The case for standardized data management for your bank finance and risk management

Banks must ensure that decision-makers across the organization have access to accurate, consistent, and up-to-date information to steer the business in the right direction while mitigating risks. Standardized data management in finance, risk and regulatory reporting can be the catalyst for operational and strategic advantages for banks. It eliminates inconsistencies, reduces operational inefficiencies, and enhances regulatory compliance.  

However, many banks remain locked in outdated, fragmented data silos or data warehouses, deterred by the perceived risks, costs, and complexity of large-scale transformation. 

Maintaining the status quo is no longer sustainable. The increasing regulatory scrutiny and growing demands for data transparency, harmonization, simplification, and adoption of AI and automation require a fundamental shift. 

Banks that embrace standardized models to manage data for all assets and liabilities will not only reduce costs and compliance risks. They will also tap into greater efficiencies and new strategic opportunities.   

This article explores why data transformation and standardization is imperative and how banks can effectively transition toward a harmonized data environment. 

Overcoming barriers to data standardization 

Data standardization and harmonization can ensure everyone within an organization accesses the same, accurate, and up-to-date information. It eliminates data inconsistencies, reduces errors, and improves decision-making by providing a single, reliable source for all assets and liability data used across the entire bank’s analytical systems and departments. 

However, the risks, costs, and intricacy of large-scale transformation projects with a focus on complex data integration and harmonization can hinder change. This can convince banks to remain in the status quo. 

Why the status quo isn’t enough

General reliance on in-house integrations and outdated legacy data warehouse solutions are already expensive and inflexible. Continuing with this approach will only increase costs and time to value. 

For example, banks require specialists to integrate and correctly maintain this integration of complex systems across siloed operational systems. One type of specialist integrates and updates systems (change the bank). Another specialist corrects, refines, and reconciles inconsistent data (run the bank). These highly technical roles increase the competition among tech experts, creating inflated costs for banks in attracting and maintaining specialized internal workforces. 

Banks with inconsistent data challenges will also come under increased scrutiny by regulators, cross validating between finance, risk and compliance. Heightened regulatory attention increases workloads to continually correct and reconcile the data to mitigate risks and prevent fines. 

Finally, time to value suffers. The complexity of integrating data across various operational and analytical systems and the limited access and transparency across multiple data sources slows data processing and reporting and limits the value that timely data has in modern bank management.  

Relying on this status quo prevents leaders from having a holistic view across a bank’s entire product portfolio, hindering strategic decision making. 

Key drivers for change

Several critical factors are driving the need for transformation, with implications for cost efficiency, operational effectiveness, and compliance. 

1. Reducing CTB costs while meeting new regulatory and risk management requirements

The rising costs associated with change-the-bank (CTB) initiatives, particularly for regulatory compliance and evolving business needs, are a key pressure point.  

Banks are facing intensifying regulatory scrutiny, particularly in risk management and regulatory reporting. Accurate, timely reporting of financial positions and credit risk measures, like interest-rate and liquidity risks, is critical.  

Standardization enables banks to create a single-source-of-truth data management capability so they can stay on top of regular compliance changes. This reduces errors, improves data traceability, and enhances compliance readiness, while lowering CTB costs. 

Banks that address these drivers effectively can transition from reactive data management to proactive, value-generating operations. Successful data management transformation enables banks to position themselves for sustained growth and operational resilience in an evolving landscape of constantly changing regulatory reporting requirements. 

2. Rising demand for data transparency and consistency

Business leaders increasingly require timely, transparent, and consistent data to support strategic decision-making. Banks face growing demands to deliver enhanced time to value while ensuring that critical information, such as credit risk and liquidity management, is accurate and actionable.  

Standardized data solutions can provide consistent and accurate information in a timely fashion to guide strategies.  

3. Facilitating AI and automation adoption

High-quality, consistent data has become indispensable for the adoption of emerging AI use cases and automation, such as predictive forecasting, strategic analysis, and driving business value.  

Standardized and harmonized data is a prerequisite for AI adoption. With consistent, harmonized data, AI can aid financial management by: 

• Automating repetitive tasks, such as reconciliation. 

• Facilitating cash flow forecasting. 

• Enabling self-service analytics and reporting to uncover new insights and respond quickly to market changes.  

• Identifying data errors, anomalies and inconsistencies. 

• Streamlining regulatory reporting and data gathering processes. 

AI simplifies the operational burden. It also positions banks to meet the time-sensitive demands of audits and compliance reviews with greater precision and speed. 

Achieving a strategic advantage with data standardization

The case for standardized data management in finance, risk and regulatory reporting is clear: it reduces costs, enhances compliance, and enables faster, more informed decision making. As regulatory pressures mount and the need for transparency increases, banks must move beyond reactive data management and toward a proactive, scalable approach. 

By investing in standardized data models and harmonized IT systems, banks can eliminate inefficiencies, accelerate AI adoption, and future-proof their operations. Those that take decisive action today will navigate regulatory complexity with greater ease. They will also position themselves for sustained competitive advantage in an evolving financial landscape. 

To find out how SAP Fioneer’s Financial Services Data Management (FSDM) solution can help banks transform their data management landscape, contact your account representative or click below.