2025 Financial Playbook: Modernize systems, govern data and adopt AI

Recommendations for financial leaders in 2025

2025 is shaping up to be yet another year of ups and downs for the financial industry, with heightened uncertainty earlier in the year. The VIX Index, a key gauge of equity volatility, hit its highest level since March 2020 in April 2025, while the Merrill Lynch MOVE Index, which measures bond market volatility, surged in tandem—driven by inflation data, Trump-era tariff reintroductions, and global labor market turbulence (Diamond Hill Report, 2025). 

Geopolitical risks have deepened due to the United States’ re-withdrawal from the Paris Agreement and broader policy shifts under President Trump. These dynamics have raised sovereign risk premiums and fueled uncertainty around energy and environmental regulations (Goldman Sachs report, 2025). As market fragmentation, capital cost fluctuations, and inflation converge, strategic transformation is no longer optional—it’s imperative. In response, financial leaders are prioritizing cost efficiency, AI adoption, and stronger data governance in the second half of 2025.

According to Forrester, over 90% of IT decision-makers’ projects increased budgets in 2025. With this, the opportunity to direct this capital towards modernizing infrastructure, automating workflows, and implementing compliant AI frameworks is now both timely and necessary. In this report, we outline three strategic tech investments firms must make to remain competitive in H2 2025: modernization, data governance, and risk-first AI.  

1. Modernize infrastructure without disruption 

Legacy systems are holding back innovation 

Many financial firms still rely on legacy systems, which, while fundamental to operations, pose significant challenges. These systems are expensive to maintain, lack agility, and introduce operational inefficiencies that stifle growth. According to the NASDAQ Post-Trade Ecosystem Study, 78% of investment budgets are still consumed by maintaining or upgrading legacy infrastructure, leaving little room for transformative investments.

Moreover, the economic burden of relying on legacy technology is significant. McKinsey reports that banks with outdated core systems face operational costs that are up to ten times higher than those using modern infrastructure. Without modernization, an IDC study predicts that banks could lose more than $57 billion by 2028 due to obsolete infrastructure.  

These inefficiencies are not merely financial either; they translate into tangible losses in speed, agility, compliance and competitiveness. Slower product launches, fragmented investment views, and impaired regulatory compliance all stem from outdated infrastructures. For instance, the Software Improvement Group found that 37% of systems built on legacy technologies received a below-average architectural rating, with 2-star systems deploying updates 40% slower than 4-star systems.  

But to modernize your legacy systems with minimal disruption is a challenge within itself. Our recommendation is a pragmatic, low-risk pathway to infrastructure modernization. Rather than a costly and disruptive system-wide overhaul, we advocate for a phased, modular approach that supports continuity, reduces risk and aligns with your firm’s strategic goals. 

Our recommendations: 

Our approach focuses on three pillars: first, building a scaffold layer to modernize vendor platforms incrementally while reducing vendor risk and maintaining continuity. Second, replacing error-prone end-user computing (EUC) tools with secure, auditable systems to enhance compliance and reduce operational risk.

Third, modernizing legacy systems through tailored, resilient applications—avoiding the disruption of full system replacements. This phased strategy empowers institutions to innovate at their own pace, regain control of their technology, and build a foundation for long-term agility and growth. 

Build a scaffold layer using the strangler pattern

The first step is to build a scaffold layer around existing vendor platforms, using the ‘Strangler Pattern’. This approach recognizes that many institutions cannot afford to abruptly decommission third-party systems that underpin core operations. This approach enables firms to build new functionalities around their existing environments. Over time, legacy components are gradually replaced by modern equivalents, routed through APIs and modular architecture. 

This model not only preserves business continuity but also provides optionality—firms can transition at their own pace without being locked into rigid timelines or vendor dependencies.  

Moreover, this vendor scaffolding approach aims to reduce third party vendor risk. A 2019 Celent study found 72% of capital markets firms rely on third-party vendors for mission-critical systems, and a Ponemon study found 60–80% of institutions struggle with third-party risk. In addition, Gartner reports that 83% of firms have experienced a third-party vendor incident in the past two years, underlining the urgency of reducing vendor lock-in.  

Vendor scaffolding allows institutions to de-risk migration and innovate without waiting for full-scale transitions. It mitigates the high switching costs and customization barriers typically associated with vendor systems. The ability to decouple functionality from vendor constraints empowers institutions to regain control over their technology roadmap and better align IT strategy with business outcomes. 

Replace risky end-user computing (EUC) tools

The second pillar in our modernization strategy is the replacement of end-user computing (EUC) systems. EUCs—such as spreadsheets and user-built tools—are notorious for being error-prone and non-compliant, yet many institutions still rely on them for core operations. In fact, a recent study found that 94% of spreadsheets have at least one significant error in them. These tools lack the security, auditability, and scalability demanded by regulatory bodies and sophisticated risk management frameworks. 

We recommend transitioning from fragile EUCs to secure, auditable solutions designed to improve accuracy and mitigate risks. By eliminating EUCs, firms can reduce operational risk, enhance oversight, and ensure their systems are ready for future regulatory requirements and technological advancements. 

Ultimately, investing in modern infrastructure lays the groundwork for innovation, scalability, and strategic advantage in an increasingly complex financial ecosystem. In such a volatile and demanding environment, financial institutions with robust, flexible infrastructure will be far better positioned to respond swiftly and effectively. 

Modernize legacy systems

The third and final step is to modernize legacy systems incrementally. Instead of a disruptive “rip-and-replace,” clients build tailored, resilient and auditable applications. Firms can customize software to meet precise operational needs while avoiding the pitfalls of off-the-shelf, one-size-fits-all solutions. This strategy also preserves operational continuity and existing workflows while reducing long-term costs and operational disruption, which is critical for organizations handling real-time financial transactions and regulatory obligations. 

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2. Turn data into a strategic asset  

Bad data quality and governance costs millions 

Every transaction, trade, and strategic decision rests on the assumption that the underlying data is accurate, timely, and complete. As financial services increasingly turn to data-driven technologies—including advanced analytics and AI—the quality of that data becomes paramount. Moreover, to implement these modern AI solutions successfully is dependent on a strong data governance foundation. 

Yet despite heavy investments, the industry continues to struggle with data integrity. Global spending on financial market data reached $42 billion in 2023, a 12.4% increase year over year. This surge reflects the growing recognition that reliable data is critical to surviving in today’s fast-moving markets. Nevertheless, Mosaic Smart Data reports that 66% of banks face persistent challenges around data quality and integrity, and 58% of firms lack proper metadata and data models (SimCorp). Gartner estimates that financial institutions lose an average of $15 million annually due to bad data. In some cases, the consequences are even more severe. In 2022, Morgan Stanley was fined $35 million not for a sophisticated breach, but for improper handling of customer data during a data center decommissioning.  

Data fragmentation is at the heart of the issue. In many organizations, critical information is spread across multiple systems and platforms, with no unified source of truth. This fragmentation hinders visibility, slows decision-making, and increases the risk of noncompliance. For example, Bloomberg notes that in volatile markets, delays in evaluating trades due to fragmented data can lead to execution failures and missed opportunities. 

Our recommendations: 

To compete effectively in today’s data-rich financial environment, institutions must establish a strong foundation for data quality, governance, and usability. Our recommendations involve centralizing and cleaning real-time data and emphasizing the importance of clear data ownership and workflow governance.

Assigning roles and embedding controls ensures accuracy, compliance and cross-functional alignment. Together, these capabilities not only improve decision-making but also lay the groundwork for safe, effective AI adoption in the future. 

Centralize and clean your data in real-time data 

Firms must unify fragmented datasets into a golden source. Genesis enables real-time ingestion from diverse sources (including email and vendor APIs), powering consistent insights across the trade lifecycle. This system should be continuously updated, easily auditable, and universally accessible to authorized stakeholders. Real-time data integration is essential to achieve this goal. Aggregate data from vendors, internal systems, and informal sources like email to create a centralized, actionable dataset.  

Data must also be normalized, synchronized, and governed. Automate data cleansing and transformation, ensuring consistency and reliability. By removing duplicates, reconciling discrepancies, and applying metadata standards, firms can elevate the quality of their data while reducing manual intervention.  

Establish clear ownership and workflow governance

Effective data governance requires clear roles, responsibilities, and policies. Assign data ownership and enforce stewardship through embedded workflows and approval hierarchies. This helps to ensure that all data modifications are tracked, verified, and aligned with organizational policies.

But data governance is not solely an IT function; it requires cross-departmental alignment. Strong communication is critical for flagging anomalies or breaches, staying compliant, and aligning workflows in a real-time, auditable environment. 

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Case Study: Primary Bond Issuance (PBI)

A notable example is Genesis’ work with a global asset manager to develop the Primary Bond Issuance (PBI) application.

• PBI aggregates data from multiple internal and external sources to provide a centralized, real-time view of the market.
• It enables portfolio managers and analysts to evaluate opportunities quickly, collaborate across functions, and request allocations without delay. The result is a faster, more efficient, and more resilient decision-making process. 

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Finally, robust data governance directly supports AI readiness. Gartner reports that 35% of CFOs cite data quality as a key inhibitor to AI adoption. AI models are only as good as the data they are trained on. Without high-quality, structured data, the outputs of even the most advanced algorithms will be unreliable. Genesis ensures that data governance is not an afterthought but a foundational capability, enabling firms to unlock the full potential of their AI investments.  

But taking a step back, adopting AI has many challenges and risks to consider, and to implement it safely requires thought and precision, outlined in our third and final recommendation.  

3. Unlock AI’s power with guardrails and control

Today, AI is transforming the way people work in all sectors and financial industry is no different. The financial services sector invested approximately $35 billion in AI projects in 2023, and market forecasts project that number to soar to over $190 billion by 2030. Yet, while enthusiasm is high, many firms remain cautious, and rightly so. 

Therefore, a “risk-first” AI adoption approach is necessary. This is where you prioritize identifying, assessing, and mitigating potential risks during the deployment of AI, rather than focusing exclusively on innovation and performance.  

AI’s potential must be approached with governance at its core. Gartner reports that 45% of CEOs view AI as more risky than beneficial. These risks include security vulnerabilities, compliance failures, hallucinations in large language models (LLMs), and integration challenges. A study from San Antonio University, Texas revealed a 20% hallucination rate, or false outputs, in AI-generated code, posing material risks to regulated environments like finance.

Plus, hallucinations increase as the tasks become more complex, evident in highly complex sectors such as finance, medical and legal. One legal tech study found hallucination rates for complex tasks reached 58%. Therefore, to adopt AI into everyday work safely, we recommend addressing these concerns with a “risk-first” philosophy.  

Our recommendations: 

Our risk-first approach, or, ‘AI on guard-rails’ strategy, recognizes AI not as something to avoid, but to carefully manage alongside your regular work and quality assurance processes. We encourage firms to integrate AI into their workflows responsibly, reaping productivity and efficiency gains without increasing systemic risk.  

Empower your developers with AI in a component-based architecture 

Embed AI into a governed, component-based architecture where AI agents interact only at designated extension points. Unlike traditional deployments that give AI free rein over codebases, Genesis ensures predictability, auditability and compliance at every stage.

This strategy empowers developers without replacing them. Using Genesis’ AI-augmented IDE, engineering teams can accelerate front- and back-end development without compromising standards. By maintaining control over logic and data flow, firms can scale innovation safely and sustainably. 

Enable your business users to build with AI safely

We also believe that AI can enable non-technical users to experience the same level of enhanced productivity. As AI tools become more prevalent and advanced, skills that were once confined to specialists can now be adopted by a wider pool of talent.

Tools like Genesis Create enable non-technical users to build powerful applications using natural language prompts. This significantly reduces time-to-market while preserving governance. Applications built with Genesis Create are modular, testable, and production-ready—critical for front-office tools and regulatory workflows. 

Audit and control AI with a Model Context Protocol Server

The Genesis MCP (Model Context Protocol) Server further reinforces control. Acting as a controlled gateway, MCP governs how AI agents interact with applications, ensuring all AI-driven actions are auditable and aligned with compliance requirements. It enables actions like data summarization, aggregation, and interaction via conversational interfaces without sacrificing oversight.  

Adopting AI on guardrails is the key to unlocking its full value. By taking a risk-first approach, financial leaders can move from experimentation to enterprise-wide impact—confident that innovation won’t come at the cost of control. 

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Lead the transformation today 

In a year marked by sharp market swings and rising geopolitical complexity, financial leaders must respond with clarity, discipline and foresight. Modernizing infrastructure, elevating data governance, and adopting AI within clear regulatory guardrails are foundational to resilience and scalability.

These imperatives not only address today’s volatility but also position institutions to capitalize on tomorrow’s opportunities. Firms that act now will not just adapt to the evolving landscape; they will define it. 

Want to assess your AI readiness or modernization path? Talk to a Genesis expert and see how we can help you future-proof with minimal disruption.