In financial services, the pressure on data has never been more intense. Banks and financial institutions face a daunting convergence of demands: deploying artificial intelligence, strengthening cybersecurity, modernising legacy platforms, and demonstrating robust governance – all simultaneously.
Looking toward 2026, the firms that thrive will be those that treat data management as a core strategic capability, not merely a back-office housekeeping exercise.
Why 2026 is a turning point for banks and financial institutions
Several transformative trends are converging to make 2026 a critical inflection point for financial services data management.
Artificial intelligence is moving rapidly from experimental pilots to production deployment. A joint survey by the Bank of England and FCA found that 75% of UK financial services firms are already using AI, with a further 10% planning adoption within three years[1]. Use cases span credit decisioning, financial crime detection, trading surveillance, and customer engagement. The FCA has confirmed it will not introduce AI-specific regulations, instead relying on existing frameworks including the Consumer Duty and Senior Managers and Certification Regime to govern responsible AI adoption[2].
Cyber threats are becoming dramatically more sophisticated. The NCSC’s 2025 Annual Review revealed a significant escalation: 204 nationally significant cyber incidents were recorded between September 2024 and August 2025 – more than double the previous year’s figure of 89[3]. Financial services remain a prime target, with ransomware representing the most pressing threat to UK organisations. The financial services sector faces an evolving threat landscape where cyber resilience has become a boardroom imperative[4].
Legacy platforms present an increasingly acute challenge. Research suggests that financial institutions consistently underestimate the true total cost of ownership of legacy systems by 70-80%, with modernisation programmes demonstrating potential to reduce total cost of ownership by 38-52%[5]. Banks reportedly spend nearly 40% of their IT budgets simply maintaining legacy platforms, diverting resources from innovation and digital transformation[6].
In this environment, rushing to adopt new tools without first addressing data foundations creates substantial risk.
AI, cybersecurity and cloud: The data reality
By 2026, most financial services firms will be navigating some point along a transformation journey that encompasses three interconnected shifts.
From siloed data to integrated, governed platforms
The era of dozens of unconnected systems is ending. The goal is to create a managed, auditable data layer where information can be analysed holistically rather than in isolation. This requires carefully planned data migrations from older core systems and product platforms into modern, cloud-ready environments.
Modern core banking platforms leverage microservices-based architectures with APIs that enable integration across previously siloed functions[7]. The prize is significant: organisations that successfully modernise report faster time-to-market for new products, improved compliance capabilities, and the ability to deploy sophisticated AI and analytics that drive better decision-making.
Integration must be designed so that payments data, transaction records, CRM information, contact centre interactions, and scanned documents can be analysed together. This unified view is essential for the credit models, fraud detection systems, and regulatory reporting that regulators increasingly expect.
From tick-box cybersecurity to data-centric security
As AI and analytics become embedded in everyday processes, the security focus must shift from perimeter defences to protecting data itself. Critical questions that were once afterthoughts are becoming central to every project discussion: Who can access sensitive data? How is it encrypted at rest and in transit? How are access rights managed when data is migrated or replicated for AI training and analytics projects?
The Digital Operational Resilience Act (DORA), which came into force in January 2025, underscores the regulatory emphasis on operational resilience and third-party risk management[8]. By 2026, data-centric security will be a standard design requirement rather than an optional enhancement.
From paper archives to analytics-ready historical data
Many financial institutions hold enormous archives accumulated over decades: loan files, account records, KYC documentation, mandate forms, and more. Historically, these materials have been scanned purely for retention and regulatory compliance purposes – not for analysis.
That approach is changing. Advanced data analytics enables financial institutions to extract actionable intelligence from historical records[9]. Scanning and capturing key data from these records – and linking it to current systems – can materially improve credit models, collections strategies, fraud detection capabilities, and customer remediation programmes.
The digitisation of historical records transforms what were static compliance archives into dynamic analytical assets. Machine learning algorithms can analyse historical transaction data and customer records to create more nuanced risk profiles and identify patterns that would be impossible to detect through traditional methods[10].
Practical priorities for 2026
For senior leaders in financial services, several priorities stand out as essential preparations for the data landscape of 2026.
Build a clear inventory of critical data sources
This includes not only current production systems but also off-site storage facilities and legacy archives. Understanding what data exists and where it resides is the foundation for any transformation programme.
Plan data migrations as phased, controlled programmes
Successful modernisation initiatives use progressive replacement approaches rather than high-risk “big bang” migrations. Co-existence strategies that run legacy and modern systems in parallel reduce operational risk while maintaining data lineage and compliance throughout the transition[11].
Use data integration to break down silos
Risk, finance, operations, and customer channels should share a common data foundation. This integration enables the unified customer views and sophisticated analytics that drive competitive advantage and regulatory compliance.
Treat every digitisation or migration project as both an opportunity and a risk
Each project creates possibilities for enhanced analytics while simultaneously introducing potential cyber vulnerabilities. Strong governance throughout the project lifecycle – from initial planning through to ongoing operation – ensures that organisations realise benefits while managing risks appropriately.
How Dajon helps financial services firms modernise data
Dajon supports financial institutions to navigate the complex transition from legacy data environments to modern, analytics-ready platforms. Our services include:
Scanning and digitising historical financial records at scale
We deliver high-quality capture and intelligent indexing that transforms paper archives into structured, searchable data assets ready for analysis.
Migrating data securely from legacy platforms to modern systems
Our controlled migration programmes maintain data integrity and regulatory compliance while minimising operational disruption.
Designing data integration and capture workflows
We create the foundations that enable AI and analytics to operate reliably across previously siloed information sources.
Embedding governance and cybersecurity throughout the data lifecycle
Security and compliance are built into how data is managed and shared, not bolted on as afterthoughts.
In short, we help you turn decades of scattered financial data into a controlled, high-value strategic asset – ready for AI, regulatory scrutiny, and the competitive demands of 2026.
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- Legacy System Modernization PiTech[↩]
- Modernizing Legacy Systems in Banking Deloitte[↩]
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- The Role of Big Data Analytics In Financial Services Infosys BPM[↩]
- Big Data Applications and Benefits for Finance Tratta[↩]
- 10 Key Areas For A Successful Core Banking Modernization Oliver Wyman[↩]