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The rise of AI in financial services
Any new technology entering the financial sector must pass rigorous compliance and resilience checks – a process that can slow adoption. Yet generative AI is rapidly becoming central to financial institutions’ technology strategies, driven by the need for efficiency, competitive advantage, and cost optimization.
BBVA, for example, has deployed over 3,000 ChatGPT Enterprise licenses to boost productivity across legal, risk, marketing, talent, and finance. Beyond these functions, AI’s potential extends to KYC, research, credit underwriting, and client services.
However, while AI offers tremendous opportunity, it also presents challenges. The rapid increase in AI-generated code introduces governance, compliance, and security concerns. Integrating AI with complex internal systems and databases remains a persistent barrier.
The challenge of AI in a legacy-dominated landscape
The push to adopt AI coincides with a long-standing challenge: modernizing legacy technology. Up to 70% of financial institutions’ tech budgets are dedicated to maintaining existing infrastructure, leaving less than 10% for innovation.
According to McKinsey, banking technology spend is rising 9% annually, outpacing revenue growth of 4%. Yet, despite this increased investment, many firms report declining productivity, rising complexity, and difficulty linking technology spend to competitive advantage.
Those that succeed often create a virtuous cycle where improved developer productivity frees capacity for innovation. AI-assisted coding and agentic AI can accelerate software development, reduce vendor dependencies, and modernize legacy systems.
But early results show the full potential hasn’t yet been realized. A study of 5,000 developers using GitHub Copilot found that AI-assisted coding increased productivity by 26%, with the greatest gains among junior developers. Yet this raises concerns around defects and security, as less-experienced coders may rely too heavily on AI outputs.
To fully unlock AI’s value, financial institutions need more than speed — they need structured governance and domain-specific frameworks that enforce compliance and quality
Use industry-specific guardrails and frameworks to enhance productivity and compliance
Financial institutions can maximize AI’s benefits while minimizing risk by embedding AI within trusted, industry-specific frameworks. These frameworks ensure that AI-generated code meets regulatory, operational, and security standards.
A validated software development framework allows firms to maintain compliance while leveraging AI for innovation. Operating within predefined guardrails helps prevent deviations that could introduce risk.
As Sequoia Capital notes, industry-specific architecture provides “guardrails to ensure compliance and application logic that mimics the way a human might reason through a workflow.”
Genesis enables this structured, AI-driven development approach — empowering firms to innovate faster without sacrificing governance, resilience, or control.
Empower software generalists to do specialist work
The competition for senior development talent remains intense, limiting how quickly financial institutions can scale innovation. While improving senior developers’ productivity is valuable, AI introduces a new advantage: enabling junior and mid-level developers, and even technically aligned business analysts, to take on more complex work.
With Genesis, AI delivers well-structured, governable code, empowering less-experienced developers to assemble production-ready financial applications. This approach helps close the talent gap, speed up delivery, and maintain compliance and security at every stage.
Segment business problems and define success metrics
To capture AI’s full impact, financial institutions should:
- Align AI strategies with business objectives: distinguish AI for software development from AI for decision-making.
- Prioritize high-value challenges: modernizing legacy tech, reducing vendor dependencies, and managing EUC sprawl.
- Accelerate the full software lifecycle: not just development, but also testing, maintenance, and integration.
- Define measurable success metrics: including cost reduction, scalability, and risk mitigation, alongside productivity.
Genesis helps firms standardize the software development lifecycle (SDLC) through AI-powered automation, driving efficiency across all phases.
The future of AI-enabled software development in financial services
AI is evolving rapidly — and no single model will remain dominant. Financial institutions must take a model-agnostic approach, integrating AI in ways that allow flexibility while maintaining compliance.
Instead of depending on a single provider, Genesis delivers domain-specific AI integrations designed for accuracy, auditability, and security within financial frameworks.
As AI continues to mature, developer productivity will remain the foundation of modernization. Yet the next phase of AI-driven software development will move beyond individual output — toward agent-driven AI, where intelligent bots assist in every phase of design, testing, and deployment.
Even in that future, AI in financial markets must operate within strict guardrails to ensure resilience, compliance, and auditability.
Building with Confidence
At Genesis, we’re committed to enabling a secure, AI-native development platform designed specifically for the financial industry. By embedding AI within a trusted framework, institutions can innovate confidently — balancing speed, governance, and strategic growth.
Ready to harness AI for innovation with the right guardrails?
Book your 30-minute, 1:1 meeting to explore how the Genesis Application Platform can help solve your business challenges.
