The End of Average: AI Is Rewriting the Rules of Digital Banking CX: By Alex Kreger
In 2024, nearly every major bank either launched or upgraded an AI-driven virtual assistant to handle customer inquiries, marking a significant shift in how service is delivered. These AI assistants (e.g., text-based chatbots and increasingly voice bots) became front-line support, capable of resolving many issues that used to require a phone call or branch visit. Industry analysis estimated that bank chatbots will save around $7.3 billion globally in customer service costs, roughly $0.70 per interaction handled by AI instead of a human. Moreover, customers who get quick, effective answers on their own tend to be more satisfied. A survey by Zendesk in 2024 found 77% of consumers say AI is helpful for simple issues, indicating that when AI works, it meets customer expectations.
For large-scale deployments, SKT also offers an API and Docker-based container options that run on-premises. That’s a big deal for banks, hospitals, and public agencies that need to keep sensitive data in-country. AI-driven document metadata extraction is both replacing the old—bringing much-needed relief to pain points of legacy systems—and enabling the new, opening frontiers for intelligence and automation. These implementations show how GenAI isn’t just about cost-saving – it’s about reimagining how services are delivered. Use AI to analyze and react to local trends to keep in-demand inventory in stock as well.
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One of the clearest impacts of AI is the proliferation of virtual assistants and chat interfaces that empower customers to get things done on their own. From simple tasks, like checking a balance or paying a bill, to more complex actions like applying for a loan, AI has made self-service more convenient. A survey by Zendesk noted that self-service adoption in financial services has grown 5.4× in recent times, as banks provide more useful tools like searchable knowledge bases and intelligent chatbots.
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Seven billion parameters may not sound huge by 2025 standards, but that’s the point. Smaller models are faster to load, use less power, and are cheaper to fine-tune, advantages that matter for mobile apps, small businesses, and research labs. Generative AI is not just a technological advancement – it’s a strategic enabler for innovation, agility and competitive differentiation in banking and telecommunications.
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It has already been used by more than 160,000 organizations to build agents, said Charles Lamanna, Microsoft’s Corporate VP of Business and Industry Copilot, in a March update. More than 400,000 custom AI agents have been created in the previous quarter alone, he added. It’s still not completely foolproof, she says, and there is still a human involved to review the final document and tailor it as needed. But when the tool launched last year, the client saw a multi-day workflow reduced to a few hours.
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- In other words, AI can not only extract basic fields but also interpret and generate insights or predictions from documents—something legacy OCR or rule-based systems could never do.
- There is a bit of “agent washing” happening in marketing departments right now.
- “We have also integrated it with a translation service, so we can translate email text or document attachments,” Nguyen says.
- Another development was AI-driven image recognition for bill payment or check depositing, simplifying processes for those who struggle with manual input, just snapping a photo and letting AI do the work.
In other words, AI can not only extract basic fields but also interpret and generate insights or predictions from documents—something legacy OCR or rule-based systems could never do. From a qualitative standpoint, AI-powered engagement in 2024 meant banks could anticipate customer needs instead of waiting for customers to reach out. Predictive models identified which customers might be shopping for a home, need a savings boost or be at risk of overdraft—and then proactively offered assistance or deals. This not only drives sales; it also improves the customer’s financial health, thus building goodwill. Nguyen recommends that companies looking to roll out agentic AI for customer service start standardizing data and systems as soon as possible.
However, the more human-like and nuanced AI agents become, the more reliant customers will become on AI agents. Even still, financial institutions need to remain human-centric, especially for emotionally fraught transactions such as buying a first home or investing for retirement. Another important use of AI is identifying fraud, especially when considering consumers lost more than $10 billion to fraud in 2023.
The project ran on SKT’s TITAN supercomputers, and the company controlled every layer, from tokenizer to inference. For example, manual data entry often has ~1% or higher error rates in practice. In accounting, 18% of professionals reported making financial errors daily at their firms. Humans also spend a lot of time on repetitive entry—over 40% of workers say at least a quarter of their week is spent on data entry tasks.
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The biggest challenge, he says, isn’t the agentic technology but the lack of company-wide standardized processes. That’s why, for its initial deployment, Bosch Power Tools is using agentic AI to assist human agents, not replace them — and is keeping humans in the loop as a safety precaution. But the latest evolution is where the coding system can generate an entire application without a human touching the code at all. It can use APIs and provision infrastructure — and there are several areas where Abnormal is already using such tools. For example, the company uses ChatGPT and reasoning models for architecture and design. A GitHub survey of 2,000 developers in the Brazil, Germany, India, and the US found that 97% were using AI coding tools by mid-2024.
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As a result, BofA saw users logging in on average more than once per day, and digital interactions overall jumped by double digits year-over-year. Google’s Vertex AI is just one of many AI agent building platforms that’s trying to make it easier to build and deploy AI agents. In April, Google also announced that its Agentspace platform, first launched in December, now has a no-code agent designer and pre-built agents for tasks like deep research and idea generation. “Agentic technology is supercharging the way we can deliver value for customers,” says Merlini. “I look at it as a new category of software.” It goes far beyond what can be accomplished with a simple chatbot interface, he says. No longer limited to answering questions, AI agents can carry out tasks on our behalf — sometimes extremely complicated tasks that require extensive interactions with other agents and systems.