October 1, 2024
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Banks have spent years investing in digital tools, automating internal processes, and shifting towards more agile operational models. Yet, despite these efforts, many financial institutions are still struggling to bridge the gap between technology, analytics and business operations. This disconnect has led to inefficiencies, poor customer experiences, and slow-to-respond business models.
And with the rise of generative AI, the stakes have never been higher. A McKinsey report estimates that generative AI could add between $200 billion and $340 billion in value to the global banking sector. Yet, despite this potential, the pace at which the banking industry is adopting AI remains slow.
Over the years, the banking industry has implemented agile methodologies, adopted cloud technologies, and shifted to product operating models. We saw digital banking rise with mobile banking and UPI but leading institutions like Goldman Sachs and Citigroup are already one step ahead with AI-driven tools to automate labour-intensive processes. In our latest article, we explore the most common use cases of Generative AI in Banking.
5 Generative AI Use Cases in Banking
For banks that are yet to catch up, the following Generative AI use cases could significantly boost their overall operations:
1. Product, Service Design and Innovation
Traditional methods of research and development are slow, often relying on manual data collection and feedback loops that can take months. And, identifying the right features or services that meet customer needs in real-time can be challenging without accurate and actionable insights.
Generative AI could help banks approach product and service innovation by streamlining research and speeding up development. It accelerates the process of identifying new features and opportunities within existing products. Generative AI models can analyse massive amounts of data- ranging from customer behaviour and feedback to market trends and competitor movements. This can help the banking industry design and implement new services or features much faster.
The best part about Generative AI in Banking is its ability to process and synthesise customer feedback in real-time through Voice of Customer data to immediately identify gaps or opportunities in their services. These insights help banks develop products that are more aligned with customer expectations to improve customer satisfaction.
2. Marketing and Sales
Marketing and sales teams are often overwhelmed by the need to deliver personalised experiences at scale. Traditional ways of content creation, lead management and customer engagement are resource-intensive and may not be as effective in driving higher engagement or conversion rates.
Generative AI addresses these challenges with hyper-personalised marketing and sales strategies at scale. By analysing customer data such as transaction history, behaviour patterns, and demographics, Generative AI can help teams create targeted and personalised marketing content that resonates with each customer. This means the right message reaches the right customer at the right time with minimal human effort.
For Relationship Managers (RMs), AI-powered agent copilots provide real-time product recommendations and next-best offers based on customer needs, preferences and user behaviours. This not only helps RMs deliver more relevant and efficient service but also frees them from time-consuming tasks, so they can focus on higher-value interactions with clients.
Sales teams can also use AI-driven sales copilots which automate routine tasks such as handling queries, responding to emails, sending follow-up reminders and providing smart nudges. These tools optimise sales processes by offering data-backed insights into customer needs and engagement patterns. With AI-powered self-service insights, sales teams can access performance data, identify key success metrics and adjust their strategies accordingly.
3. Underwriting and Onboarding
Traditional underwriting and onboarding processes have always been slow, resource-intensive, and prone to human error. Credit managers must sift through large volumes of unstructured data such as financial statements and loan applications to make informed decisions. Generative AI can help in the underwriting process by analysing unstructured data such as loan applications, bank statements and customer correspondence. AI provides real-time insights that help credit managers make quicker and more informed decisions by flagging potential risks and assessing creditworthiness.
For onboarding, Generative AI enhances the customer experience by creating personalised onboarding kits. These kits which combine text, video and audio can provide a seamless, engaging experience for new customers, helping them understand the products, services, and processes. This personalised approach not only increases customer satisfaction but also ensures that customers feel supported and valued from the moment they join.
4. Customer Service and Experience
Just like every other sector, exceptional customer service is imperative to maintaining customer loyalty but managing customer queries in a timely and efficient manner can be challenging. Traditional customer service channels are often overwhelmed with requests which leads to delays and frustration. Customers now expect 24/7 support across multiple channels, making it difficult for banks to keep up.
Generative AI can improve customer service in banking by providing 24/7 support through AI-powered chatbots and virtual assistants. These AI agents can handle a broad range of queries, from basic account inquiries to more complex requests such as loan applications or investment guidance. By automating responses, AI helps banks offer instant support, reducing customer wait times and improving overall satisfaction.
But that’s not all, Generative AI solutions can also help banks to personalise the customer journey by analysing spending habits, savings goals and investment preferences. Based on this data, AI delivers tailored financial advice, helping customers make informed decisions about their finances. For example, Generative AI can offer personalised budgeting tips, recommend suitable products or provide financial wellness advice to improve long-term customer outcomes.
5. Collections, Recovery and Attrition Control
Banks often face difficulties managing collections, recovering overdue payments and preventing customer attrition. Traditional collection processes are often rigid which results in impersonal communications that may not be effective in encouraging customers to repay. Banks struggle to detect early signs of attrition with manual human efforts which leads to missed opportunities to retain customers.
Generative AI can improve collections and recovery efforts by automating the creation of personalised repayment messages and offers. AI analyses customer data, including transaction history and payment behaviour to tailor messages that are more likely to resonate with each customer. By offering embedded repayment options and flexible terms, Generative AI makes the payment experience smoother, increasing the likelihood of timely repayment while maintaining a positive relationship with the customer.
For attrition control, Generative AI can detect early warning signs of potential churn by analysing customer behaviour and engagement patterns. By monitoring changes in usage, transaction activity, and interactions, AI identifies customers who may be at risk of leaving. Once flagged, AI can help banks create personalised win-back offers, such as exclusive deals, discounted rates or loyalty incentives, to re-engage at-risk customers.
NexGen Labs: Your Partner for Generative AI
Unsure which Generative AI solution could be the right choice for your workloads? Partner with NexGen Labs to learn how to adopt the right Generative AI Application for your banking and financial services, ensuring scalable infrastructure and expert guidance. Speak to an expert to learn how we can help you implement Generative AI solutions in Banking.
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FAQs
What is Generative AI in banking?
Generative AI in banking refers to AI technologies that can generate new data or content by learning from existing data. It helps banks improve decision-making, automate processes, and create personalised experiences by analysing large datasets such as customer interactions, transaction histories and financial reports.
What are the use cases of Generative AI in banking?
Generative AI in banking has several use cases, including product and service innovation, marketing and sales optimisation, underwriting and onboarding automation, enhancing customer service, and improving collections and attrition control. It helps banks streamline operations and deliver more personalised services.
How can Generative AI help banks improve customer service?
Generative AI can improve customer service by providing 24/7 support through AI-powered chatbots and virtual assistants. These tools can quickly resolve customer inquiries, provide tailored financial advice, and ensure a seamless customer experience across multiple channels.
How does Generative AI enhance marketing and sales in banking?
Generative AI helps banks personalise marketing and sales strategies by analysing customer data to create targeted content. It automates routine tasks for sales teams, provides real-time product recommendations, and delivers smarter customer engagement, improving conversion rates and operational efficiency.
How does Generative AI assist in underwriting and onboarding in banking?
Generative AI speeds up the underwriting process by analysing unstructured data such as loan applications and financial statements, helping banks make quicker, data-driven decisions. In onboarding, it personalises the process through dynamic kits that enhance customer engagement and streamline account setup.
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