Did you know? In 2022, over 98 million Americans, about 37% of the population, used a bank’s chatbot, according to the CFPB.
AI-driven customer services today don’t follow decision-tree logic. They understand intentions and problem-solve in real-time.
However, a wrong rate and fabricated fee might put the bank in violation of UDAAP laws. What you require is AI that works for banks. This technology would fetch information from bank data, make use of disclosures, and escalate the matter to people.
This article is bound to clarify all your doubts. You will learn the kinds of banking AI, compare the best providers, and get a purchase checklist.
Why AI Customer Support for Banks Matters Today
Customer support is one of the biggest costs in retail banking. Contact centers spend millions every year, yet customer satisfaction barely improves.
- Long queues: Simple queries like “what’s my balance?” consume the queue time and obstruct important queries such as fraud.
- Repeating of queries: Outdated IVR and rule-based chatbots lack context so that customers repeat the same information.
- High cost per customer contact: Much time is spent by agents in freezing the card, password reset, and status checks on accounts.
- Poor quality control: Most banks audit 1% to 2% of phone calls. The rest are not audited.
To get a better idea of how it works, see our guide on AI-powered banking customer experience.
What is AI Customer Support in Bank
AI in banking customer service is not one tool. These three roles are distinct from each other. Understanding them will help you choose the appropriate virtual assistant for your bank.
Resolve
Resolvers communicate directly with the customer without the participation of any human intermediary. A robust AI resolver could authenticate the customer, freeze a stolen credit card, provide calculations of refunds, and deliver confirmations.That is the key difference between a safe conversational AI for banking and a risky one. To get a better idea of how it works, see our guide on conversational AI for banks.
They get answers by querying your bank’s own APIs, not by guessing from general training data. That is the key difference between a safe conversational AI for banking and a risky one.
Assist
Not every call can be automated. Large transfers and complex disputes need human judgment and empathy. Agent assist tools run quietly in the background during live calls and chats.
They transcribe speech in real time, pull up the right standard operating procedure (SOP), and show client history. The agent no longer switches between five screens. Average handle time drops, and agents feel less stress.
Oversee
Oversee is the compliance layer. QA with the use of artificial intelligence checks everything, unlike the sampling process that is used. It also helps to monitor any missed legal disclosures in real-time, compliance with fair lending practices, and keeps a record of all the activities.
AI Agents That Resolve Customer Queries
Banks need AI agents that give accurate answers and handle sensitive data safely. Three platforms stand out for autonomous resolution and call deflection.
Thunai
We built Thunai as an agentic customer experience platform for financial services. The system deals with Level 1 tickets, which are easy and common. The system solves 78 to 80% of these tickets independently through voice, chat, and email channels and responds within 0.8 seconds.
The key is how Thunai gets its answers. It connects to legacy core banking systems through the Model Context Protocol (MCP).
Setup is fast. A bank can go live in under 48 hours. Thunai holds ISO 42001, SOC 2 Type II, and GDPR certifications.
Cognigy
Cognigy's strengths are voice automation and transformation of IVR. Retail banks utilize Cognigy to generate voicebots which will be responsible for balance check, branch calls, and card activation.
It can cope with thousands of calls. The disadvantage is that the implementation of projects takes two to four months, and it costs somewhere between $150,000 and $300,000 a year.
Kore.ai
Kore.ai provides BankAssist that is a product with over 200 pre-built retail banking workflows. It performs status checking for debit cards and bill payments.
Deployment is done within six to twelve weeks. Customization in mid-market banks poses challenges as changes in basic workflows require certified engineers.
AI Tools That Help Human Agents on Regulated Calls
Regulated banking calls carry legal risk. Agents must follow procedures, read required disclosures, and avoid unapproved promises. Agent assist tools work like live compliance guardrails.
Thunai
Thunai listens to the audio call, understands the requirement from the client, and displays the relevant response. It searches for the client’s previous interactions and recommends the right SOP. Later, Thunai prepares the call summary and updates it in your CRM.
Balto
Balto concentrates on closed loop prompts for regulatory compliance. Immediately after the call reaches the trigger point, an alert pops up. For example, when a borrower is asking for information on mortgage refinancing, the software triggers the required TILA disclosures.
Observe.AI
Observe.AI is built for QA and post-call intelligence. It will automatically redact PCI and PII from call recordings. The compliance scorecards that it generates are designed for banks. The compliance officer can see trends of risk in thousands of agents without the need for manual audits.
Cresta
Cresta caters to massive contact centers that operate with sales and retention as key objectives. The generative AI from Cresta learns from your best performers. It guides your agents live while cross-selling and stops account churns.
AI Built Into Contact-Centre Suites
Migration takes time and can be highly risky. The solution in such cases is to overlay the artificial intelligence on the contact center platform that you are using already. This will be the contact center automation without migration.
Thunai
Thunai works as flexible middleware. It is built on top of cloud contact center applications and CRM systems such as Genesys, Five9, Salesforce, and Zendesk. You save yourself from paying the price of full-scale migration, since your call routing and data exchange processes remain untouched. Thunai captures every voice signal, updates pipeline stages automatically, and builds one shared knowledge layer.
NICE
NICE builds its Enlighten AI into its own CXone platform. You get embedded compliance analytics and a built-in agent copilot. It fits very large national banks that already run on NICE CXone. The downside is vendor lock-in. Once you commit, moving away is hard.
Which Tool Fits Which Kind of Bank
Large national banks that have substantial budgets usually require extensive customization of their IVR systems. Such organizations prefer platforms like NICE or Cognigy, which involve substantial investments and time and require in-house engineering.
Regional banks, credit unions, and mid-market companies need something else. They expect prompt results from their projects.
Thunai works for every bank regardless of its size, but it is particularly effective when it comes to speed of implementation. The deployment of Thunai takes less than two days without any customization. It integrates with any core banking system through MCP.
Red Flags in Vendor Demos and Common Buying Mistakes
Buying banking AI takes a skeptical eye. A tool built for e-commerce can quickly cause compliance failures in a bank. Watch for these four red flags.
1. AI jumped into old databases: If the AI does not have an access to live and integrated view of the client, it is doomed to fail. They are unable to detect the payment fraud and provide outdated quotes since the database has not been updated.
2. No semantic banking guardrails: The CFPB has warned about the risk of being a violation of UDAAP if the information about fees or policies was incorrect. The good platform utilizes information from trustworthy data sources and refuses to provide the answers when confidence level is low.
3. Narrow focus on ROI: Many customers only pay attention to saving from reduced staff hours. However, there is much more profit to be gained. If the AI is able to process loan application for four hours instead of five days, the bank closes more deals.
4. Interface doom loops: The CFPB fines financial institutions when they force their clients into never-ending dialogue with an artificial intelligence. In case the artificial intelligence is unable to resolve the issue, then the latter has to transfer to a real person immediately.
To get a better idea of the numbers, see our AI in banking ROI benchmarks and deployment guide.
Bank AI Support Checklist
Use this checklist to verify compliance, security, and readiness before you sign a contract.
Ready to see AI resolve and assist in real banking workflows? Book a demo and get your questions answered by our team.
FAQs on AI Tools for Customer Support in Banks
What are the best AI tools for customer support in banks?
Thunai is capable of being used in order to conduct unified ticket deflection and agent assistance in live conversations. Balto is one of the best tools when it comes to mandatory prompts for compliance during the call. Observe.AI is a tool that is good in terms of post-call analytics and QA automation. Cognigy can be used for large-scale enterprises.
Can AI handle regulated banking queries safely?
Yes, where the architectural design has effective guardrails. The AI has to depend on receiving facts from trusted sources through secure APIs rather than the facts within its memory. In cases involving regulations like Reg E Dispute Rights or fee disclosures, it has to use approved templates.
AI agent vs agent assist: what’s the difference?
The agent is communicating and solving the problem for the customer independently, such as ensuring that the card is not lost or conducting a transfer. The process of agent assist is conducted within the context of the bank.
Will AI replace bank support teams?
False. AI technology is used to help employees perform tasks, but it will never be able to replace them completely. AI automates routine processes such as transactional balancing and password resetting, which allows human agents to focus on handling more complicated and expensive interactions.
How do banks keep AI answers compliant and auditable?
Maintain the AI within the confines of a defined context. It must only rely on internal policies that have been verified along with the data available from the central ledger. Every input, decision, and output must be logged by the platform with a precise timestamp for creating the required audit trail.
What should banks ask AI vendors?
Request for evidence of independent certifications like ISO 42001 and AIUC-1. Enquire about the system's process of managing hallucinations and what the system does when there is no source content at all. Request for escalation rate measurement and how it redacts PII.
Can AI support work with our core banking system?
Absolutely. The current AI tools are compatible with the existing core systems without any need for major modification. Thunai has incorporated MCP and API keys in order to integrate with the core ledger. It allows for live data synchronization in both directions.
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