Operational Transformation Financial Firms: Oliver Wyman CARE Guide

The Case for Operational Transformation in Financial Services

Oliver Wyman’s report, “Moving From Incremental Improvements to Operational Transformation,” by partners Rico Brandenburg and Matthew Gruber alongside engagement manager Noah Katcher, delivers a blunt verdict on the state of operational change in financial services: most so-called transformation efforts are siloed and unambitious, achieving only incremental and narrow improvements while missing the opportunity for genuine structural change. This is not a theoretical observation—it is backed by empirical analysis of financial institution performance data and informed by Oliver Wyman’s direct experience across the sector.

The report’s central argument is that true operational transformation requires financial firms to rethink their services from end to end while effectively balancing three dimensions simultaneously: customer needs, resilience requirements, and efficiency expectations. Organisations that address these dimensions in isolation—as most currently do—may achieve short-term improvements in one area while creating problems in the others. The result is a cycle of perpetual remediation where each “transformation” generates the next set of problems, consuming resources without building lasting competitive advantage.

The data supporting this argument is compelling. Oliver Wyman’s analysis of 10-K filings from select financial institutions between 2020 and 2024 reveals a clear pattern: institutions with less mature or less disciplined operational practices consistently show higher non-interest expense growth—in some cases exceeding 20% over the five-year span. But cost overruns are just the visible symptom. These same institutions also experience higher customer complaint volumes, longer and more frequent technology disruptions, and higher operational losses. The correlation between operational maturity and multi-dimensional performance is not coincidental—it reflects the fundamental interconnectedness of customer experience, resilience, and efficiency that the report’s framework seeks to address.

Five Drivers Creating Urgency for Transformation

Oliver Wyman identifies five emerging forces that make operational transformation not just desirable but urgent for financial institutions. Each driver individually creates pressure for change; together, they create a convergence that demands a fundamentally different approach to operations.

The accelerating adoption of artificial intelligence is redefining customer engagement, risk management, and operations across financial services. Unlike previous technology waves that offered improvements along a single dimension, AI creates the possibility of simultaneous enhancements across efficiency, customer effectiveness, and resilience that were previously not available. This makes AI both a driver of transformation urgency and an enabler of the multi-dimensional optimisation that true transformation requires.

Rising cost pressures from persistent inflation, margin compression, and recession fears create an environment where operational inefficiency is no longer sustainable. Financial institutions must find new levels of cost discipline without compromising service quality or operational stability—a balance that incremental cost-cutting programmes consistently fail to achieve. The tension between cost reduction and service quality is precisely the kind of multi-dimensional challenge that requires holistic transformation rather than siloed optimisation.

Increasing merger and acquisition activity across financial services creates both motivation and deadline for operational transformation. Acquirers need efficient, well-functioning operations to integrate rapidly and capture deal value. Targets need to demonstrate operational excellence to maximise valuation and smooth integration. Post-merger integration typically exposes operational weaknesses that were tolerable in isolation but become critical when two organisations must operate as one. For a broader perspective on how these strategic pressures intersect with technology investment, see our analysis of the GIC Singapore Annual Report on investment performance and strategy.

Shifting customer experience expectations have reached a tipping point. Modern consumers expect seamless, 24/7 services—a standard set by fintech companies and digital-forward institutions that legacy financial firms struggle to match. The competitive pressure from firms that were designed around modern service paradigms rather than retrofitted from legacy operations is accelerating, creating an existential imperative for incumbents to fundamentally reimagine service delivery rather than incrementally improve existing processes.

Finally, evolving cyber threats demand a modern approach to resilience that cannot be bolted onto legacy systems without creating excessive costs or compromising user experience. The growing sophistication, volume, and impact of cyberattacks require integrated security architectures—another dimension that must be addressed holistically alongside efficiency and customer experience.

Why Incremental Improvement Consistently Fails

The report’s most instructive content may be its analysis of how incremental improvement fails in practice. Two detailed scenarios illustrate patterns that will be uncomfortably familiar to any financial services operations leader.

In the first scenario, a company facing challenges with a legacy payments platform—lengthy outages, security concerns, and increasing customer complaints—launched an initiative to replace the platform as quickly as possible. The team implemented a top-of-the-line modern system that directly replaced the functionality of the legacy platform. Initial results looked positive: a modern user interface, security capabilities exceeding expectations, and reliably high uptime. Success, apparently. But after a year, the payments service had not improved on key metrics such as processing time and customer complaints. The root cause: the team had replaced the technology without redesigning the service itself and its associated core processes. The new platform was running old processes, delivering old outcomes at higher cost. Additional investment was required after installation to improve the upstream and downstream processes that actually drove the metrics that mattered.

The second scenario involves a company that addressed legacy lending operations plagued by cost overruns. An end-to-end review was conducted, opportunities were prioritised, and redesign began—but the team only considered efficiency-focused improvements and chose to delay the customer-facing platform overhaul. Initial efficiency gains materialised. But over the following two years, customer complaints increased and operational losses grew as the degrading platform created downstream problems. The organisation was forced to invest in resilience upgrades, followed by a customer remediation effort—erasing the initial efficiency gains and adding costs that a holistic approach would have avoided.

These scenarios illuminate the structural dynamics that drive incremental failure. Transformation initiatives are typically led by one team tasked with one primary objective: risk teams focus on resilience, finance initiatives target cost reduction, and product teams optimise customer experience. Even when competing objectives are acknowledged, scope is difficult to negotiate due to challenging organisational dynamics, budget restrictions, and urgent regulatory requirements. The consequence is that each narrow success plants the seeds of the next problem—a resilience programme creates unexpected cost implications, a streamlining effort compromises performance, and organisations find themselves revisiting services not long after completion to address elements that were deliberately neglected. Research from the McKinsey Operations Practice corroborates this pattern, finding that narrowly scoped transformation programmes consistently underperform holistic approaches across cost, quality, and customer satisfaction metrics.

The Three Dimensions of True Transformation

The conceptual foundation of Oliver Wyman’s approach rests on a simple but powerful observation: rarely do organisations look across their business and consider customer experience, resilience, and efficiency together. While they succeed in meeting a single, short-term objective, they miss the opportunity to optimise their business and create more effective, longer-lasting outcomes. This three-dimensional framing transforms how organisations think about operational change.

Customer experience and effectiveness measures how well a service meets customer needs—speed, accuracy, availability, personalisation, and ease of use. In financial services, this encompasses everything from account opening times to claims processing to payment error rates. Customer experience is not merely a satisfaction metric; it directly drives retention, cross-sell, and competitive positioning. Institutions that treat it as a secondary concern in operational decisions lose customers to competitors who don’t.

Resilience encompasses the ability to withstand disruptions: technology outages, cyberattacks, operational failures, volume spikes, and regulatory changes. In financial services, resilience has moved from a back-office concern to a board-level priority as regulators worldwide increase operational resilience requirements. The Bank of England’s operational resilience framework, the EU’s Digital Operational Resilience Act (DORA), and similar regulations have created mandatory resilience standards that cannot be addressed in isolation from the services they protect.

Efficiency covers cost management, process streamlining, and operational expense control. In a sector facing persistent margin pressure, efficiency is a non-negotiable requirement. But the report’s key insight is that efficiency pursued in isolation—without considering its impact on customer experience and resilience—creates false savings. Cost reductions that increase complaints, trigger outages, or create regulatory risk are not efficient—they are transfers of cost from one ledger line to another, often with compound interest.

The three dimensions are not merely interconnected—they are mutually constitutive. A resilient system that processes transactions slowly fails customers. An efficient system that breaks under load fails on resilience. A customer-friendly system that costs three times its competitors’ operations fails on efficiency. True operational excellence requires optimisation across all three dimensions simultaneously, which is precisely what the CARE methodology is designed to deliver.

CARE Methodology Step 1: Data-Driven Opportunity Identification

The CARE methodology—Customer-Aligned, Resilient, and Efficient—was developed to address the most common issues seen in financial services transformation efforts. Its first step focuses on unlocking transformative potential by leveraging data insights and AI to identify the services most urgently in need of transformation across all three dimensions.

The methodology begins at the service level rather than the process level—a crucial distinction. Rather than looking at individual processes within an organisational silo, Step 1 evaluates full end-to-end services to identify the highest-impact areas for transformation. A legacy shared service with a high number of touchpoints across the organisation, for example, may represent a higher-value transformation target than a single departmental process, even if the latter has more visible problems.

Cross-functional data is essential to this assessment. Getting a 360-degree view of each service requires integrating data from operations, technology, customer complaints, incident management, financial reporting, and risk assessment. This integration reveals bottlenecks, duplicative efforts, and hidden costs that are invisible within individual silos. The report emphasises the use of AI and machine learning models to drive insights from novel and unstructured data sources—including Risk and Control Self-Assessment (RCSA) outputs—to uncover hidden problem areas that traditional analysis methods would miss.

The output of Step 1 is an impact-based assessment across all three dimensions of resilience, efficiency, and effectiveness that guides investment toward the services with the greatest need for transformation. This prioritisation is critical: not all services require transformation simultaneously, and attempting to transform everything at once typically results in transforming nothing effectively. The data-driven approach ensures that limited transformation resources are directed where they will generate the greatest multi-dimensional impact. Our analysis of McKinsey’s State of AI 2025 explores similar themes around enterprise AI-driven transformation priorities.

CARE Methodology Step 2: Holistic Service Diagnostic

With the highest-priority services identified, Step 2 conducts a collaborative, end-to-end diagnostic to identify the root causes of pain points rather than merely cataloguing symptoms. The distinction between symptoms and root causes is central to the CARE approach: organisations that address symptoms—delays, errors, rework—without understanding their true sources create solutions that are fragile, temporary, and often counterproductive.

The diagnostic follows the service rather than the organisational chart. This means tracing the end-to-end customer journey and supporting processes across functions, not just within individual departments. A payment that appears to work smoothly within the payments team may encounter friction at compliance checkpoints, manual interventions in operations, and delays in customer reporting—problems that are invisible to any single team but painfully visible to the customer and the P&L.

Differentiating between localised issues and systemic frictions is another critical diagnostic skill. A localised issue—a poorly designed form, an outdated validation rule—can be fixed quickly with local changes. A systemic friction—fragmented data architecture, inconsistent process definitions across functions, misaligned incentives between departments—requires structural intervention. The CARE diagnostic is designed to surface these systemic issues that hold back the entire service rather than treating each symptom individually.

Prioritisation at this stage operates three-dimensionally, evaluating each identified issue against its impact on customer alignment, resiliency, and efficiency simultaneously. Issues with cross-cutting impacts—those that affect all three dimensions—receive highest priority because addressing them generates the greatest multi-dimensional return. The output is a clear prioritisation of improvement opportunities that guides both immediate transformation priorities and longer-term initiatives to address issues that require more extensive intervention.

CARE Steps 3-4: Future-Proofing Design and Rapid Delivery

Step 3—Future-Proofing Design—moves from diagnosis to solution, designing innovative enhancements that align strategic vision with actionable outcomes. The emphasis on future-proofing is deliberate: transformation investments that solve today’s problems but cannot adapt to tomorrow’s requirements create technical debt that compounds over time.

Four design principles guide this step. Leveraging new technology—particularly AI—enables three-dimensional optimisation across efficiency, resiliency, and customer effectiveness simultaneously, creating solutions that would have been impossible with previous technology generations. Designing for outcomes rather than outputs ensures that every change delivers measurable impact to the core value proposition of the service, not merely features or compliance checkboxes. Putting the customer first orients design around improving experience, speed, and reliability for end users, recognising that internal efficiency gains that degrade customer experience are not true improvements. Focusing on the future, not just the fix ensures that solutions anticipate evolving business needs while still addressing today’s problems.

Step 4—Rapid Impact Delivery—executes transformation with agility through cross-functional teams, early value delivery, and sustained engagement. The methodology embraces an iterative delivery model that rapidly develops minimum viable products (MVPs) and enhances them incrementally while maintaining focus on the target-state vision. This is not agile for agile’s sake—it is a pragmatic response to the observation that transformation initiatives lose momentum and executive attention when they fail to deliver tangible results quickly.

Visible, active leadership support is positioned as non-negotiable: it drives urgency, removes roadblocks, and signals commitment across the organisation. Change management is embedded from the start—not added as an afterthought—including stakeholder assessment, communication plans, and targeted training. Accountability is maintained through OKRs, KPIs, and KRIs defined upfront with regular monitoring and clear escalation paths. The standard for success is measured against impact metrics, not activity milestones—a crucial distinction that prevents the common trap of busy transformation programmes that produce extensive activity but negligible business improvement.

Case Study: Major Financial Institution Payments Transformation

The report’s case study brings the CARE methodology to life through a real-world application at a major financial institution. The institution aimed to modernise its business but was uncertain where investment would yield the greatest return—a common starting point that typically leads to scattered, unfocused improvement efforts.

In Step 1, the institution used an AI-powered model to assess cross-functional data spanning operations, technology, customer complaints, and incident management. The analysis uncovered major friction in the payments journey driven by hidden costs in exception handling, excessive manual processing, and recurring technology outages. Critically, the payments service was identified as the transformation target—an area that had been neglected through prior siloed analyses. Each individual department had addressed its own payments-related issues, but no one had evaluated the payments service holistically across all functions.

Step 2 mapped the payments service end-to-end across front office, operations, technology, compliance, and customer touchpoints. Beyond the known issue of manual processing, the diagnostic uncovered pain points in anti-financial crime screening, control automation, and customer reporting. The cross-functional view allowed targeting systemic issues rather than treating symptoms in isolation—a perspective that previous departmental improvement efforts had fundamentally lacked.

Step 3 produced a transformation roadmap including full straight-through-processing, resilient architecture upgrades, and substitutability across payment channels. AI and ML models were deployed to simplify exception handling, fraud detection, and sanctions screening, achieving accuracy levels that prior models could not match. The design balanced ambition with feasibility, ensuring that the target state was achievable within realistic resource and timeline constraints.

Step 4 delivered an MVP of key features within months, with continued iteration maintaining focus on the target-state design. Progress was tracked against impact metrics rather than activity milestones, blockers were identified and resolved quickly, and post-implementation support was embedded from the start. The results were measurable: faster cycle times, fewer payment errors, improved resilience during peak volumes, and measurable improvement in customer satisfaction. Cost, performance, and resilience metrics all showed improvement by the end of MVP development—demonstrating the multi-dimensional value that holistic transformation delivers. For additional context on how financial institutions are navigating similar technology-driven transformation, our analysis of NVIDIA’s State of AI Report 2026 covers AI’s impact on industry-wide operational change.

When Operational Transformations Work: Success Factors

Drawing on the case study and broader experience, Oliver Wyman identifies several conditions that determine whether operational transformation succeeds or joins the long list of well-intentioned but ultimately ineffective improvement programmes.

Senior sponsorship is the non-negotiable starting point. Without visible, committed leadership that can direct resources, break down organisational barriers, and sustain focus through inevitable setbacks, transformation reverts to incremental improvement. This sponsorship must be more than nominal—it requires leaders who actively engage with transformation progress, remove roadblocks in real time, and protect transformation scope from the erosion that organisational politics and budget pressures inevitably create.

A cross-functional governance structure that breaks down silos is equally essential. Transformation that follows the organisational chart reproduces the siloed thinking it seeks to overcome. Effective governance brings together operations, technology, risk, compliance, finance, and customer-facing functions under a unified mandate with shared accountability for multi-dimensional outcomes. This collaborative structure ensures that transformation aligns with strategic goals, directs investment toward services most ready for change, and avoids the narrow focus on isolated processes that characterises failed efforts.

The report notes an important caveat: CARE may not be ideal for organisations seeking quick, low-cost solutions. Holistic transformation requires genuine investment of resources, executive attention, and organisational energy. Institutions that cannot commit to this level of engagement are better served by targeted, limited improvements—with the understanding that these improvements will likely need to be revisited as neglected dimensions create new problems.

The concluding insight is worth quoting directly: “Ultimately, incremental improvement only refines what exists. Transformation creates what’s next.” For financial services leaders navigating AI adoption, rising costs, M&A integration, evolving customer expectations, and escalating cyber threats simultaneously, the choice between incremental improvement and genuine transformation is not just an operational decision—it is a strategic imperative that will determine which institutions lead the industry and which spend the next decade perpetually catching up.