The past year has been a mixed bag for the banking industry. While rising interest rates seemed to improve margins and revenues, the evolving macroeconomic conditions, such as inflationary pressures, inadequate growth, and emerging geopolitical uncertainties, are concerning.

Through it all, banks have persisted in their digital transformation journey, reaching varying levels of maturity based on their individual pace and areas of focus. But the distance between progressive banks — players who have proactively made efforts to digitise, and laggards — financial institutions yet to make significant progress in their digital evolution — has widened. And the key difference is the former’s approach to digital transformation programmes.

A joint report by Qorus, a Paris-based non-profit specialising in the financial services ecosystem, and Infosys Finacle revealed that only a little over 10% of senior banking executives are satisfied with the scale of their banks’ digital transformation and that the outcomes from their programmes were as expected.

On the other hand, the industry has been at a cusp of profound technology shifts with each new wave of advancement. Most recently, with generative AI (Gen AI). The integration of Gen AI is poised to widen the gap between industry leaders and laggards. Those embracing and effectively leveraging this technology will gain a significant competitive edge through enhanced operational efficiency, advanced analytics, and more personalised customer engagements. Those slow to adopt may struggle to keep pace with evolving industry standards and customer expectations.

Unlocking the secrets to a successful digital transformation: How Gen AI can help

Even predating the emergence of Gen AI, banks that outperformed the industry in their digital transformation journey adopted a comprehensive strategy emphasising five key areas. First, they have an unflinching focus on superior digital customer engagements.

Second, they continuously innovate to create new value propositions and remain competitive by participating in platform-centric business models — models with predetermined standards and operating procedures that enable interactions and collaboration at scale.

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Third, they digitise and automate ubiquitously to achieve operational efficiency and cut costs.

Fourth, they leverage technologies like cloud, API, and AI to unlock new possibilities. Finally, they invest in building talented teams and a purpose-driven culture to harness their organisation’s true potential.

Digital transformation requires both speed and scale because it involves fundamental changes to how financial institutions operate, interact with customers, and leverage technology. Such a transformation can include everything from automating processes, developing new products and services, and reimagining customer experience. By moving quickly and at scale, businesses can achieve digital transformation more effectively and efficiently. And here is where Gen AI’s integration into the digital transformation journey of banks can have extensive implications.

Generative AI ticks several boxes for banks undergoing critical digital transformation. Aside from its tremendous potential in helping transform the IT aspects of banking, generative AI also holds much promise in accelerating the development of new offerings, enhancing customer engagement and service operations, and personalising sales and marketing.

Augmenting IT engineering with Gen AI

The far-reaching implications of Gen AI are evident across operations, advanced analytics, and customer engagements. But Gen AI can play a significant role in transforming aspects of software engineering and in boosting the outcomes of digital transformation programmes. Banks can leverage different models of this essential tool, Gen AI, to evolve differentiated approaches and set new precedents.

Here are some areas in IT engineering that Gen AI can supercharge:

  • Making code generation more efficient

Gen AI uses transformer models, which are pre-trained language models that can be refined to automatically generate code in multiple programming languages, speeding up software development. Code snippets, templates, and even complete programmes can be developed using high-level descriptions or natural language specifications. This efficiency can significantly reduce coding time and effort while guaranteeing it is consistent and error-free.

The potential of ChatGPT in efficient code generation is already being tested at major international banking institutions such as Goldman Sachs.

  • Bolstering testing process

To reduce human dependencies, especially in test-case generation and test script creation, generative AI can analyse the codebase and develop comprehensive test suites to benchmark software quality and minimise the probability of human error in testing. Here, generative adversarial networks (GANs) and pre-trained transformers work well in tasks like data generation or content creation.

  • Improving bug detection and correction

Deep learning models such as long short-term memory (LSTM) can be used with transformers to analyse code and identify and correct bugs to address common issues. Generative AI can also suggest solutions for these issues for developers and support them in keeping their applications reliable and secure.

  • Detecting anomalies real time

Banks are increasingly under pressure to implement tighter fraud detection, security monitoring, and system health check measures. With Gen AI, detecting anomalies in IT infrastructure can happen in real time by learning normal behavioural patterns and quickly identifying deviations using deep learning models such as autoencoders.

  • Mirroring real-world testing with synthetic datasets

Banks are bound by stringent data privacy and security regulations and cannot easily extend their datasets to different requirements for testing. In such an environment, synthetic datasets that mirror actual transactions but are anonymised are a boon.

Generative AI can create synthetic datasets with the help of GANs and variational autoencoders and make these available in the software testing, model validation, and quality assurance processes. The distinct advantage of synthetic data is that it can be stress-tested for various scenarios.

  • Automating document generation

Generative AI can automatically generate technical documentation, user manuals, system architecture diagrams, and network configurations. These are among the typically massive documentation efforts necessary in a bank’s IT operating environment that needs to be up-to-date and consistent.

Transformer models are exceptional in generating human-readable text and can be fine-tuned for specific documentation generation. With generative techniques and custom templates, it is very easy to produce structured documents readily.

  • Forecasting to help predictive maintenance of systems

Preventive measures can go a long way in minimising downtime and creating operational efficiencies for banks. For example, Gen AI can predict when a particular IT infrastructure component, such as a server or networking equipment, might fail. Time-series forecasting models and AI variants such as LSTM can be leveraged to analyse historical data and patterns and forecast maintenance needs.

The promise of generative AI in banking

In the digital evolution of banking, generative AI emerges as a transformative force. While I have spoken about just one aspect of Gen AI in banking, which is about augmenting software engineering, success for banks continues to hinge on embracing a comprehensive digital strategy. And generative AI is set to accelerate this journey. Adopting Gen AI is a strategic imperative, and its transformative power can propel banks into a future characterised by unparalleled customer centricity, heightened efficiency, continuous innovation, and enhanced resilience in a dynamic digital landscape.

K R Venkatraman is Head Product Architecture, Infosys Finacle

K R Venkatraman (KRV) brings over 28 years of global technology experience in international banking and technology transformations. His experience spans multiple core banking vendors, start-up ecosystem and experience with clients through their digital transformation journey across multiple markets globally.