The client is a large global banking institution operating across multiple regions, including America, Asia-Pacific, Europe, and the Middle East. The bank manages a complex structure of more than 150 legal entities, each operating in different currencies and regulatory environments.
The organization reports under both IFRS and local statutory frameworks, with Net Interest Income (NII) serving as the most critical performance indicator for both management and regulatory reporting.
Net Interest Income represents the difference between interest earned on assets (such as loans and securities) and interest paid on liabilities (such as deposits). It is the primary driver of profitability for the bank.
However, prior to implementation, the NII process was highly fragmented and inefficient. Data was spread across multiple systems including core banking platforms, treasury systems, and ALM tools, resulting in heavy reliance on Excel-based reconciliations.
While management could view overall NII figures, there was limited transparency into the underlying drivers such as interest rates, balances, and product mix. This made it difficult to answer key business questions like:
In addition, interest rates themselves were not centrally governed. Different types of rates existed across systems, and they were maintained outside the CPM environment, making forecasting and scenario analysis extremely difficult.
Senior management required a solution that could provide:
The implementation faced several complex challenges due to the nature of banking operations.
Fragmented Data Sources
Loans, deposits, and investment data were sourced from multiple systems, each with different formats and structures. There was no centralized mechanism to unify this data within a single analytical model.
Complex Interest Rate Management (Critical Challenge)
One of the most significant challenges was handling interest rates.
The bank used 8–9 different types of interest rates, including:
Additionally, these rates existed at different frequencies, such as:
This made direct loading into OneStream difficult. The rates required preprocessing, transformation, and alignment before they could be used in financial calculations.
Complex Product Structures
Banking products included both fixed and floating rate instruments, each with different repricing frequencies and regional variations.
Manual Adjustments and Controls
A large portion of the process relied on manual Excel adjustments, especially for accruals and rate overrides. These adjustments often came late in the cycle, impacting reporting timelines.
Limited Explainability
Although NII values were available, the organization lacked the ability to explain movements in NII. There was no structured way to break down changes into rate, volume, or mix impacts. OneStream, the month‑end close process was slow, error‑prone, and highly dependent on a few key individuals.
The implementation was delivered in approximately 6 months, using a phased approach designed to minimize risk and build trust with local finance teams.
Phase 1 – Design and Foundation (6 months)
Conducted a series of detailed workshops with group finance, treasury, and regional teams to define the target Net Interest Income (NII) model, including product structures, rate types, and reporting requirements.
Analyzed existing Excel models, ALM systems, and core banking data to design a standardized dimensional model and driver-based framework within OneStream.
Defined the strategy for handling multiple interest rate types (8–9 types), including:
Designed and established the data architecture:
Agreed a phased delivery roadmap focusing on:
Phase 2 – Build and Integration (7 months)
Configured OneStream cubes to support:
Implemented data integration:
Developed advanced interest rate integration framework:
Implemented complex business rules to:
Recreated and enhanced reporting within OneStream, including:
Phase 3 – Parallel Run and Go-Live (5 months)
Executed multiple parallel cycles with OneStream running alongside legacy Excel-based processes.
Used differences identified during parallel runs to:
Validated outputs across:
Trained finance, treasury, and regional teams focusing on:
Successfully transitioned to full production with OneStream as the single source of truth.
Post go-live, continued stabilization ensured:
The solution was designed to centralize data, standardize calculations, and provide full transparency into NII drivers
The implementation delivered several key reports that provided deep business insights.
Net Interest Income Report
The core report provided a consolidated and drillable view of NII across:
Cost of Funds Report (Custom Enhancement)
A dedicated Cost of Funds report was developed to analyze:
This report enabled finance teams to understand the cost impact of funding sources and how it evolved over time.
Transfer Pricing Report (Custom Enhancement)
A Transfer Pricing framework was implemented to:
The report supported:
This provided greater visibility into internal financial performance.
Variance Analysis
Automated variance analysis was enabled to break down NII changes into:
This allowed management to clearly understand the drivers behind NII movements.
Governance & Controls
Strong governance mechanisms were built into the system, including:
These controls significantly improved data reliability and audit compliance.
Data Accuracy & Control
Business Insights
This implementation demonstrated that NII is not just a financial metric, but a comprehensive performance narrative driven by rates, volumes, and customer behavior.
By leveraging OneStream’s extensible dimensionality and business rule framework, the bank successfully transformed its NII process into a transparent, automated, and highly analytical system.
Conclusion
The project delivered a scalable and future-ready NII solution that not only addressed current challenges but also enabled advanced capabilities such as scenario modeling, transfer pricing, and cost of funds analysis.
The inclusion of complex rate handling—particularly managing multiple rate types and frequencies using database integration and Connect Business Rules—was a key success factor that differentiated this implementation.
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