The banking industry is one of the most data-intensive industries in the global economy, with institutions relying on vast stores of information to deliver their services, manage risk and meet their compliance obligations. While many banks have invested significantly in optimizing data management to improve front-end customer-facing operations, back-office processes have struggled to keep up.
It’s no longer enough to simply store data – financial information needs to be structured, accessible and model-ready to enable banks to adapt to the changing environment. However, data for finance, risk and regulatory reporting often reside in different systems, siloed from an organization and IT perspective, requiring extensive manual management, reconciliation and transfer in order to access.
This presents major challenges for CFOs looking to keep up with constantly changing regulatory requirements and gain timely access to complex accounting, risk and regulator reporting and analytics, with a Gartner report putting the cost of poor data quality at $15 million per organization per year.
Here, SAP Fioneer Chief Solution Expert Karsten Egetoft and SAP Fioneer Product Owner Marko Jendreck explore why traditional banking approaches to data management are falling short, the opportunities available for data-ready organizations and how modern systems capture the value of the data available to the organization to turn data into valuable insights and at the same time drive down costs.
The cost of data for banks
Banks manage enormous amounts of data around their customers and their usage of banking services – every transaction leaves a trace, and every decision remains as a record for posterity. This data plays a key role in not just operational delivery and customer service, but also managing the essential back-office that keeps a bank solvent, compliant and efficient, including:
- Accounting / Finance (e.g IFRS9)
- Risk (e.g. credit risk, market risk, liquidity risk)
- Regulatory Reporting (e.g. FINREP, COREP, CRD IV, and CRR)
The first challenge is sourcing – this requires bringing together information from a range of systems, both internal and external. However, without a standard means of integration, IT teams have no choice but to build bespoke integrations for every new data source or third-party solutions, managing diverse formats, templates and data lineages.
“Today every company is facing constant changes to their interfaces or connected products. Building integrations one by one is a major cost center, slowing down innovation and adding risk for any new data project.’ says Marko.
Once gathered, the traditional approach to managing this information leveraged vast third-party data warehousing tools to store data, combined with internal models to leverage information for internal accounting, risk and regulatory reporting teams.
In working with leading institutions, we have seen the limits of this approach as the pace of change in risk, regulation and market agility has increased. “In many banks, information is still accessed and leveraged on a case-by-case basis, with business owners working with IT teams to set requirements, build models and perform analysis each time a challenge arises, from new compliance, accounting regulations, or changing capital requirements,” explains Karsten.
“This puts huge pressure on IT teams whose main job is to run the bank, creating a bottleneck for important changes in the business, creating unnecessary costs, slowing innovation, and creating additional regulatory and operational risk. In many cases, once an integration is built, it can take up to nine months of back-and-forth to revise and implement the data requirements for a new risk model, during which time the rules may have already changed,” says Karsten.
Data management challenges
The legacy approach has a direct effect on bank profitability and performance due to:
- Lack of visibility: Complex data integration with point-to-point integration from systems of record (e.g. core banking applications) to analytical banking applications often involving one or more data warehouses for different analytical scenarios. One McKinsey report found a bank spending $100 million over a few months to document the data lineage for a handful of models.
- Manual review and standardization: New data sources are connected one by one and require extensive manual work to bring data into a standard format and track the origins and destinations from the business side.
- Low time to value for new data-driven use cases: Limited access and transparency across multiple data sources means new changes can take months or even years.
- High implementation and maintenance costs: New data and reporting requirements raise costs due to missing out-of-the-box integration to standard finance, risk and regulatory reporting applications.
- Poor data quality: Multiple copies of the same data due to multiple physical data stores – often one for each reporting requirement create inconsistencies and high costs for time-consuming manual reconciliation.
As regulators and investors put pressure on banks to manage capital, risk models, and regulatory reporting requirements more efficiently, data management practices will need to evolve.
Changing the culture of data in banking
At the heart of the issue sits an essential structural tension in the way data is stored and deployed within banks’ back office operations. “Home-grown business warehouse installations are seen as an IT asset, managed and run by the IT department. However, within the organization it is business users who rely on this data day-to-day to perform essential functions,” says Karsten
In a modern financial structure, where data is a key asset for finance, risk, and regulatory reporting, responsibility for its management and use needs to shift to business users. However, this means systems that fit their requirements, moving from an IT-dependent model, where technical resources act as a limiter, to an IT-empowered model where both sides of the business can collaborate effectively.
Key requirements for modern data management
- Standard templates for integration, data output, and lineage and audit tracking to enable efficient connectivity across internal and external data sources and 3rd party vendor applications.
- A consistent business-driven data process for acquisition and modeling owned by business users with a full scope of analytical banking applications for finance, risk, and regulatory reporting.
- Data consistency by design based on the “Single Source of Truth” (SSOT) concept including full versioning and historicization of all data, as well as fully auditable data trails between sources.
- Out-of-the-box integration content from source data to analytics reports and analytical applications to speed up implementation time and drive down costs
- Extension capabilities of the pre-defined data model and integration content based on clear extensibility patterns.
- Instant access and full transparency to fine granular data for finance, risk, and regulatory reporting requirements including modern API technology to consume and share the data across the organization.
- Trusted data with transparency helps business users verify data origin, transformations, and quality.
This system creates true accountability for both IT and business users, with the former able to clearly understand requirements and the latter able to see the dependencies and data model content behind their use cases in real-time.
Standardizing data for agility and growth
Working with a range of leading financial institutions around the world, SAP Fioneer has created a single source of truth integration and data management solution for data acquisition, analysis and reporting based on a single consistent data model that supports consistency and simple access from analytics applications for finance, risk and regulatory reporting.
- FOF, short for Finance Open Integration Framework, is an all-in-one solution to streamline integration through standardized interfaces, reliable access to S/4HANA data, third-party platforms and end-to-end lineages to create a consistent, efficient data journey.
- FSDM, short for Financial Services Data Management, acts as a data and integration component, harmonizing and decoupling the world of operational banking systems and the analytical applications used by the office of the CFO, such as SAP S/4HANA for financial products subledger (FPSL) and best of breed risk management and regulatory reporting solutions from vendors like Regnology, Wolters Kluwer, msg, Zeb or FIS.
Key advantages for banks
- The open data management solution offers APIs based on industry standards is fully extensible by customers and partners and is upgradable, with efficient operations and tools on the SAP S/4HANA technology stack.
- FOF enables agile implementation of new analytical applications and integration of new sources of information to help institutions adapt quickly to new data needs.
- FSDM enables data management for finance, risk and regulatory reporting at a low TCO (total cost of ownership) for banks using SAP S/4HANA Finance and other SAP Fioneer solutions together with best-of-breed third-party vendors for risk and regulatory reporting.
- Business-user applications can access globally aligned data across all lines of business and products offered by the bank at a fine granular level.
Key to our holistic-data approach is a single data model leading to a built-in abstraction layer that unifies access from analytical applications irrespective of the concrete customer situation and the number and complexity of the system of record data sources.
“Whether working across current accounts, credit cards, loans, collaterals, treasury, investment banking, commercial banking, capital markets, users can access data from a single source with full confidence in its completeness, currency, and accuracy,” explains Karsten. “Not only does this reduce costs and lead times for key projects, but users can also have real confidence in their decisions.”
Future-proofing data systems
As the pace of change and the volume of data held by banks increases, tailor-made software systems will face complexity and cost over time. Building a consistent data approach for finance, risk, and regulatory reporting is a complex task and it requires deep know-how and experience – knowledge that is increasingly rare in today’s market. With infrastructure built for scaling and stability and flexible deployment options, institutions can focus on their actual business instead of the in-house extension of their tailor-made solutions. All this applies to Fioneer FOF and FSDM, backed by a dedicated team of SAP Fioneer experts.
Instead of viewing data as a cost driver, leaders must now see it as an essential asset – one that requires certain investments to unlock its true value and drive down costs over time. Building a modern data management platform based on standard software for finance, risk, and regulatory reporting is an investment that will pay off handsomely for tomorrow’s digital leaders.