How Can Banks Balance GenAI Investments with Tangible ROI?

Posted on: January 16th 2025

As Generative AI (GenAI) accelerates its integration into the financial services industry, banks face a critical challenge: managing high initial investments and demonstrating clear, measurable ROI. Here’s how banks can balance their GenAI investments to ensure the technology delivers tangible value without excessive financial strain. 

Target High-Impact Use Cases

One of the most effective ways to manage GenAI investment is to begin with clear, high-value use cases. Banks should focus on areas that promise both immediate ROI and long-term impact. These include customer service, fraud detection, risk management, and regulatory compliance.

GenAI can drive personalization for customer-facing applications by creating tailored financial products or providing on-demand financial advice. By offering individualized experiences, banks can enhance customer loyalty and increase revenue. 

On the operational side, automating routine tasks like loan underwriting or fraud detection can streamline processes, reduce human error, and free up resources for higher-value activities.

Banks can achieve tangible benefits by focusing on use cases that directly improve operational efficiency or customer satisfaction while avoiding unnecessary complexity or risk.

Strengthen Data Foundations

Before deploying AI, banks must establish strong data infrastructure. GenAI thrives on high-quality, clean data, and banks must invest in building data platforms that ensure their information’s availability, consistency, and integrity. Investment areas must include:

• Developing centralized data hubs or lakes.
• Integrating various data sources.
• Ensuring the data is accessible and actionable for AI models.

Leveraging cloud-based solutions can significantly reduce the costs of data storage and processing while providing flexibility to scale GenAI applications. This cloud infrastructure makes it easier to manage data at scale and provides a cost-effective way to experiment with AI without heavy upfront investments in physical infrastructure.

A solid data foundation is essential for the success of any AI initiative. Without it, the risk of inaccurate predictions or suboptimal AI performance increases, ultimately diminishing ROI.

Adopt an Agile, Iterative Approach

Banks should adopt an agile approach to GenAI deployment to mitigate the uncertainty of new technology. Instead of committing to large-scale projects upfront, starting with small-scale pilots allows for testing, learning, and refinement. This approach helps identify potential challenges early on and enables banks to adjust their strategy before making significant investments.

With agile methodologies, banks can implement GenAI solutions in phases, constantly iterating on performance and adjusting according to real-time feedback. This flexibility reduces risk and ensures that the technology is adapted to the bank’s unique needs and goals, resulting in more effective deployment and higher returns.

By keeping projects small and manageable, banks can scale their AI initiatives gradually, optimizing investments and ensuring that each step delivers measurable value.

Foster a Digital-First Culture

GenAI deployment is not just about technology; it’s about transforming how the entire organization works. Successful integration requires a digital-first mindset across the whole workforce and includes upskilling employees, changing how departments collaborate, and fostering a culture of continuous learning.

As banks roll out AI tools, they must ensure employees can effectively interpret and work with AI-driven insights. They must also implement training programs that focus on technical skills (e.g., understanding AI models) and softer skills (e.g., collaborating with AI systems) to maximize the value of these technologies.

Furthermore, a digital-first culture fosters innovation and adaptation across the bank. It builds an environment where the organization sees AI not as a standalone tool but as an integral part of business processes, supporting teams across functions—from marketing to risk management. This broad integration enables AI to deliver maximum value across the entire organization.

Leverage Partnerships and Ecosystems

Building in-house AI capabilities can be expensive and time-consuming. To accelerate GenAI adoption while managing costs, banks should explore partnerships with external tech companies or FinTech startups. Banks can quickly and cost-effectively deploy AI-driven innovations with specialized expertise and pre-built solutions. 

Strategic partnerships provide access to cutting-edge technology without needing large-scale, in-house development. For example, banks can integrate third-party AI-powered tools for customer service automation or fraud detection, reducing development costs and accelerating time-to-market.

Moreover, partnerships can help banks access data ecosystems that enhance AI models. Open banking frameworks and shared data platforms allow banks to integrate external data sources, improving the accuracy and effectiveness of their AI-driven solutions.

By embracing these ecosystems, banks can innovate faster, reduce development overheads, and gain a competitive edge in the market.

Implement Ethical AI Governance

As GenAI becomes more pervasive, banks must prioritize ethical considerations. They must ensure that their AI solutions remain transparent, explainable, and free from bias. By implementing ethical AI governance, banks can build and deploy models responsibly, align with regulatory requirements, and protect customers’ privacy.

Banks should establish clear guidelines and processes for evaluating the ethical implications of AI applications. This includes assessing models for fairness, ensuring that AI-driven decisions (such as credit scoring) are transparent, and implementing robust mechanisms for auditing AI systems.

Responsible AI practices not only ensure compliance but also build trust with customers. Maintaining high ethical standards in AI deployment is essential for long-term success in banking, where consumer confidence is critical.

Measure and Optimize ROI Continuously

Banks must continuously track the ROI to justify the initial investment in GenAI and evaluate efficiency gains and revenue growth. AI’s potential to drive new revenue streams, such as through personalized offerings or improved customer engagement, should be measured alongside traditional cost-reduction metrics.

Advanced analytics can help banks monitor real-time performance, offering insights into AI applications’ performance. Metrics such as customer satisfaction, time saved through automation, and reductions in fraud or risk can help quantify AI’s value. Furthermore, by using dashboards and analytics platforms, banks can adjust their AI strategies dynamically, optimizing investments and ensuring that the technology delivers the expected benefits.

Banks can ensure that their AI investments align with short-term and long-term strategic objectives by adopting a comprehensive approach to measuring ROI.

A Pragmatic, Scalable Approach

Banks need a clear, strategic roadmap to balance GenAI investments with ROI. Best practices include:

• Starting with high-value use cases  
• Investing in strong data infrastructure  
• Adopting agile methodologies  
• Fostering a digital-first culture, and  
• Embracing partnerships  

GenAI offers immense potential, but realizing that potential requires a thoughtful, phased approach and knowledgeable partners. Straive designs and delivers cutting-edge GenAI solutions that seamlessly integrate into enterprise workflows. 

Our GenAI Design & Deployment services are focused on developing AI-driven solutions that generate high-value content, actionable insights, and innovative designs, all while ensuring that ROI requirements are met and exceeded for our clients.

About the Author

Sudhakaran Jampala

Sudhakaran Jampala is a Content Writer (Marketing) with Straive, specializing in the cutting-edge technology areas of data science, machine learning, and AI. He is fascinated by the art of storytelling, which transforms data into sparkling insights by revealing patterns and infusing visual narratives.