Achieving interoperability within banking systems is becoming less of a luxury and more of a necessity. To continue thriving and maintaining a competitive advantage when faced with heightened regulatory challenges, industry complexity, rapid market shifts, and an ever-growing web of interconnected risks, the banking industry is tasked with evolving beyond traditional strategies and technologies.
There are many benefits to data interoperability, including faster decision-making, reduced operational costs, and improved data quality and risk aggregation. However, banks face significant cultural and structural challenges that hinder their progress. This blog examines these challenges and how data interoperability can help bridge the gaps, creating a seamless banking environment.
Enhancing productivity by improving operational efficiency and end-to-end data lineage
The inherent divide between different functions — particularly sales, risk, and compliance — often leads to departments working in isolation, which can result in inefficiencies and slower decision-making. Data silos — often cross-region, department, and division — exacerbate these barriers, preventing a holistic view of the bank's operations. This fragmentation can lead to inconsistent risk assessments and a lack of understanding of the bank’s overall risk exposure. At a higher level, sales teams may focus on short-term revenue prioritization goals while risk teams prioritize mitigating underlying longer-term risks. This misalignment can lead to conflicting strategies and missed opportunities.
Blind spots can also leave the business vulnerable to unforeseen threats. Traditional portfolio management strategies are often insufficient to address dynamic risks like cyberattacks, which can have cascading impacts on credit quality, liquidity, and operational stability. Data silos prevent critical information from being shared in a simple, accurate, complete, or timely manner across functions. If a cyberattack on a key client is not promptly communicated to the credit risk team, they cannot make necessary adjustments to the portfolio, increasing the risk of financial loss. Such risks can be proactively identified and mitigated by integrating incremental data assets such as cyber risk ratings and environmental, social, and governance (ESG) data to enhance early warning systems and connect systems together to offer real-time insights.
These same issues impact effective balance sheet and interest rate management. As banks navigate the challenges posed by volatile interest rates, data interoperability allows them to perform stress testing and scenario analysis more effectively. By integrating data from various sources, banks can better understand the potential impact of interest rate changes on their loan portfolios and develop and share strategies to mitigate risks.
By building a culture of collaboration and data sharing and using advanced technology to achieve data interoperability, banks’ functions can align their objectives and improve overall performance.
Structural inefficiencies within banks contribute to the challenges of achieving interoperability. Issues with fragmented data systems and inadequate data management practices can lead to operational vulnerabilities in processes and require multiple manual interventions. Capturing data multiple times or having several points of human interference further down the process chain increases operational risks and can have major impacts on reputation and customer journeys.
Accurate risk assessment, reporting, and downstream calculations hinge on the integrity of upstream data. This dependency makes a clear case for investment in technologies that enhance end-to-end data accuracy and allow for workflow automation and reporting standardization. Advanced analytical tools significantly reduce breaks in the process, manual interventions, and errors.
A good example to quantify the potential impacts here is addressing overlays, or margins of conservatism. These significantly impact a bank’s profitability and are one of the main ways upstream investment has a direct impact on the bottom line. Every $1 billion in risk-weighted assets (RWA) overlays can result in a $10 million-$15 million hit to profitability or up to $40 million if reducing RWAs via the secondary market [1]. Many of these overlays are necessary as a direct result of poor data quality.
By implementing a robust reference data strategy and focusing on authoritative golden data sources, maintaining data quality from its capture at origination throughout its life cycle becomes easier. Leveraging external data sources to complement proprietary data can help banks improve their risk assessments’ reliability and decision-making processes as well as make significant cost savings further down the chain.
Connected data as an enabler for growth
Data "productization" for easy integration
Data interoperability is a vital mechanism for bridging cultural and structural gaps in banks, especially in the face of the emerging regulatory challenges that globally systemic important banks (G-SIBs) face. By investing in a robust enterprise-wide data management framework, banks can move beyond traditional strategies and gain a competitive edge. One approach is to treat data as a product, designing datasets that systems and functions can easily integrate and use with clear ownership structures and accountability for that data.
For example, by treating certain datasets as “published,” banks can create standardized, high-quality data packages for different departments and external partners to use. This improves operational efficiency and drives strategic growth.
Entity verification and master data management (MDM)
Entity verification and MDM are critical components of an effective data management strategy, creating, managing, and maintaining a consistent, accurate single source of truth for critical data assets. The transition of entity verification into MDM represents a major shift toward more streamlined data ecosystems. Entity verification APIs provide data from various sources for robust Know-Your-Business processes. This not only facilitates compliance with Know-Your-Customer (KYC) and anti-money laundering (AML) regulations but also provides a competitive advantage and enhances customer satisfaction.
Overall, having a robust MDM strategy, framework, and resilient systems improves operational efficiency, data quality, and strategic growth by maintaining consistent, accurate, and comprehensive data across departments. A new, faster, and more cost-effective alternative is the “keyring approach.” This allows banks to link master data across silos, creating unique identifiers — or “keys” — that connect disparate data sources, creating a single customer view without full data integration. This solution minimizes compliance risks by keeping data in its original location.
Some simple steps banks can take to start adapting their programs to meet new challenges and stay ahead include:
- Conducting a data maturity assessment
Identify gaps in your current data management framework, data governance processes, integrations, and data quality practices. - Prioritizing critical data elements
Focus on the critical data subsets and elements that drive the most value, such as customer IDs for KYC and counterparty data for credit risk. - Developing a phased implementation plan
Start with quick wins and scale up to a comprehensive MDM framework over time.
Partnering with specialized solutions providers can be instrumental to success in these transformations. These partnerships allow banks to take advantage of expertise and tools to accelerate implementation and achieve measurable results.
The cost of not investing in robust data management
In another Moody’s blog, “Banking on change: technology’s role in the new financial era,” we discuss the importance of embracing new technologies to remain competitive. Data interoperability is a foundational element for using artificial intelligence (AI) technologies effectively since it allows data from different systems to be seamlessly integrated and analyzed. This integration is essential for developing AI-driven insights and automating processes.
In conclusion, achieving data interoperability is essential for creating a seamless banking environment. By addressing cultural and structural challenges, investing in robust data management practices, and leveraging entity verification and MDM insights, banks can bridge the gaps and gain a competitive edge. The cost of not investing in these practices is significant, but the benefits of a unified, clean data ecosystem are immense. Banks that embrace data interoperability will be better positioned to navigate the complexities of the modern financial landscape and achieve long-term success.
Banks need to separate risk signals from the noise, connect data more seamlessly, and uncover patterns hidden within the chaos so they can stay ahead of the curve.
Moody’s brings together data, experience, and best practice capabilities, with our specialized and agile intelligence.
All so banks can act with confidence.
Talk to us today to find out more about how we can help with your specific needs.
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[1] Calculation: Extra Revenue=Saved RWA x Capital Requirement Ratio X Cost of Funding x Return on Redeployed Capital
Common equity Tier 1 (CET1) is approx. 10-14% (varies by jurisdiction and product).
Cost of Funding typically is 3-6%
Return on redeployed capital ranges from 10-20% depending on business model
Conservatively, if Cap Requirement ratio is 10%, Cost of Funding is 5%, return on redeployed capital is 15%
Extra revenue = $1bn x 12% x (15%-3%) = $14.4m
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