Long known as technology laggards, commercial banks are making up for lost time by accelerating digital adoption. A case in point is the increasing use of artificial intelligence (AI) solutions in commercial banking operations such as customer service. Chatbots and virtual assistants are enhancing service interactions by answering routine queries, to minimise the workload of human agents and wait times for customers.

But these tools are not only about efficient resolution—based on their conversations with customers, combined with machine learning (ML) insights into customer preferences and behaviour, the tools can identify unmet needs and cross-sell/up-sell products to fulfil the same.

In a real-life example, a bank used an ML-based product recommender to aggregate various customer data points and other intelligent tools to finely segment customers, rate each segment’s likelihood of buying different products, and assess each customer’s inclination to purchase a particular product based on its recommendation. Through this, the bank evolved the role of its virtual assistant from merely answering customer queries to assessing customers’ interest in the most highly recommended products, with the goal of driving almost one-third of revenues through its AI-powered chatbot within three years.

Chatbots can also summarise customer interactions to create a permanent knowledge base that human agents can access to understand individual customer contexts and improve service levels. When Natural Language Processing capability—the technology behind chatbots and smart assistants—is integrated with Customer Relationship Management, it improves the accuracy and relevance of service responses.

Today, AI solutions can automate a range of commercial banking processes and transactions to reduce friction and improve efficiencies. Take customer onboarding, which typically involves a lot of paperwork and procedures for ensuring KYC compliance, establishing business credentials, etc. AI can automate a number of operations, including secure identification, credit evaluation, and customer profiling, to improve onboarding speed and quality. Intelligent tools can offer new customers personalised support to ensure the relationship gets off to a smooth start.

 Empower service agents

What’s more, by automating tedious tasks, including document verification and analysis, data entry, and reporting, AI technologies free up time that bank staff can devote to solving complex customer issues and building relationships. Further, AI solutions empower agents with key insights, enabling them to issue faster loan approvals, personalise interactions, and resolve pain points quickly, to name a few.

A major use case for AI and ML technologies in commercial banking is fraud detection. The solutions can analyse enormous quantities of data in real time to identify suspicious patterns that may be indicative of fraud or other types of nefarious activity. When bank staff are alerted to a potential threat, they can immediately warn their clients and take early action, including freezing the impacted accounts, to prevent losses.

Think it through

The emergence of sophisticated AI agents is expanding the scope of customer service beyond simple account queries to include even complex financial advice delivered in real time after considering the customer’s present financial position and immediate context. But while there is a strong business case for implementing AI to improve customer service operations, commercial banks should go about it thoughtfully.

Firstly, there needs to be a clear plan, along with defined goals that are aligned with the bank’s overall business objectives. AI tools need robust, highly scalable cloud-based infrastructure to support their data and computation requirements. Banks should ensure that the data fed to AI, or to train AI models, is of high quality—clean, complete, consistent, accurate, and free of bias—to secure good, reliable outcomes.

Also, they need to adopt Responsible AI—artificial intelligence based on the principles of trust and human-centricity that prioritises customer safety and rights (such as privacy), and adheres to legal, social, and ethical requirements. Setting up robust data management and governance frameworks would go a long way towards ensuring data integrity and compliance. Last but by no means least, banks need to build the right talent and skills across data science, AI engineering and analytics, as well as a culture that promotes Responsible AI practices, continuous learning, and an AI-first mindset.

(Gada is Executive Vice President and Global Head of Banking & Financial Services, Infosys. Views are personal.)