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How Banks Can Overcome Legacy System Challenges to Adopt AI Successfully?

Posted on: December 13th 2024

The banking industry is at a crossroads. On one side, there’s the promise of Artificial Intelligence (AI) and Generative AI (GenAI)—technologies that can transform operations, enhance customer experiences, and drive innovation. On the other side, there’s the weight of legacy systems, some decades old, that slow progress and create friction in AI adoption.  So, what should banks do to overcome the burden of legacy technologies in their AI adoption journeys? Let’s start exploring.

Legacy Systems: A Challenge to Innovation

Consider these statistics: According to a recent PYMNTS Intelligence Report in collaboration with Galileo, 75% of banks struggle to implement new payment solutions due to outdated infrastructure, and 59% of bankers see their legacy systems as a significant business challenge, a “spaghetti” of interconnected but antiquated technologies.  It’s a stunning statistic that nearly three-quarters of banks globally continue to run on legacy core banking systems, which fail to deliver on transformation goals. But AI in banking is a trend whose time has indeed come. International Data Corporation (IDC) expects financial services to spend the most on AI solutions during the 2024-2028 forecast period, accounting for over 20% of all AI spending.

The Growing Pressure for AI Adoption in Banking

AI is reshaping banking, from enhancing customer service through virtual assistants to accurately detecting fraud. However, not all banks are ready to fully embrace AI due to the limitations of their existing systems.  Here’s why banks must act now:
  • Customer Expectations: Consumers increasingly expect faster, more personalized services that AI can provide.
  • Competitive Edge: Banks using AI can outperform competitors in areas like predictive analytics, customer insights, and risk management.
  • Operational Efficiency: AI can automate repetitive tasks, reduce errors, and drive down operational costs.
But legacy systems often stand in the way.

Legacy Systems Constitute a Significant Speed Bump

For many banks, legacy systems—older, inflexible technologies—form the backbone of daily operations. While they have served their purpose over the years, they are inadequate for the demands of modern AI solutions. Some critical challenges posed by legacy systems:
  • Data Silos: Legacy systems often store data in fragmented formats, making it difficult for AI algorithms to access and analyze.
  • Limited Scalability: These systems were not designed to handle the volume or speed required by AI-driven processes.
  • Inflexibility: Legacy software can’t easily integrate with newer technologies, creating roadblocks for innovation.
How can banks update their infrastructure to leverage AI’s potential?

Navigating the Legacy Barrier

The solution lies in a carefully planned, phased strategy that reduces disruption while establishing a foundation for successfully integrating AI.

1. Prioritize Data Modernization:

Data is the fuel that powers AI. Without clean, accessible, and well-structured data, AI models can’t perform at their best. For banks, this means taking steps to modernize their data infrastructure.
  • Centralize Data: Avoid siloed data systems and create a unified data lake that allows AI models to access information from all parts of the business.
  • Improve Data Quality: Cleanse, normalize, and structure data to ensure it’s usable by AI algorithms.
  • Embrace Cloud Solutions: Leverage cloud computing to store and process vast amounts of data at scale, enabling faster and more efficient AI models.

  1. Gradual System Integration and Upgrades:

Instead of attempting to replace legacy systems all at once, banks must take a more gradual approach that enables them to integrate AI while improving their infrastructure.
  • Modular Upgrades: Focus on upgrading specific parts of the legacy system that will immediately impact AI adoption (e.g., core banking or transaction processing systems).
  • Hybrid Solutions: Implement hybrid architectures that combine existing legacy systems with modern AI tools, allowing both to coexist while the bank transitions.
  • API Integration: Use Application Programming Interfaces (APIs) to connect legacy systems to newer AI platforms, enabling smoother data flow and better interoperability.

  1. Invest in Talent and Culture:

Adopting AI isn’t just about technology; it’s also about people. Banks must invest in the talent and culture required to make AI successful.
  • Upskill Existing Workforce: Train existing employees to help them understand AI’s potential and integrate it into their workflows.
  • Hire AI Experts: Bring in data scientists, machine learning engineers, and other AI specialists who can bridge the gap between traditional banking processes and new technologies.
  • Foster an Innovation Mindset: Cultivate a culture that embraces change and encourages experimentation with new AI-driven approaches to banking.

  1. Start Small, Scale Gradually:

When implementing AI, banks should start with pilot projects that can demonstrate value quickly and then scale from there.
  • Pilot Projects: Launch small, manageable AI initiatives in areas with clear ROI, such as fraud detection, customer support chatbots, or credit scoring.
  • Iterate and Scale: Use the results from pilot projects to refine AI strategies and apply them to other business areas.

  1. Collaborate with Tech Partners:

Banks can enhance their AI implementation by partnering with technology providers and consultants.
  • Tech Partnerships: Work with AI-focused firms to integrate advanced technologies more quickly and cost-effectively.
  • Collaborations with Fintechs: Partner with startups specializing in AI solutions tailored for the banking sector.
Banks must prioritize data modernization, integrate AI gradually, invest in talent, and scale their AI initiatives thoughtfully. By taking a step-by-step approach, they can harness AI’s full potential while overcoming legacy system challenges.

The Path Forward

The journey to AI adoption will be challenging, especially for banks burdened by legacy systems. However, the benefits of AI—improved efficiency, better customer experiences, and a competitive edge—make the investments worthwhile. At Straive, we help banks unlock the full potential of AI and GenAI. We create intuitive interfaces that seamlessly integrate AI across operations. We build robust AI infrastructure for optimized model deployment, ensuring smooth integration within a data environment.  Moreover, Straive’s AI Training, Mediation, and Monitoring solutions enhance model outputs through continuous refinements.

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