With the financial services industry facing mounting disruption from Fintech startups, a single source of data for finance, risk and regulatory reporting can dramatically improve firms’ insight, ultimately delivering meaningful change and driving profitability. Writing for FinTech Finance, Richard Reeves, vice president of Strategy for OneSumX at Wolters Kluwer, and Richard Bennett, vice president, Regulatory Reporting for EMEA at Wolters Kluwer, examine the quest for a single source of data.
Ask any financial institution what one of its biggest challenges is and the answer will often be the same. They now require access to unprecedented volumes of data to meet growing demands for transparency and integrity across all their business activities. Operational needs and regulatory requirements are driving rigorous data sourcing, collection and integration efforts that are designed to reap substantial operational benefits in terms of fewer manual tasks, reduced duplication of data and improved reconciliation rates.
Regulators, however, have different views of risk weightings, and afford varying levels of discretion on credit, market and other risk types. And the sheer variety of regulations firms are facing also compounds the problem. The Basel Committee for Banking Supervision’s set of principles for risk aggregation (BCBS 239), Basel’s Fundamental Review of the Trading Book (FRTB) and other regulations impacting accounting (such as BCBS 311), liquidity and capital allocations are posing reporting challenges for banks across multiple functional areas. These regulations and others like them require action from the risk department, but also the finance and compliance areas of the business.
As they strive to meet the growing requirements banks actually require a discrete approach to data sourcing and integration for each regulator. This situation raises the complexity and cost of regulatory compliance across the board, and raises the specter of inconsistent data underpinning reporting to different regulators. This points to the need for the much-talked about ‘single version of the truth’. Achieving this requires consistent and flexible access to data in support of the finance, risk and regulatory reporting functions.
So far, the absence of a unified, consistent data model that underpins all data used for regulatory reporting purposes makes it difficult to achieve consistency in reporting, and in supply of data to disparate business lines. By understanding their data and quickly accessing required information, ensuring consistency of assumptions, structure and calculations, firms can shift toward a common analysis approach, ultimately creating a ‘single version of the truth’ across the enterprise. This, in particular, will be key for requirements related to risk data aggregation – such as BCBS 239.
Financial institutions can also establish a direct link between the regulatory requirement and business data, by ensuring that reporting accurately reflects their business activities. A finance, risk and regulatory hub, for example gives institutions more control over different data sets, opening the door for more detailed and complete performance reporting.
In fact, as we note in our new white paper, “In Search of a Single Version of the Truth: Adopting a Universal Data Model” institutions should now be encouraged to put in place an enterprise-wide data management infrastructure that’s capable of meeting the needs externally by the regulators, and internally, across the entire enterprise. By combining deep industry expertise and best practice, a standardized approach can provide a blueprint for firms striving to institute wide data consistency, giving both business and compliance a clearer view of finance, risk and performance both from a business and a regulatory standpoint. This allows the user to visualize the data under different scenarios comfortable in the knowledge that the data is sourced from a single consistent data model that serves all departments in the bank, even as business lines are allocating, analyzing or classifying the underlying data model differently.
This, of course, makes it possible to conduct enterprise-wide stress testing, and enterprise risk management, and gain a true understanding of enterprise risk-adjusted performance. The data model can be used to establish a pervasive business, and solo and multi-country regulatory reporting infrastructure across the organization that can help address the compliance, reporting and analysis requirements of all applicable regulations. Using this kind of model, institutions can extend their existing information infrastructures, resulting in faster response times and lower resource requirements for analysis and design of functional requirements. At the same time, the model provides flexibility of access, combined with auditability and consistent data governance.
From an operational standpoint, a universal data model and corresponding reporting mechanism improves business agility. Achieving greater responsiveness in dealing with external supervisory requests, whilst attaining agility in internal decision-making processes, is arguably a double-win that enables financial institutions to embrace best practices and realize the benefits of improved decision making and competitiveness.