Generative AI: Redefining the Banking landscape through innovation

The financial services landscape has been significantly influenced by Artificial Intelligence in the last 18 months. Generative AI is emerging as one of the most revolutionary aspects of our time. Bolstered by sophisticated foundation models such as Chat GPT-3 and GPT-4, it can reshape the way financial institutions operate and interact with customers.

Generative AI refers to a class of models that can create content such as text, images, or audio, based on patterns and examples learned from vast datasets. Unlike traditional AI models with specific tasks, it exhibits human-like creativity by producing novel outputs.

Benefits through Generative AI

Generative AI’s share in the Fintech Market is expected to grow beyond $6.2 billion by 2032: A number of Fintech companies are already putting it to use in a wide range of functions including customer support, synthetic data generation, risk factor modelling, fraud detection, and more.

Analysis by the Infosys Knowledge Institute shows that an effective AI strategy can help enterprises collectively add nearly $467 billion in profit growth and improve user satisfaction. AI investments are set to grow exponentially, projected from $118 billion in 2022 to $300 billion in 2026.

Here, we explore some possible ways in which banks can test the transformative potential of Generative AI in their realm.

The power of Generative AI in Banking

Banking integrates human interaction and data leverage for services like retail and institutional finance, savings, investments, and lending, which rely on both technology and human expertise.  Generative AI can prove game-changing here.

However, its adoption will not be straightforward because of the highly regulated and complex nature of business, which makes banks cautious about change. The diversity of financial products, intricate contractual agreements, multiple outdated IT systems are also likely to be roadblocks.

Generative AI requires operational adjustments

In our experience, banks seek technology that enhances efficiency without replacing humans. This calls for significant operational changes and rapidly deployable, high-value solutions of the kind listed below:

  • Enhancing internal efficiencies: Banks often struggle with fragmented data across different business units, leading to inefficiencies and manual work. Generative AI offers a solution by connecting data from diverse sources, enabling real-time analysis for millions of customers, and facilitating personalised digital services. By streamlining operations and elevating internal efficiencies, banks can create an improved experience for their users.
  • Transforming customer experiences: The financial industry has already witnessed significant AI adoption with the early applications of chatbots and fraud monitoring over the last few years. Generative AI can tap into new territory and transform more aspects of banking. Advanced digital or virtual assistants with AI and natural language processing capabilities are set to play an increasingly significant role in consumers’ financial lives, offering predictive insights and hyper-personalised experiences.
  • Responding swiftly to fraud and cybersecurity challenges: As transaction volumes increase, detecting and preventing fraud becomes increasingly challenging. Generative AI can be invaluable in identifying and stopping fraudulent activities by analysing online traffic patterns and customer device fingerprints in real-time. By automating fraud detection and response, banks can offer new services to their customers and mitigate financial losses.

Risks of Generative AI

While Generative AI offers tremendous promise, it also introduces risks and headaches for the financial services industry such as inaccurate results (“hallucinations”), data privacy concerns, and biases built into AI models. Financial institutions must manage these risks and act responsibly in their AI deployment to ensure successful outcomes.

AI-first frameworks like Infosys Topaz ensure they tick all these boxes in building an AI-first core that augments the potential of individuals, communities, and enterprises.

Responsible AI Practices: a three-pillar framework and adoption opportunities

To fully explore the transformative potential of generative AI responsibly, financial institutions can trial a three-pillar framework that entails:

  1. Human-centric design, transparency, and auditability: in AI systems to ensure fair and accessible outcomes. Human oversight remains essential in decision-making processes, fostering trust and understanding in AI-driven solutions. Well-defined parameters can still lead to generative AI systems producing unexpected responses – the industry’s complexity makes it difficult to train situational scenarios for AI models.
  2. Strong foundations and governance: to ensure robust data privacy and infrastructure while choosing the right AI service and vendor. Prioritising platform flexibility will help them adapt to evolving ecosystems. Establishing a comprehensive governance, risk management, and compliance reporting will help build trust and prepare for emerging regulations.
  3. Agile monitoring to ensure ethical implementation: and continuously monitor AI applications for emerging risks and maintain human override capabilities. Establishing clear criteria for testing and evaluating AI models will ensure unbiased outcomes. Start with low-risk AI applications and gradually expand to higher-risk ones, supporting responsible AI adoption through internal AI literacy and governance.

Having passed the adoption and exploration stages of Generative AI, financial institutions can go further to identify strategic opportunities for value creation.

Co-creation of own Generative AI solutions

For example, financial institutions and financial software companies can co-create and launch their own Generative AI products and position themselves uniquely by using their own datasets to train the model. The aim is to take the lead in conceptualising and executing product development, as well as offering Generative AI solutions to other businesses.

More established players could participate in fund-raising by FinTech companies that specialise in Generative AI-driven solutions, thereby influencing the trajectory of FinTech innovation and validating products across industries.

White Labelling is another option for banks and Fintechs to personalise existing business solutions using Generative AI. These partnerships and service standardisation could disrupt the vertical value chain allowing banks to delegate or procure services as needed.


To conclude Generative AI is a radical departure from traditional AI and offers financial institutions a terrific opportunity to create strategic value in a dynamic landscape by leveraging internal innovation, capital investment, white-label solutions, and more.

Financial institutions have poured billions of dollars into digital but rarely seen proportionate productivity gains – Generative AI promises to change that for the better.


Author Details

Anmol Jain

Anmol Jain is the Managing Director of Infosys Consulting´s APAC region. He has been with Infosys Consulting since 2013 and has a strong experience in leading large-scale core and digital transformation for financial services along with defining Business and Operating Model digital banks within Retail, Corporate and Private.

Maurizio Cuna

Maurizio is a recognised Infosys Consulting leader with two decades of experience in the banking and insurance industries, across Europe, USA, Asia and Australia. He has been with Infosys Consulting since 2016, driving retail banking transformations for major Infosys clients across Australia's east coast.

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