) Trading – this is achieved in combination with an instrument / security (product) master, counterparty master • Timely and Accurate Operations and Financial Reporting by reducing reconciliation effort between financial ledgers and client master • Elimination of incorrect or duplicate mailings 3. Risk Management and Regulatory Compliance Credit Risk Management and Regulatory Compliance have been one of the earliest drivers for achieving a Single View of Customer for banks. Case Study: A leading European Bank with global operations implemented MDM in record time to build a central credit risk reference data repository from a set of diverse databases and excel spreadsheets. The bank also used MDM to secure their Data Governance processes across the organization, resulting in better compliance to both SOX and BASEL II requirements. Compliance was achieved through MDM by its ability to enforce rights and security rules, data governance rules, create secure audit trails from multiple sources, and the ability to manage and control multiple versions of master records. How do Banks typically approach establishing a Single View of Customer? • Ignore the Problem – Yes, surprisingly enough, due to the complexity and large volume of data involved in client data remediation, some firms take the approach of side-swiping the entire issue. The cost of not addressing this problem is huge but often tactical band-aid solutions are used to provide temporary relief. 8
  9. 9. • Employ a Master Data Management (MDM) solution for customer data integration – This method is typically the optimum solution and is described in the following sections. Options include either a custom built solution or a packaged MDM solution. Typical issues with a custom built application include higher implementation and maintenance costs, hard to code complex probabilistic and deterministic matching, building the relationships and hierarchies, limitation in business rules implementation, and insufficient data governance user interfaces and workflows. Currently, there are many robust and cost effective MDM platforms on the market that can provide a better alternative to a custom built solution. A custom built MDM solution should only be pursued if the business requirements of the bank are validated to be so unique that none of the MDM vendors support them. What is a Master Data Management (MDM) Solution? Master Data Management (MDM) spans all organizational business processes and application systems, enabling the ability to create, store, maintain, exchange and synchronize a consistent, accurate and timely “system of record” for core business entities. When the business entity being managed is Customer, it is also referred to as Customer Data Integration (CDI). A MDM solution should have the following core capabilities: • Identify a customer across multiple sources, by matching various identifying attributes of a customer such as name, address, phone, email, SSN, etc. • Probabilistic (fuzzy) matching – consider changes in attribute values due to spelling errors (Main St. – Mai St.), phonetic differences (Tami - Tammy), nicknames (Robert – Bob), etc. • Deterministic matching – match based on pre-defined rules that are based on data profiling and source system specific information. • Assign suspect customer record matches to a data steward for manual review. • Construct a golden view of the customer by assembling attributes from the matched source records according to pre-defined rules such as trusted source, most current attribute, etc. Publish golden view changes to consuming systems. • Identify, construct and manage customer hierarchies and relationships, with the help of third party reference data. 9
  10. 10. Risk Systems KYC Systems Legacy Customer Master Financial Systems Account Opening Systems CDI Master Hub ETL Batch Extracts Security & Visibility Web Services EAI/EII Standardization Algorithms Match&Link Algorithms Hierarchy Mapping External Reference Data (D&B, Bloomberg Acxiom, etc) MDM Solution Components for Banking 1. MDM or CDI Hub - Data standardization algorithms, Core matching (probabilistic algorithms / deterministic rules), Golden copy rules assignment, Hub data model and reporting structures. 2. Task management system for manually linking and unlinking customer records from same/multiple systems. 3. Identity management User Interface (UI) for displaying golden view and detailed views. Hierarchy & Relationship management UI for viewing, comparing and remediating multiple hierarchies and relationships. 4. Batch and Real-Time Interfaces to receive source data and distribute mastered data – ETL or EAI, Adaptors for various messaging standards, SOA Compliancy, API’s, Web Services, etc. 5. Security and Visibility Components (the chosen MDM system must be able to integrate well with the firm’s existing security models and policies) 10
  11. 11. A MDM solution may not include the following tools and capabilities which are however key to a successful MDM implementation, and thus they may need to be sourced separately: • Data Quality – Profiling, Cleansing and Transformation (ETL) • Data Enrichment – e.g. Address Validation, Dun & Bradstreet, Bloomberg, etc • Rules Engine • Workflow Many leading vendors of MDM solutions offer some of these as separate products or provide adaptors to integrate with other vendors offering these capabilities. The integration effort required for these tools to interact with the MDM solution needs to be carefully evaluated before a sourcing decision is made. Which MDM Architecture Style is most suited for a Bank? There are different architectural patterns for implementing a MDM solution and the appropriate style for a bank depends on its business objectives and its existing technology maturity, which is required to support the solution. In most cases, the Coexistence (Hybrid) style is appropriate for large banks that are embarking on the MDM journey. As the bank gains more MDM maturity, it can transition to a Centralized (Transaction) style MDM solution. 11
  12. 12. # MDM Architecture Style Adoption by Banks 1 Consolidation Style - This style extracts a copy of the master data from the MDM Hub, and stores it in a data warehouse for reporting and analysis purposes. This style does not improve the integrity of master data in the operational systems. Banks that adopt this style are usually ones that want to understand the benefits of MDM in an analytical environment before they make the investment to integrate it with their operational systems. 2 Registry Style – This style maintains only cross-referenced links to multiple data sources by means of keys or pointers in the hub. All other data attributes should be retrieved from the relevant source systems using these keys. It can be deployed fairly quickly, but transitioning into more mature styles would be difficult due to significant architectural differences. Most banks see little value in adopting this architecture style, but sometimes adopt them for a quick proof of concept. 3 Coexistence (Hybrid) Style – This style stores a physical copy of the customer master data in the MDM hub, and synchronizes it with multiple operational systems at varying latencies. It is easier to transition from this style to the centralized style, if required. A large number of banks adopt this architecture style since it accommodates different operational systems that have variances in their ability to consume customer master data. 4 Centralized (Transaction) Style – This style federates up-to-date customer transaction data (Create, Read, Update, and Delete activities) in real time from multiple channels, and unifies it with the most reliable master reference and relationship data within the hub. This style delivers the most timely, unified views of the customer to supporting applications. This is a very invasive approach given that various operational systems in a bank have to consume customer master data directly from the same hub. For that reason, it is also hard to implement and is attempted only by bank’s that have reached a high maturity level in MDM and data governance. 12
  13. 13. How can the key challenges in implementing MDM be addressed? From a strategic perspective the following are some of the key challenges banks face in implementing MDM, and recommended approaches to addressing those challenges: Challenge Recommended Approach Executive Ownership and Stakeholder Buy-in Participating is not the same as buying in - Key stakeholder groups need to be determined and senior executive leadership need to be active, engaged participants. Successful large scale programs have a clear Executive Champion • Key Steering and Working Committee participants must set aside the appropriate amount of time and be actively engaged in the process • Development of a steering committee with an identified chair that is cross functional • Hold Steering committee responsible and accountable for key decisions throughout the project (Target future state, roadmap initiatives, and business case estimates) Fact-Based Business Case All spreadsheets are not equal - Many business cases are based on hypothetical costs and benefits. Costs are typically estimated at a deeper level of detail than benefits. Frequently, business cases are build on fact based components validated with real world experience • Leverage multiple sources for calculation of benefits – such as the results achieved by peer organizations, case studies and external benchmarks from other Financial Institutions • Validation and sign off from working teams throughout the process Practical Implementation Roadmap Big bang does not typically deliver explosive results - The complexity of large scale Customer Information Implementations often results in multi-year big bang implementations before business value is delivered. These result in organizations losing momentum and often funding for the initiatives • Pragmatic and staged roadmap approach that identifies quick wins an self funded projects • Continued participation of the steering committee through business case and implementation Integrated Solution Technology by itself will not deliver the results - Programs that focus exclusively on delivering technology do not achieve sustainable business results. Focusing on addressing the required organizational elements (new roles, responsibilities, interaction models) and business process changes that are required help ensure that the technology will enable business value • Dedicated stream of work focused on process will identify necessary changes that have to align with the technology • Cross functional working teams will ensure that all relevant processes are addressed Proactive Data Governance and Management Plan and Pray is not an option - Trust but Verify - Many programs make dramatically incorrect assumptions around the current quality of existing customer information. Planning for data profiling, detailed data quality assessments in the early stages of • Implementation plan should include initial early data assessment of core data stores • Extensive data profiling and data quality assessments • Proof of concept for data migration efforts 13
  14. 14. the implementation roadmap helps avoid significant data conversion overruns. Many programs leave the assessment of data quality and subsequent conversion too late in the lifecycle From a business case perspective, realistic benefit calculation is critical to its validity and in achieving executive buy-in. The following are some sample benefits that have been achieved by Banks in recent years and can be used in building the business case: Client Solution Benefit Identified Global Bank Risk-scoring improvements - These enable a relationship approach to credit management, thus enabling a customer centric view across businesses and products (restrict good accounts if customer is delinquent on another account or other behavioral activities such as excessive cash advances) $120 million (EBIT) over 5 years in hard dollar benefits identified to manage customer relationships across Commercial Lending platforms (reduced credit losses) Global Bank Speed up cycle time - By capturing and scoring behavior data nearer to the event, highly targeted offers can be made – across any channel – in a matter of days rather than months $26 million (EBIT) / year from incremental lift in overall response rate and improved overall retention rate Top-10 US Retail Bank Customer centricity - Implemented integrated enterprise-wide data model, information hub, BI tools, contact and client management systems, and messaging broker to enable enterprise-wide customer management Computing and operations efficiencies funded 64% of total incremental investments with a 285% ROI Global Bank Reduced IT costs - Consolidation of customer information into a single repository meant that certain O&T enhancements must only be made into a single system. This efficiency will enable O&T to deliver one incremental project per release $14 million (EBIT) over 3 years in reduced project costs resulting from a central data repository Global Bank Cross-sell and reduced operational costs - Use customer-centric data to increase online market share and move customers to a more electronic environment (i.e. payments, statements, etc.) $38 million (EBIT) over 3 years in hard dollar benefits. New business and lower operational costs associated with migrating customer to paperless processes When implementing a MDM solution, it is important to keep it first and foremost a business focused project. This is because the majority of benefits will come from: 14
  15. 15. cross-sell / up-sell revenue lift, significantly improved risk management capability, and cost savings through operational efficiencies. Also, MDM program governance that extends beyond Data Governance is critical because of the enterprise-wide implications and operational governance that will be required to realize the full benefits of this initiative. Without it, the capabilities built may not be converted to business results due to lack of process re-engineering and integration into customer facing activities. Now, from a tactical perspective, the following are some of the roots causes of data quality problems with customer data, which can affect the success of an MDM program, which can be addressed through application controls, improved customer data governance processes, as well as staff training and education: • Lack of integrated systems and the complexity required to on-board a client: There is a lack of ability to be able to look up an existing client across channels, and there is a tendency to enter dummy data when client vetting and Know Your Customer (KYC) activities are too complex or take too long; and too often the dummy data is not corrected, when the right data is captured later. Incorrect data then propagates downstream to the transaction processing environment. This negatively impacts control functions in the bank such as credit, finance, compliance and tax. Application controls with systematic edits and validation at the point of entry can help to address this. • Indifference towards “getting data right the first time” in the front office, at the time of data capture which stems from an apathetic attitude by the front office that the middle / back office would capture the error and remediate it. To address this issue, a global bank even took the approach of tying a part of their front line staff bonuses to data quality performance. • Lack of defined data ownership and stewardship – Many data attributes used by more than one function suffer from a lack of ownership and stewardship. There might be inherent variances in the way the data attribute and its usage is defined. For e.g. the credit risk function might be using and changing certain client attributes based on their definitions, without comprehending the 15
  16. 16. effects it might have on financial reporting. Improved data governance processes help to address this. • Unavailability of an external hierarchy which can be used as a remediation reference – Client data quality gets eroded all the time with new data coming in from multiple sources, which might have new or changed processes for data capture. The typical way to address this is to compare the bank’s internal customer data with external sources such as the Postal Service, Dun & Bradstreet, Acxiom, Bloomberg, etc. • Lack of data quality performance metrics – Without defining data quality performance metrics; it is hard to see if data remediation efforts are paying off. Key Performance Indicator (KPI) Dashboards with drill-down metrics are a useful tool in monitoring and tracking of data quality trends and performance levels. What makes Banking MDM implementations uniquely challenging? There are several unique challenges in implementing MDM at banks, and it would be prudent for banks to recognize them before they embark on their MDM journey. These challenges need to be considered not only when selecting the MDM solution, but also in adopting the methodology used for the implementation. • Dealing with Big Data - Most banks have a large customer base and experience frequent changes to this data. These changes could be core customer data (e.g. name, address, phone) or secondary customer data (e.g. products owned, interactions). An increasing number of new channels for banking and interaction management (e.g. mobile devices, social networking sites) have added to this data explosion. MDM solutions have to scale effectively to deal with these large volumes of data and real-time transactional requirements. • Rapid M&A Activity - Due to the highly dynamic nature of corporate actions in this industry, MDM solutions need to be highly flexible to accommodate 16
  17. 17. various new sources and consumers of customer data, as well as the associated challenges already mentioned. • Complex Customer Relationships and Hierarchies - In banking, it is just not enough to identify who your customers are and what products they own, but also to understand the relationships they have with other customers from the perspective of a household, guarantor, etc. When the bank’s customers are institutions as opposed to individuals, it poses the challenge of maintaining corporate hierarchies. This is because understanding the ultimate parent entity and all its subsidiaries across the globe are critical to accurately assessing risk as well estimating the value of a customer. • Need for Customer-Product Linkage - In addition to core customer data, most banks find it desirable to maintain high level product data linked to its customer data within the MDM Hub, as opposed to creating that linkage within downstream analytical data stores such as a data warehouse. This linkage is usually required for accessing the 360 degree profile view of the customer, including what products are owned and what the customer’s contact preferences are. Maintaining it within the MDM Hub provides an instant and one-stop access for applications interacting directly with the Hub (e.g. contact center and marketing applications). To retrieve more product- centric detail data, they can use smart navigation techniques to drill into the product systems. • Need for High-Accuracy Solutions - When it comes to customer matching for supporting a banking platform, the level of accuracy demanded from an MDM solution’s core matching process should be higher than that of less demanding applications such as for a marketing campaign. Conclusion Banks that have recognized the need to establish a Single View of the Customer to drive revenue growth and manage risk are turning towards MDM as an enabling technology. This is becoming more important with the increased competitive and regulatory environment. For banks to succeed, they need to ensure that they have a well defined MDM strategy and practical implementation plan which is supported by a clear business case endorsed by key executive sponsors. 17

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