How AI/ML Can Help Banks Bridge the Human-Digital Divide


When applied correctly, AI/ML and other technologies can take banking back to its roots to solve challenges and help identify new business opportunities.

Small-and-medium business (SMB) is a segment that has traditionally relied on a certain level of human touch for its banking needs. In a turbulent economy, with small business confidence at a low, banks have a ripe opportunity to grow their market share in the SMB segment by turning data into real-time insights. By leveraging artificial intelligence and machine learning (AI/ML), banks can gain a contextual understanding of SMB customer needs and offer tailored products, services, and advice in real time. At a time when SMBs need hyper-personalized, actionable advice to withstand economic headwinds, AI-led insights can lead to healthier business relationships.

Today, most banks operate data-driven business models. Yet, more proactive, untapped opportunities exist to take advantage of data inside and outside of their organizations, including first-party transactional and behavioral data from the banks’ systems and third-party data. AI/ML technology offers banks a chance to narrow the human-digital divide, strengthening human relationships by providing new ways to form trusted, personalized partnerships with their SMB clients.

AI makes banking more human and personalized — not less.

Most banks today offer similar suites of services to SMB customers, with limits to how much they can compete on interest rates. But a bank that collects, analyzes, and applies multi-channel data insights in real time can deliver personalized customer experiences that achieve lasting emotional brand loyalty.

AI/ML-driven insights can help banks gain a contextual understanding of SMB customers’ needs and equip relationship managers with timely and actionable insights to address customers’ needs in the moment, such as suggested action for a cashflow exception. In addition, real-time insights, such as an increase in a customer’s likelihood to churn, can help banks take preventative action and eliminate attrition blind spots. Data-driven alerts such as upcoming loan renewal can help the relationship manager to proactively engage with their customers.

Further, SMBs are most likely to remain loyal and invested when they are gaining business value from their banking relationship. When relationship managers form genuine partnerships with customers based on personalized understandings of their needs, they feel understood and valued.

See also: Future-Proofing Banking: 4 Steps Toward a Tech-Driven Customer Experience

AI-powered insights can amplify the local knowledge of community banks  

Community banks and credit unions have the advantage of knowing their SMB customers and regional business trends through the human touch, while big banks may offer a more comprehensive banking experience with an array of deposit accounts, loans, insurance, and wealth management insights. The local knowledge of SMBs augmented with the power of data-driven insights based on enterprise and external data can be a winning combination for these banks.

With AI/ML-driven insights, small banks can translate the macro-business environment and a cash flow-based understanding of their customers’ needs into personalized recommendations and guidance. For example, it may provide a personalized recommendation for suitable financing solutions to meet short-term liquidity and long-term growth requirements. By using their nimble size and localized knowledge to their advantage, smaller banks have the ability to build stronger customer relationships that larger institutions can’t. Coupled with AI/ML, it’s game-changing.  

AI can enable banks to identify SMB needs faster and improve their ability to meet these needs

True relevance is derived from understanding SMB customers’ needs as they arise. Combining data from internal channels and relevant third-party sources, AI/ML-driven models can surface intent signals and respond to customer needs in the moment with relevant and appropriately timed recommendations, advice, and experiences.

For example, identify intent signals from first-party data and third-party B2B intent data and analyze the same to create ranked leads. Using these leads, AI/ML models can create contextual SMB banking product and service recommendations with a score so that the recommendations are personalized for the customer and actionable for the relationship manager.

Banks can evolve from being banking service providers to trusted business partners for SMBs

Ecosystems to offer services beyond traditional banking can help create new propositions and offerings. Such offerings can be provided as a service to target customers’ business needs in addition to their banking needs. This can help banks provide greater value to their customers and become their trusted advisors. Examples of value-added services include invoices and accounting software.

Banks can also help their customers with investments in green businesses and provide green loans such as loans for solar and other renewable energy projects, building retrofits, and purchasing carbon offsets. Further, banks can empower their business customers with tools and knowledge of their environmental impact. For example, these tools can include an emissions calculator provided as a self-serve capability to estimate their emissions for internal or external reporting.

Having focused my career on technology that powers the banking industry, I have seen how AI/ML can empower human interactions with insight, understanding, and actions. These technologies can take us back to the origins of local banking, re-strengthening the real human connections between a business owner and its banking partner, but at the scale of our digitally connected world.

Ashvini Saxena

About Ashvini Saxena

Ashvini Saxena is VP and Head of TCS Digital Software and Solutions.

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