By: Charan Kumar Hiremath, Principal - Consulting, Banking & Financial Services

Introduction

The financial services sector has seen considerable digital transformation over the past few decades. However, evolving technologies and the rise of neobanks and digital-native FinServs have again put the traditional banking industry under immense pressure. Evolving customer expectations are further pushing banks to seek newer ways to transform operations, streamline processes, and significantly cut operational costs. Banks understand that implementing generative AI or Gen AI can help them accelerate this transformation.According to McKinsey[i], adopting Gen AI in banking can reduce operational costs by up to 25% by automating routine tasks and enhancing decision-making processes. It can also improve productivity by up to 30% by 2028[ii], driving new revenue opportunities. Armed with this information, 41%[iii] of financial service organizations are already spending up to 20% of their digital budgets on Gen AI use cases.These projections indicate a robust future for AI in banking, with significant cost savings and efficiency gains expected over the next 4-5 years. With this in mind, this article will highlight the most common challenges that plague banking operations and how Gen AI can remedy them.

Hurdles of traditional banking operations

Even after initiating their digitization journeys, many banks still struggle with operational inefficiencies, labor-intensive workflows, high costs, and regulatory complexity. Traditional banking processes often include redundant tasks that drain resources, slow operations, and increase expenses. Examples of these inefficiencies include manual data entry, extensive paperwork, prolonged approval cycles, and manually navigating intricate regulatory requirements. This operational lag stifles productivity and hampers the ability to deliver prompt and personalized customer service. The need for innovative solutions to streamline operations, manage regulatory compliance, and reduce costs has never been more urgent.

How can Gen AI overcome these challenges?

Over 50%[iv] of banks in the US and Europe have already implemented some form of generative AI or Gen AI in their operations, and 70%[v] of institutions plan to integrate Gen AI into their technology strategy. Here’s how Gen AI can help improve banking operations and how LTIMindtree is helping global institutions leverage this technology:

Boosting operational efficiency

By automating routine tasks, Gen AI in financial services can free up human resources for more strategic roles that require creativity and complex problem-solving skills. For example, Gen AI-driven process optimization can streamline workflows, reducing the time needed for task completion and minimizing human error. Tools like Enterprise Semantic Search can quickly locate relevant information across vast datasets, while Data Query Assist can generate accurate responses to complex queries, simplifying data management and retrieval. AI-powered automated credit scoring models can analyze extensive data inputs, including unconventional sources like utility payments and social media activity, to generate predictive credit scores. Loan processing automation allows banks to verify documents, assess creditworthiness, and approve applications in minutes, significantly reducing errors and enhancing customer experience.

Generative AI can also enhance workflow automation, ensuring repetitive tasks are handled swiftly and accurately. This level of automation speeds up operations and ensures consistency and reliability in task execution. By leveraging these capabilities, banks can significantly boost their operational efficiency, positioning themselves to better meet customer demands and regulatory requirements.

• Case study
  The largest American bank wanted to reduce the time its experts spent compiling research reports manually. The researchers spent up to 80 hours per report trying to maximize investment, but customers were unwilling to wait so long. LTIMindtree trained a Gen AI model on the previous year’s reports and used its natural language generation capabilities to automate report generation and summarization. This increased the research team’s productivity and reduced operating costs by 60%.

Reducing costs

One of the most compelling advantages of generative AI is its potential to reduce operational costs. Automating manual processes means fewer resources are wasted on repetitive tasks, resulting in increased cost savings. Gen AI applications such as automated customer service and smart document processing can handle large volumes of work without the need for extensive human intervention. For instance, Gen AI-powered chatbots and voice assistants can manage customer inquiries around the clock, reducing the need for a large customer service team. AI-driven targeted marketing can segment customer bases and deliver personalized campaigns, enhancing engagement while reducing marketing expenses. Similarly, dynamic pricing solutions optimize loan pricing based on real-time economic factors, further reducing operational inefficiencies.

Additionally, Gen AI-driven email assistance tools can draft and respond to emails efficiently, cutting down on time and labor costs associated with email correspondence. Document Generation tools can automate the creation of complex documents, reducing the need for manual drafting and proofreading. By integrating these AI-driven innovations, banks can significantly lower their operational expenses while maintaining high levels of accuracy and efficiency.

• Case study
  The contact center of a leading American bank was plagued with up to 85% call drop rates and high costs per transaction. Despite having spent significant cost and time training nearly 20K agents, this was detrimental to its customer service. We leveraged conversational AI to enable voice and chat self-service and Gen AI to enhance the agents’ data retrieval capabilities. Our Contact Center-as-a-Service (CCaaS) helped reduce cost per transaction by 90% and OpEx by USD 6 million/annum.

Elevating customer experience

Generative AI-powered systems can offer personalized interactions and swift responses, meeting the increasingly high expectations of modern customers. AI-driven customer support systems can provide instant assistance, addressing customer queries and issues without delay. These systems can learn from previous interactions, improving their responses and providing a more personalized service.

Sentiment analysis is another powerful Gen AI tool that banks can leverage to gauge customer sentiment and tailor their services accordingly. Predictive upselling models enable banks to recommend additional products aligned with customer preferences, enhancing satisfaction while driving revenue. By analyzing customer feedback and interactions, Gen AI can identify areas for improvement, helping banks enhance their services and foster stronger customer relationships. Leveraging Gen AI in financial services can elevate customer experience and give banks a significant competitive advantage.

• Case study
  The contact center of an American bank holding company spent significant time finding responses to complex customer queries. This led to poor customer and agent experience and increased call handling times. We implemented a Gen AI chatbot to search through structured and unstructured data across the knowledge base. Retrieval Augmented Generation (RAG)-based contextualized search was used to retrieve accurate responses. This helped improve customer satisfaction, increase the productivity of contact center agents, and reduce call handling time by 40%.

Conclusion

Generative AI holds immense potential to revolutionize banking operations. Many banks are already seeing how Gen AI can achieve significant efficiency gains and cost savings by automating mundane tasks, optimizing workflows, and delivering personalized customer experiences. The real-life examples also illustrate how AI can be successfully implemented to drive tangible results. Banks that can invest and scale their Gen AI use cases will be well-positioned to lead in the competitive financial landscape of tomorrow.For banks and financial institutions looking to explore the benefits of generative AI, the time to act is now. Start experimenting with Gen AI technologies, identify areas where Gen AI can add the most value, and develop a strategic roadmap for its integration. Doing this can help unlock new levels of efficiency, cost savings, and customer satisfaction.

References:

[i] McKinsey, The state of AI in 2023: Generative AI’s breakout year

[ii] EY, Unlocking the future of banking: the transformative power of generative AI,

[iii] McKinsey, The state of AI in early 2024: Gen AI adoption spikes and starts to generate value

[iv] McKinsey, Capturing the full value of generative AI in banking

[v] Forrester, Predictions 2024: Generative AI Transitions From Hype To Intent