Why profitability steering in banking needs a rethink

This podcast episode is based on the Deloitte Luxembourg article below and includes content generated, assisted, or edited using artificial intelligence technology. It has been reviewed by a human prior to publication. The voices featured are synthetic. This podcast is provided for general information purposes only and does not constitute any kind of professional advice rendered by Deloitte Luxembourg. Deloitte Luxembourg accepts no liability for any loss or damage whatsoever sustained by any person who uses or relies on the content of this podcast. 

Introduction  

Banks are increasingly struggling to understand what truly drives their profitability and to answer fundamental business questions, such as:

• Which products, channels, or client segments are the least profitable?
• What is the true cost of serving a specific client or client segment?
• How does growth in assets under management (AUM) translate into staff costs or other cost bases?
• How do relationship managers, products, business units, and channels contribute to overall performance?

In a context of macroeconomic uncertainty, rising organizational complexity, and heightened regulatory expectations, these questions are no longer confined to reporting, they are central to effective steering and decision-making.

Leading institutions are responding by designing and implementing advanced profitability analytics: structured “digital twins” of the bank that track revenues and costs at granular level across multiple dimensions, such as client, product, business unit, channel, relationship manager, and segment. These dimensions form the foundation for more robust benchmarking, sharper performance management, and better-informed strategic choices.

While allocating revenues across these dimensions is generally relatively straightforward, cost allocation remains significantly more challenging, particularly when shared services, overheads, and indirect costs are involved. As a result, many banks still rely on legacy, fragmented or manual allocation approaches that:

• Obscure true performance and profitability.
• Dilute accountability across business lines and support functions.
• Limit the ability to adapt strategies and reallocate resources as conditions evolve.

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To move from fragmented views to robust profitability analytics, banks need a clear, integrated, and flexible approach to cost allocation. This complexity can be effectively addressed by focusing on four core principles: 

1. Standardized and business agnostic structures,
2. Transparent and objective allocation rules,
3. Data-driven methods that enhance traceability and organizational buy-in, and
4. Models designed to adapt to organizational change while striking the right balance between granularity and manageability.

Standardizing the cost allocation approach

Outdated cost allocation models are hard to trace, slow to update, and poorly aligned with real operations. Data collection is often manual and fragmented, relying on last-minute data extracts from multiple systems and spreadsheets from numerous stakeholders. As a result, maintaining a consistent, auditable view of profitability becomes increasingly difficult.

Standardized frameworks address these issues by clarifying rules, roles, and reporting logic, enabling greater automation of data collection and preparation. This reduces operational burden, supports scaling across regions and entities, and eliminates confusion over “which version of the truth” is being used. Standardization does not mean rigidity—it means applying a shared, reliable method that delivers consistency and transparency while remaining adaptable as the business evolves.

This is where a bank’s “digital twin” becomes a powerful enabler: an agnostic organizational structure that governs cost and revenue allocations across key dimensions, including departments and business units, channels, clients and client segments, products and services, and relationship managers.

Within this structure, profitability analytics tracks how costs and revenues flow through the organization in a modular and flexible way, allowing updates without rebuilding the model from scratch.

For example, following an organizational or chart-of-accounts change, the digital twin can be adjusted within the existing framework. Indirect costs (e.g. IT, HR, marketing, legal, data, ESG, project management) are defined once and redistributed to final profitability dimensions using documented allocation rules. This approach enables cost traceability, supports objective, data-driven decisions, and ensures long-term maintainability as the bank evolves.

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Cost transparency enabling “culture of accountability”

Standardized and well-documented cost allocation models help foster a stronger internal culture. When employees understand how their actions influence costs and how these costs link to performance, they take greater ownership.  This creates a common language between finance and the business, shifting discussions from budget disputes to strategic trade-offs.

Stakeholder engagement also improves when cost and profitability reports are perceived as fair, logical, and understandable. Clear allocation rules and visible links from raw costs to final dimensions (client, product, segment, etc.) drive buy-in. Clarity builds trust, and trust enables better decisions.

A transparent view of costs reframes the conversation from “What does this function cost?” to “What value does it create?” This enables:

• Better pricing and product design;
• Smarter investment decisions across channels, IT, and operations; and
• Fewer internal disputes over shared costs (e.g., IT, HR, marketing, facilities, software licenses, compliance, risk,).

As financial operations become more tightly linked to regulation and performance management, cost clarity is no longer optional; it is a business requirement. The ability to explain and actively use cost data distinguishes organizations that merely report results from those that manage performance. A robust cost allocation model helps organizations:

• Ask better questions and challenge assumptions;
• Support scenario analysis and strategic decision-making; and
• Identify inefficiencies and unprofitable segments or products hidden in standard reporting.

Finally, as banks adopt agile planning and rolling forecasts, flexibility becomes critical. Static annual models quickly lose relevance in volatile environments. Embedded within profitability analytics, a well-designed cost allocation framework can be updated regularly, ensuring insights remain timely and actionable.

Aligning cost allocation with business strategy

Cost allocation is most effective when embedded within a broader profitability steering framework and aligned with the bank’s strategic objectives. When properly integrated, it connects day-to-day operations with financial targets and long-term planning. Conversely, misaligned cost models can send the wrong signals, driving unintended behaviours, obscuring risks, or overlooking strategic opportunities.

Alignment starts with clear governance and ownership, with well-defined responsibilities for the design, maintenance, and evolution of the model. Costs should be allocated in line with how the bank actually creates value, rather than mirroring the accounting chart of accounts.

In practice, this means integrating cost allocation and profitability analytics into core business processes, including pricing of products and services, budgeting and resource allocation Performance tracking for business units, segments, and relationship managers, and strategic planning and monitoring of key initiatives.

This integration enables consistent monitoring of product and client profitability, identification of profit-enhancing opportunities (including at the frontline), and reporting along business-relevant dimensions to management and the board.

When supported by profitability analytics:

• Performance reviews can be conducted on a regular basis (e.g., monthly or quarterly) with business departments, objectively measuring the impact of their actions using facts and data.
• Financial control can evolve from a purely reporting function into a strategic advisory role, actively supporting profitability and improving the cost-to-income ratio.
• Cost management can shift from silo-based decision-making to cross-organizational cost optimization, enabling synergies and more coherent, strategically aligned initiatives.

Conclusion

Cost allocation is evolving from a narrow, compliance-driven exercise into a central lever for profitability steering. As banks face pressure to deliver resilient profitability, meet rising regulatory expectations, and navigate ongoing digital transformation, understanding profitability drivers at granular level has become essential.

Profitability analytics enable this shift by allowing banks to:

• Build a consistent “digital twin” of the organization, capturing costs and revenues across the dimensions that matter most—clients, products, segments, channels, relationship managers, business units.
• Foster a culture of accountability by making cost drivers visible, traceable, and easy to understand.
• Align cost allocation with business strategy, embedding profitability insights into pricing, budgeting, performance management, and strategic planning.

The objective is not to introduce complexity for its own sake, but to design models that are clear, usable, and aligned with how the business actually operates. Banks that invest in cost transparency, robust profitability analytics, and strong governance are better equipped to manage change, meet regulatory expectations, and steer their strategy with confidence.

Cost allocation is no longer just a finance exercise; it is a leadership steering tool, and when designed and applied thoughtfully, it enables better decisions, more effective resource allocation, and sustainable growth grounded in a deeper understanding of true profitability drivers.