Financial services institutions face significant challenges in modernising legacy systems, with 80% of projects failing to meet goals. This failure can lead to strategic and financial setbacks, impacting competitiveness. Common pitfalls include operational inefficiencies, customer experience deficits, and compliance vulnerabilities. To avoid these issues, firms should adopt AI technology due diligence, form strategic partnerships, implement phased approaches, and define clear success metrics. A proactive strategy is essential for effective modernisation and future growth.
Financial services institutions are currently navigating a period of significant transformation. Modernisation holds the promise of enhanced efficiency, superior customer experiences, and a more robust market position. However, the journey to upgrade legacy systems is fraught with challenges. A concerning statistic reveals that 80% of modernisation projects fail to achieve their intended goals 1.
For leaders in banking, insurance, and investment management, this figure is a stark reminder of the risks involved. Unsuccessful modernisation efforts can lead to strategic and financial setbacks that extend beyond IT, impacting competitiveness and long-term viability. For firms aiming for sustained success, understanding and avoiding this ‘80% Failure Trap’ is not merely advisable; it is essential.
This article explores the common pitfalls that can derail modernisation initiatives. More importantly, it provides a strategic, evidence-based approach, centred on AI technology due diligence, to guide financial services organisations through this intricate process towards successful transformation. By adopting a proactive and informed strategy, institutions can significantly increase their chances of not only modernising effectively but also leveraging these enhancements to propel future growth and innovation.
The Persistent Challenge of Legacy Modernisation
The financial services sector, characterised by its intricate architecture of long-standing systems, faces a particularly demanding modernisation challenge. These deeply embedded systems, often decades old, are the foundation of essential operations. Research confirms that outdated infrastructure is a primary cause of setbacks in digital transformation, with many firms still heavily reliant on legacy systems 1.
While these systems were once cutting-edge, they now frequently impede business agility, inflate operational costs, and constrain innovation.
- Operational Inefficiency: Outdated systems often necessitate manual processes, hindering real-time data analysis and slowing down informed decision-making. This can result in delays in service delivery and increased error rates.
- Customer Experience Deficit: Legacy infrastructure can struggle to deliver the seamless, digital-first interactions that customers now expect, potentially leading to customer dissatisfaction and attrition.
- Competitive Disadvantage: Organisations burdened by legacy systems may find themselves at a disadvantage compared to more agile, digitally advanced competitors, struggling to launch new products or services swiftly, or to adapt to evolving market conditions.
This high failure rate represents more than just a statistic; it signifies wasted investment, missed opportunities, and ongoing vulnerability due to obsolete technology. For financial institutions, the stakes are exceptionally high.
Uncovering the Hidden Costs of Outdated Systems
The limitations of legacy systems extend beyond obvious technological constraints. The true cost often lies in operational inefficiencies and untapped potential. Finance teams, crucial to the smooth operation of these institutions, often find their efforts hampered by manual processes and outdated systems 2.
This not only consumes valuable time but also exposes organisations to unnecessary risks. Financial institutions typically allocate a significant portion of their IT budgets, often between 60% and 80%, to maintaining these legacy systems.
Consider these often-overlooked costs:
- Increased Operational Risk: Manual processes are prone to errors and lack the robust controls of modern systems, increasing the risk of financial misstatements and operational disruptions. This can lead to regulatory scrutiny and financial penalties.
- Compliance Vulnerabilities: Outdated systems may struggle to adapt to evolving regulatory demands, potentially leading to penalties and reputational damage. Non-compliance can escalate operational costs by approximately 10% when modernising payment processing and storage systems. Continuous monitoring frameworks aligned with GDPR and PSD2 standards are therefore essential.
- Reduced Agility and Scalability: Responding swiftly to market changes and scaling operations becomes significantly more complex and costly with inflexible legacy infrastructure, stifling growth and innovation.
- Missed Innovation Opportunities: Resources spent on maintaining outdated systems divert investment from innovation and strategic projects that could drive future growth, potentially leading to a failure to capitalise on emerging market opportunities.
These hidden costs can erode profitability and weaken competitive positioning, underscoring the compelling need for carefully planned modernisation.
The Pitfalls of AI without Modern Foundations
The transformative potential of Artificial Intelligence (AI) is undeniable, offering exciting prospects for financial services. However, many organisations encounter difficulties when attempting to integrate AI solutions with outdated legacy systems. This fundamental mismatch can lead to disappointing results.
Data indicates that many companies fail to realise significant financial benefits from AI, often because they neglect essential infrastructure upgrades 1.
Attempting to implement AI without first addressing legacy system limitations can create several problems:
- Accumulating Technical Debt: Layering new technologies on top of old systems creates complex integrations and ongoing maintenance challenges. Integration complexity between legacy infrastructure and core banking platforms lacking API compatibility contributes to technical debt accumulation, cited by over 75% of banks struggling with digitalisation due to outdated architecture.
- Data Silos and Compatibility Issues: Legacy systems often lack the data quality and accessibility needed for effective AI deployment. Data may be fragmented across disparate systems, making it difficult to create a unified view for AI algorithms.
- Performance Bottlenecks: Outdated infrastructure may not be able to handle the processing demands of AI, resulting in slow performance and limited scalability. Unplanned AI implementation can lead to systemic failure, especially when fraud detection algorithms are rapidly deployed onto legacy transaction-processing infrastructure lacking real-time data capabilities.
With these challenges in mind, a structured approach to technology assessment becomes essential for financial institutions seeking to modernise effectively. For AI to deliver its promised benefits, a robust, modern technological foundation is crucial.
Modernisation is not just a preliminary step to AI adoption; it is a critical prerequisite for success. Without this foundation, AI initiatives risk becoming costly and ineffective add-ons rather than transformative drivers of business value.
"'Legacy systems are frequently large and difficult to modify, and scrapping or replacing them often means re-engineering an organization’s business processes as well.' - Wikipedia"
AI Technology Due Diligence: A Strategic Risk Mitigator
To avoid the ‘80% Failure Trap’, financial institutions must adopt a more disciplined and informed approach to legacy modernisation. This is where AI Technology Due Diligence becomes indispensable. It’s more than just a technical assessment; it’s a strategic process designed to minimise risks and ensure modernisation efforts are practical, effective, and aligned with core business objectives.
Comprehensive technology assessment before modernisation significantly reduces implementation risks and improves outcomes. For smaller financial services businesses with limited resources, cost-effective AI due diligence tools are increasingly available, making this rigorous assessment accessible even for SMEs.
Effective AI Technology Due Diligence involves a thorough evaluation across several key areas:
Comprehensive System Assessment
This involves a detailed examination of the existing technology landscape, identifying interdependencies, bottlenecks, and vulnerabilities. This includes assessing the age, condition, and supportability of hardware and software components.
Diligize methodologies in this area include evaluating the existing technology stack, including software, hardware, and data infrastructure, assessing compatibility with new AI technologies, and pinpointing potential integration challenges.
Data Quality Evaluation
This step focuses on assessing the quality, accessibility, and suitability of data to support both modernisation and future AI initiatives. This involves evaluating data completeness, accuracy, consistency, and relevance.
A common pitfall to avoid here is the lack of transparency in AI models, which can lead to system glitches and data bias. Prioritising robust data governance is crucial to mitigate such biases and meet regulatory demands.
Security and Compliance Analysis
This critical component involves identifying potential data loss and security risks that could arise from AI demands on current infrastructure 3. Effective due diligence must include comprehensive security assessments throughout the modernisation journey.
This should encompass data migration vulnerabilities, integration points between legacy and new systems, and evolving compliance requirements. Implementing a ‘security by design’ approach ensures that protection measures are built into the modernisation process rather than added as an afterthought. Cybersecurity vulnerabilities can increase by approximately 40% during the cloud migration phase, according to reports.
By undertaking comprehensive AI technology due diligence, financial institutions can proactively identify and address potential obstacles, paving the way for a more secure and successful modernisation journey. This proactive stance is particularly important for financial services leaders, many of whom express concerns about infrastructure vulnerabilities when implementing AI 3.
For SMEs, this process should also include evaluating low-code/no-code platforms to expedite upgrades, leveraging their AI and cloud capabilities for faster and more cost-effective solutions 7. Furthermore, to automate security tasks during modernisation, financial institutions can leverage AI-driven tools such as Microsoft’s Security Copilot agents, which handle phishing detection, vulnerability management, and access control, enhancing security and efficiency 8.
Strategic Partnerships: Amplifying Modernisation Success
Navigating the complexities of legacy modernisation is rarely a solitary undertaking. Successful transformations in financial services often rely on building strategic partnerships with specialist technology providers. These collaborations bring essential expertise, experience, and proven methodologies, significantly increasing the likelihood of a positive outcome.
When evaluating potential technology partners, financial institutions should prioritise those with proven expertise in both legacy systems and modern technologies—a combination that ensures a seamless transition while preserving critical business functions.
Strategic partners can offer:
- Specialised Expertise: Access to professionals with in-depth knowledge of both financial services and cutting-edge technologies, bridging internal skill gaps and accelerating project timelines.
- Established Methodologies: Proven frameworks and best practises for managing intricate modernisation projects, reducing the risk of reinventing the wheel and ensuring a structured approach.
- Accelerated Delivery: Experienced partners can expedite the modernisation process, reducing the time to realise value, crucial in fast-paced markets where time-to-market is a key competitive differentiator.
- Shared Risk and Responsibility: Partnerships can distribute risk and provide accountability for project deliverables, providing financial institutions with greater confidence and reducing the burden on internal teams.
For example, the partnership between Tietoevry Banking and Version 1 is a prime illustration, aiming to accelerate digital transformation for European financial institutions by modernising banking systems with SaaS solutions and AI compliance 4.
These kinds of collaborations offer a structured approach to modernisation, addressing both technical and business imperatives, and crucially, helping to avoid common pitfalls. However, it’s important to acknowledge that forming effective strategic partnerships isn’t without its challenges. Compatibility and integration issues between new and old systems, regulatory compliance, data security, and aligning organisational cultures are all factors that require careful consideration.
Despite these challenges, the trend towards strategic partnerships in legacy modernisation is clear, with 80% of successful programmes leveraging specialist FinTech collaborations, compared to a 45% industry average for projects that fail 5. To manage employee resistance during these transitions, especially in SMEs, clear communication, leadership engagement, and comprehensive training are essential best practises 9.
Phased Implementation: A Manageable Path Forward
The ‘big bang’ approach to legacy modernisation – attempting a complete system overhaul all at once – is a high-risk strategy that frequently contributes to the high failure rate. A more pragmatic and effective approach is phased implementation. Breaking down modernisation into smaller, more manageable phases allows for incremental progress, reduces overall risk, and delivers tangible value sooner.
For smaller financial institutions, resource constraints make phased implementation particularly valuable. Starting with high-impact, low-risk modernisation projects—such as customer-facing applications or specific operational bottlenecks—can deliver measurable benefits while building internal capabilities and confidence. Targeted modernisation of loan processing systems often provides the optimal balance of implementation complexity and business impact.
Phased implementation offers several key advantages:
- Reduced Complexity: Focusing on smaller, well-defined projects simplifies management and execution, making it easier to track progress and manage resources effectively.
- Faster Value Delivery: Delivering incremental improvements provides early, demonstrable benefits and builds momentum for further change, helping maintain stakeholder support and justify continued investment.
- Flexibility and Course Correction: Phased approaches allow for adjustments based on feedback and results from each phase, ensuring alignment with evolving business needs. This iterative approach reduces the risk of large-scale failures and allows for continuous improvement.
- Minimised Operational Disruption: Incremental changes are less disruptive to day-to-day operations compared to a large-scale, all-encompassing overhaul, ensuring business continuity and reducing the impact on customer service.
Industry forecasts emphasise the growing need for faster service delivery and reduced costs within financial services 5. Phased modernisation, utilising cloud-enabled computing and AI integration, provides a realistic pathway to achieve these objectives while mitigating the risks associated with large-scale failures.
Measuring the return on investment (ROI) of phased projects is crucial. Financial institutions should consider metrics such as reduction in financial losses, improved detection and response times to cyber threats, and enhanced customer trust to measure the ROI of cybersecurity investments made during modernisation 10. Focusing on key performance indicators (KPIs) such as reduced operational costs, improved customer satisfaction, and enhanced data analytics capabilities can provide clear metrics for success.
"'The biggest risk is not taking any risk... In a world that is changing really quickly, the only strategy that is guaranteed to fail is not taking risks.' - Mark Zuckerberg"
Defining Success: Metrics that Matter
Many modernisation initiatives struggle due to a lack of clearly defined success metrics. Without objective measures, it becomes challenging to track progress, evaluate outcomes, and demonstrate value. Establishing specific, measurable Key Performance Indicators (KPIs) for both technical and business outcomes is essential for guiding modernisation efforts and ensuring accountability.
A mid-sized insurance provider established three primary KPIs for their claims processing modernisation: 40% reduction in processing time, 25% decrease in operational costs, and 90% customer satisfaction with the digital claims experience. By focusing on these specific metrics, they maintained project focus and achieved all three targets within 12 months of implementation.
Effective success metrics should be:
- Specific: Clearly defined and unambiguous, leaving no room for misinterpretation. For example, instead of “improve efficiency,” use “reduce loan processing time by 30%”.
- Measurable: Quantifiable and trackable, allowing for objective assessment of progress.
- Achievable: Realistic and attainable within the project scope and available resources. Setting unrealistic targets can demotivate teams and lead to project failure.
- Relevant: Aligned with overarching business objectives and strategic goals, ensuring modernisation contributes to wider organisational success. Metrics should directly contribute to key business outcomes.
- Time-bound: Having a defined timeframe for achievement, creating a sense of urgency and focus.
Case Study: Tamilnad Mercantile Bank’s Measurable Success
Tamilnad Mercantile Bank (TMB) exemplifies effective modernisation with clear metrics. Facing increasing competition and customer demands for faster service, TMB implemented an AI-powered loan processing with specific targets:
- 25% reduction in loan approval times
- 15% decrease in processing costs
- 30% improvement in customer satisfaction scores
By establishing these concrete metrics before implementation, TMB could track progress throughout their modernisation journey. The result was a measurable 25% reduction in loan approval times, translating to improved customer satisfaction and competitive advantage in their market 6.
For financial institutions, particularly SMEs, such well-defined metrics are vital for maintaining stakeholder confidence and ensuring modernisation efforts deliver real, demonstrable business value. For SMEs with budget constraints, focusing on easily trackable and resource-efficient KPIs is particularly important. Furthermore, financial institutions must also stay informed and proactive in adapting to the latest regulatory compliance standards impacting AI adoption, such as the Digital Operational Resilience Act (DORA) and guidelines on algorithmic transparency and accountability 11.
Conclusion: A Strategic Imperative
Legacy modernisation in financial services is not merely a technical upgrade—it’s a strategic imperative for maintaining competitiveness and enabling future innovation. By implementing rigorous AI technology due diligence, forming strategic partnerships, adopting a phased implementation approach, and measuring success with clear metrics, financial institutions can confidently navigate the modernisation journey.
The organisations that successfully avoid the ‘80% failure trap’ position themselves not just for immediate operational improvements, but for sustained leadership in an increasingly digital financial landscape.
Now that we are a quarter of the way through 2025, the urgency for modernisation is even more pronounced. As financial institutions consider their next steps, initiating a preliminary AI technology due diligence assessment of their current technology landscape is an essential first action. This evaluation will help identify priority areas and potential risks before significant resources are committed.
For a confidential discussion about how Diligize can support your modernisation journey with expert technology advisory services, please reach out to our team today.
Our Opinion
At Diligize, we see the complexities of legacy modernisation in financial services not as insurmountable obstacles, but as critical junctures demanding strategic foresight and expert guidance. The statistics around modernisation project failures are indeed stark, yet they reinforce our long-held belief: technology transformation must be approached with the same rigour and diligence as any major investment decision. For us, this begins with a comprehensive AI technology due diligence assessment. It is simply not enough to consider modernisation as a purely technical undertaking; it is a strategic imperative that must be grounded in a thorough understanding of existing systems, data integrity, and security protocols. This is the foundation upon which successful transformation—and sustained competitive advantage—is built.
Our experience consistently demonstrates that the path to successful modernisation is paved with pragmatism and clear objectives. We advocate for a phased implementation approach, allowing for iterative progress and tangible value delivery at each stage. Strategic partnerships are, in our view, essential for accessing specialised expertise and accelerating project timelines without overstretching internal resources. Crucially, success must be defined by measurable outcomes – specific KPIs that align with broader business goals. For Diligize, modernisation is not merely about upgrading technology; it is about driving demonstrable improvements in efficiency, customer experience, and ultimately, profitability. This is the results-oriented approach that we bring to every client engagement, ensuring that technology investments deliver real, lasting value.
Author
Steve Denby, based in London, UK, is a Senior Partner and an entrepreneur, technologist, consultant, public speaker, and leader with 28 years of experience in managed IT services. Specialising in private equity-backed businesses and rapid-growth organisations, Steve has deep expertise in mergers and acquisitions (M&A), supported by his studies at Imperial College Business School. He focuses on minimising risk and creating value through technology in privately invested companies growing by acquisition.
