AI use case

AI agent for account and card servicing

An AI account and card servicing agent is an AI agent that resolves routine banking account and card requests for customers, end to end. It handles things like balances, statements, card blocks and replacements, PIN resets and limit changes, across app, web, messaging and phone, and hands anything sensitive or unusual to a human with the full context.

By Len Debets · Last verified 27 September 2026 · 17 public deployments

84.8%
Median containment rate
4 deployments.
20%
Median contact deflection
3 deployments, 1 "up to" value left out.
USD 720,000 to USD 8.6 million
Indicative value per year
A retail bank with 1 million digitally active customers. Worked example, see how it is calculated.

What problem does it solve?

Routine servicing requests arrive in a constant stream in retail banking: where is my transaction, send me a statement, my card is lost, raise my limit. Each request is simple, but together they fill queues, push up waiting times at the moments customers are most anxious (a lost card, a payment that did not arrive) and take human agents away from the conversations that need judgment, such as hardship, fraud victims and complaints.

Many first generation banking chatbots were rule based: the CFPB describes them as using decision tree logic or a database of keywords to trigger preset, limited responses. A preset answer can explain a procedure, but the customer still has to finish the task somewhere else. The step change is an agent that is authenticated, can act in the core banking and card systems within strict limits, and knows when to stop and hand over. DBS describes a similar shift: its DBS Joy assistant used to give customers instructions on how to find the information they needed, and now answers from their own transaction and account data.

  • The US Consumer Financial Protection Bureau cites an estimate that in 2022 over 98 million users, about 37% of the US population, engaged with a bank's chatbot.Chatbots in consumer finance (2023)

How does it work?

  1. Understand the request. The agent detects the intent ("freeze my card", "why was I charged twice") in the customer's own words and language, on any channel.
  2. Authenticate proportionally. Information requests need a logged in session; actions that move money or change a card need step up authentication, such as an app confirmation or voice biometrics on the phone.
  3. Act through approved tools. The agent calls a small allow list of banking APIs (card block, replacement order, statement request, limit change within set bounds) and confirms the result back to the customer.
  4. Answer policy questions from approved content. Fees, product terms and procedures come from retrieval over the bank's own documents, so answers are grounded and current.
  5. Hand over well. Vulnerability signals, complaints, disputes and anything outside the allow list go to a human specialist with a summary of the conversation, so the customer never repeats themselves.
Audience
Customer facing
Autonomy
Supervised agent
Adoption
Mainstream
Channels
Mobile app, Web chat, WhatsApp, Phone and voice

What is it worth?

Benchmarks are computed from the public deployments below: one data point per organization per KPI, with who made each claim.

Value benchmarks for AI agent for account and card servicing
KPIMedianReported rangeData pointsClaimed by
Interactions handledNot pooled
80,000 to 10.8 million
55 organization
Containment rate84.8%
67% to 90%
43 organization, 1 vendor
Contact deflection20%
7% to 50%
Not pooled: up to 25%
3plus 1 up to3 organization
Satisfaction upliftToo few to pool
17% to 150%
22 organization

Value drivers: Lower cost to serve, Customer experience, Inclusion and access, Employee productivity.

Indicative value

A retail bank with 1 million digitally active customers

USD 720,000 to USD 8.6 million

Human handled contact cost avoided per year

How this is calculated

Formula: customers * contactsPerCustomer * servicingShare * containment * costPerContact. The low scenario uses every low input, the high scenario every high input.

InputLowHighBasis
Digitally active customers customers, customers1,000,0001,000,000The reference bank.
Assisted contacts per customer per year contactsPerCustomer, contacts per customer per year24Editorial assumption for a digitally active retail bank. Replace with your own contact volume.
Share of contacts that are routine servicing servicingShare, fraction of contacts0.40.6Editorial assumption, replace with the share from your own contact reason report.
Share of servicing contacts the agent resolves containment, fraction of servicing contacts0.30.6Conservative against the benchmarks on this page (Microsoft reports about 84.6% of self service messaging interactions resolved end to end at Commonwealth Bank; DBS reports nine in ten digibot queries resolved digitally), because both figures are for messaging and this range also covers the phone channel.
Cost of a human handled contact costPerContact, USD per contact36Editorial assumption for a blended chat and phone contact. Replace with your own fully loaded cost.

What it leaves out: Gross avoided contact cost only. It leaves out the cost of running the AI, the integration work, the revenue effect of faster service and any reduction in complaint handling.

Market estimates (analyst estimates, not deployments)

Who already uses it?

17 public deployments, strongest evidence first. Grades: A regulator or audit, B the organization itself, C vendor case study, D anonymous or estimate.

Abu Dhabi Commercial Bank (ADCB)

United Arab Emirates · Banking · 2026

ProductionGrade B

Abu Dhabi Commercial Bank (ADCB) rebuilt its mobile banking app around an AI powered conversational assistant that customers can address by voice or text to check balances, move money, manage cards and services, track goals and explore the bank's products, instead of navigating menus and forms. The assistant is meant to personalize its responses over time from each customer's own interactions, and its security architecture combines biometric authentication, AI driven fraud detection and real time monitoring. ADCB's Group Chief Operating Officer said artificial intelligence is central to the bank's digital transformation.

No outcome disclosed.

Arab Bank

Jordan · Banking · 2026

ProductionGrade B

In March 2026 Reflect, the digital banking app of Arab Bank, launched ReVA, an AI powered virtual assistant inside the app. Customers can type, speak or send voice messages in Arabic or English and get written or spoken replies. ReVA answers questions about Reflect services and promotions, shows account details and reviews spending, and also helps prospects who have not yet opened an account. The bank calls the voice to voice feature among the first of its kind in the region. No usage or containment figures are published.

No outcome disclosed.

DBS Bank

Singapore · Banking · 2026

ScaledGrade B

DBS runs two generative AI virtual assistants on its own AI platforms: DBS digibot for individual customers in Singapore, Hong Kong and Taiwan, and DBS Joy for corporate and SME customers. In July 2026 DBS Joy became agentic in Singapore and now answers questions such as payment status and fees from the customer's own transaction and account data. DBS digibot answers card, refund, fee waiver and remittance questions today; DBS plans to add agentic tasks such as checking card usage, tracking reward points and blocking or replacing cards in the fourth quarter of 2026, for logged in customers only.

  • Containment rate: about 90%, DBS digibot, first half of 2026, queries resolved without a follow up call
    "In the first half of 2026, DBS digibot successfully resolved nine in every 10 queries digitally, without customers needing to make a follow-up call."
    Claimed by: organization
  • Contact deflection: 7%, DBS Joy in Singapore, first six months of 2026, calls or emails to customer service
    "Active users increased by 61%, contributing to a 7% reduction in calls or emails to customer service."
    Claimed by: organization
  • Satisfaction uplift: 17%, DBS Joy in Singapore, first six months of 2026
    "Customer satisfaction scores for DBS Joy rose by 17% over the same period."
    Claimed by: organization

Garanti BBVA

Türkiye · Banking · 2026

ScaledGrade B

Garanti BBVA runs Ugi, a smart assistant inside its mobile banking app that answers questions and carries out banking transactions through conversation. The bank reports that Ugi handled more than 60 million chats in 2024 and now averages more than 6.4 million interactions a month. After the bank added generative AI based on large language models, Ugi understands 90% of user requests, and it supports more than 300 end to end banking transactions.

  • Interactions handled: at least 6.4 million, per month on average
    "Garanti BBVA’s AI journey, launched in 2016, has evolved into Ugi, which today handles more than 6.4 million monthly interactions on average, has 1.6 million active users, and supports more than 300 end-to-end banking transactions."
    Claimed by: organization
  • Accuracy: 90%, share of user requests understood after the generative AI upgrade
    "With the integration of generative AI, Ugi can now understand 90% of user requests."
    Claimed by: organization
  • Interactions handled: at least 60 million, chats in 2024
    "In 2024, Garanti BBVA’s AI assistant, Ugi, handled over 60 million chats, helping more than 5 million customers resolve their doubts."
    Claimed by: organization
  • Users served: at least 5 million, customers helped in 2024
    "In 2024, Garanti BBVA’s AI assistant, Ugi, handled over 60 million chats, helping more than 5 million customers resolve their doubts."
    Claimed by: organization

Vision Bank

Saudi Arabia · Banking · 2026

ProductionGrade B

Vision Bank, a Saudi Central Bank regulated Islamic digital bank that calls itself the first Saudi Islamic bank powered by AI, offers Noura, a conversational banking AI agent. Customers complete everyday banking tasks through natural interactions instead of menus. The description comes from the boilerplate of the bank's September 2026 release on an innovation partnership with Publicis Sapient, which is not tied to Noura; the release names no channel, tasks or vendor for Noura and gives no usage figures.

No outcome disclosed.

BPCE Group

France · Banking · 2025

ScaledGrade B

BPCE, the French banking group behind the Banque Populaire and Caisse d'Epargne networks, integrated an AI virtual assistant into both networks' mobile apps that guides retail customers autonomously, 24 hours a day, through their banking. As of its November 2025 progress update, the group reports 1 million digital clients already using the assistant, and the service is being rolled out next to professional and business clients. The assistant sits alongside MAiA, BPCE's internal generative AI tool built on external models from Mistral AI, OpenAI, Google and Anthropic, which half of the group's 100,000 employees use an average of 40 times a month.

  • Users served: 1 million, as of the November 2025 update, since launch
    "Proof of the effectiveness of this service is that 1 million digital clients are already using it."
    Claimed by: organization

NatWest Group

United Kingdom · Banking · 2025

ScaledGrade B

NatWest routes many kinds of everyday customer queries through Cora, its AI assistant in online banking and the mobile app, now with generative AI (Cora+). Customers whose ATM withdrawal did not pay out are sent to Cora with the phrase "ATM dispute" as the first step of the claim, and the assistant is available before login as well.

NatWest disclosed that Cora handled 10.8 million queries over 2023, more than double the roughly five million it handled in 2019, and that the top five questions asked via Cora are about canceling a transaction, changing an address, asking for a bank statement, requesting a new bank card or opening an ISA.

NatWest says the generative AI version improved customer satisfaction and reduced how often a colleague has to step in, and in 2025 it began a collaboration with OpenAI to extend the assistant to more complex tasks.

  • Satisfaction uplift: 150%, Cora+ generative AI functionality
    "The GenAI functionality offered by Cora+ has shown a 150% improvement in customer satisfaction, while reducing the number of times a colleague needs to intervene."
    Claimed by: organization
  • Interactions handled: 10.8 million, over 2023
    "NatWest revealed the virtual chatbot handled 10.8 million queries over 2023, up from about five million in 2019."
    Claimed by: organization

Zenith Bank

Nigeria · Banking · 2021

ProductionGrade B

Zenith Bank, one of Nigeria's largest banks, launched ZiVA (Zenith Intelligent Virtual Assistant), an AI powered chatbot on WhatsApp, so customers could transact and get real time customer service around the clock on the encrypted messaging platform they already used. Through ZiVA, customers can open new accounts, receive instant transaction notifications, check balances, transfer funds, top up airtime, confirm checks, pay bills, apply for loans, block their accounts and request mini statements. Group Managing Director Ebenezer Onyeagwu said the launch was driven by the need for additional secured communication channels as the bank deepened its retail penetration.

No outcome disclosed.

Emirates NBD

United Arab Emirates · Banking · 2019

ProductionGrade B

Liv., the digital bank of Emirates NBD, launched Olivia in March 2019, a conversational chatbot built on Kasisto's KAI Banking platform. Olivia answers questions about Liv.'s banking and lifestyle offers, including how much a customer spent last month on groceries or in restaurants, explains how to make a local transfer or block a card, and hands the conversation to a Liv. agent when the customer prefers. During a mentorship program before the launch it handled more than 80,000 customer questions, which the bank used to train it on how UAE customers phrase requests.

  • Interactions handled: at least 80,000, During the mentorship programme before launch
    "During the mentorship programme, Olivia was able to offer instant assistance to over 80,000 customer questions, while learning and incorporating additional nuances of natural language based communication of UAE customers."
    Claimed by: organization

KB Kookmin Card

South Korea · Payments and cards · 2026

ProductionGrade C

KB Kookmin Card upgraded its existing digital consultation chatbot into a hybrid chatbot that combines the stable, rule based processing of the original chatbot with generative AI. The upgrade has three stages: generative AI first corrects typos and unclear wording in customer questions that the original chatbot could not understand, then it analyzes and handles multiple questions asked in a single message so the conversation can continue naturally, and for a subset of answers generative AI now drafts the reply itself, with technology to limit hallucination. KB Kookmin Card plans to extend generative AI to its voice callbot next and to a system that pre analyzes customer intent as soon as a conversation starts.

  • Accuracy: at least 99%
    "특히 생성형 AI의 한계로 지적되는 환각 현상(할루시네이션) 방지 기술을 탑재해 99% 이상의 응답 정확도를 달성했으며, 나머지 예외 질의는 표준 안내 매뉴얼로 처리해 안정성을 보완했다."
    Claimed by: organization

Simple (Attijariwafa bank)

Morocco · Banking · 2026

PilotGrade C

Simple, the digital bank brand of Attijariwafa bank, opened Simple AI in October 2026 as a preview for a first community of customers. The assistant is built into the Simple app, takes written or spoken requests in six languages including Darija, answers product questions, explains account activity and runs routine operations such as a transfer, a bill payment or a vehicle tax payment. The customer validates any operation that needs approval, and a customer can open and follow a complaint from the same place. The bank says customer data stays on its own infrastructure. No usage figures are disclosed.

No outcome disclosed.

Trust Bank

Singapore · Banking · 2026

ProductionGrade C

Trust Bank, a Singapore digital bank, added a generative AI chatbot to its app that answers customers in natural language using updated product information and customer context. It handles common requests such as deleting a payee, paying a credit card bill, replacing a card and getting statements, and hands complex cases to human agents with the conversation history. Its customer care team helped refine the knowledge base. Trust Bank reports that human handled chats fell by 50 percent and that complaints dropped by about 40 percent within months of the rollout.

  • Contact deflection: 50%, customer chats handled by human agents, within months of rollout
    "Trust Bank has cut customer chats handled by human agents by 50 percent and reported around a 40 percent drop in complaints within months of rolling out its Gen AI chatbot."
    Claimed by: organization

Commonwealth Bank of Australia

Australia · Banking · 2024

ScaledGrade C

Commonwealth Bank built a central AI orchestration agent that reads the customer's intent and routes it to a conversational AI, retrieval over public content, a deterministic guarded path for regulated journeys such as fraud disputes, or a human specialist with the full context, on its messaging channel. It migrated nearly 700 chatbot topics and launched a generative AI banking chatbot in November 2024. Voice bots are a planned extension of the orchestration layer.

  • Containment rate: about 84.6%, May 2026, self service messaging
    "In May 2026, approximately 84.6% of self-service messaging interactions were resolved end-to-end in the messaging channel."
    Claimed by: vendor

Emirates Islamic

United Arab Emirates · Banking · 2024

ProductionGrade C

Emirates Islamic, which says it was the first Islamic bank worldwide to launch WhatsApp banking, runs a chatbot on that channel. A Euromoney award profile reports that the service handled over 430,000 interactions in 2024, that the chatbot resolved two thirds of customer queries and that at least 10,000 customers a month were handed to live chat agents. The profile adds that the bank is implementing generative AI, so the 2024 figures describe the earlier chatbot.

  • Interactions handled: at least 430,000, 2024
    "Emirates Islamic was also the first Islamic bank worldwide to launch WhatsApp banking, handling over 430,000 interactions in 2024, with their advanced chatbot resolving two-thirds of customer queries and connecting at least 10,000 customers monthly with live chat agents."
    Claimed by: organization
  • Containment rate: about 67%, 2024
    "Emirates Islamic was also the first Islamic bank worldwide to launch WhatsApp banking, handling over 430,000 interactions in 2024, with their advanced chatbot resolving two-thirds of customer queries and connecting at least 10,000 customers monthly with live chat agents."
    Claimed by: organization

MONETA Money Bank

Czechia · Banking · 2024

ProductionGrade C

MONETA Money Bank in Czechia saw contact centre call volume rise by 300 percent between 2016 and 2022. With NTT Czech Republic it deployed Tom, a voicebot built on the Feedyou platform and Microsoft Azure AI speech services, to take common calls such as blocking lost cards, changing card limits and mortgage information. The Microsoft customer story reports that Tom handles up to 25 percent of all inquiries without an agent and that the bank cut call centre operating costs by 10 percent while customer satisfaction rose.

  • Contact deflection: up to 25%, share of total inquiries resolved without contact centre agents
    "It efficiently manages as much as 25 percent of the total inquiries received, resolving tasks such as blocking lost cards, adjusting card limits, providing mortgage information, and more, all without intervention by MONETA Money Bank’s contact center agents."
    Claimed by: vendor
  • Cost reduction: 10%, call center operational costs
    "As a result, the bank cut call center operational costs by 10 percent, while increasing customer satisfaction."
    Claimed by: vendor

Equity Bank Kenya

Kenya · Banking · 2022

ScaledGrade C

Equity Bank Kenya officially commissioned EVA, the Equity Virtual Assistant, in May 2022 to replace EazzyNet's customer support role with a chatbot that runs on artificial intelligence and machine learning models. On WhatsApp, Facebook Messenger and Telegram, customers can chat with EVA to manage their accounts and carry out transactions such as money transfers, airtime purchases and viewing account balances, in addition to general customer support, and Equity says the assistant will keep learning from its interactions with customers.

No outcome disclosed.

Abu Dhabi Islamic Bank (ADIB)

United Arab Emirates · Banking · 2021

ProductionGrade C

Abu Dhabi Islamic Bank launched Chatbanking on WhatsApp in the fourth quarter of 2020, built on the Verloop.io chatbot platform. Customers check balances, transfer funds, pay bills and book branch appointments with simple commands, in classical Arabic and in the Emirati dialect that the bank added in November 2020. By mid 2021 the bank reported 150,000 customers with a verified chatbot account, more than 350,000 conversations in the first half of the year and 85 percent of queries resolved without a live agent. The bank also reported a 20 percent drop in call volume at its call center.

  • Containment rate: 85%, First half of 2021
    "During the first half of the year, the ADIB chatbot handled more than 350,000 conversations where 85 percent of the queries have been resolved without the need for live agent assistance."
    Claimed by: organization
  • Interactions handled: at least 350,000, First half of 2021
    "During the first half of the year, the ADIB chatbot handled more than 350,000 conversations where 85 percent of the queries have been resolved without the need for live agent assistance."
    Claimed by: organization
  • Contact deflection: 20%, Around Q2 2021
    "This has enabled the call Center to reduce call volume by 20%."
    Claimed by: organization

How do you implement it?

A model agnostic playbook: what to prepare, the order to build in, and what goes wrong.

Data you need

  • Approved, current product terms, fee tables and servicing procedures
  • A catalog of servicing intents with volumes from the contact center
  • Customer and card data reachable through APIs, not screens

Systems to integrate

  • Core banking system (balances, transactions, statements)
  • Card management platform (block, replace, limits, PIN)
  • Identity and step up authentication (app push, one time passcode, voice biometrics)
  • Contact center platform for handover with conversation context
  • CRM or case management for follow up tasks

Complexity: Medium

Answering questions is easy. Acting on the account takes the integrations with core banking and card platforms, step up authentication and a clean handover into the contact center.

  1. 1

    Pick the first intents by volume and risk

    Take the contact reason report and choose five to ten high volume, low risk intents (statement request, card freeze, transaction lookup). Leave money movement for a later wave.

  2. 2

    Define the action allow list

    For every action write down the API, the authentication level it needs, the limits (for example a maximum limit increase) and what the agent says when a limit is reached.

  3. 3

    Ground the answers

    Load only approved product and fee content into the knowledge base, with an owner and a review date per document, and make the agent refuse when the answer is not in it.

  4. 4

    Design the handover

    Decide which signals trigger a human (vulnerability, complaint, dispute, repeated failure) and pass a summary and the authenticated identity so the customer does not repeat anything.

  5. 5

    Test before customers do

    Build a test set of real conversations per intent, including edge cases and attempts to make the agent act outside its limits, and run it on every change.

  6. 6

    Launch in one channel, then widen

    Start in the logged in app, where authentication is strongest, measure containment and satisfaction per intent, then add web, messaging and voice.

Guardrails

  • Actions only through an allow list of APIs, each with its own authentication level and limits
  • Step up authentication before any card action or money movement
  • Answers only from approved content, with a refusal when the content does not cover the question
  • Automatic handover on vulnerability signals, complaints and disputes
  • Masking of card numbers and personal data in logs and model prompts

KPIs to instrument

  • Containment rate per intent, counting repeat contacts within seven days as not contained
  • Handover rate and handover reasons
  • Customer satisfaction on contained conversations versus human handled ones
  • Share of actions completed without error, from the core system logs
  • Complaints that mention the assistant

Human in the loop

Humans own the exceptions: disputes, hardship, suspected fraud victims and complaints. They also review a sample of contained conversations every week to catch answers that were fluent but wrong, and approve every new intent and action before it goes live.

Common failure modes

Fluent but wrong policy answers
Answers drawn from outdated or unapproved content. Prevent with document ownership, review dates and refusal when retrieval finds nothing.
Containment that is really abandonment
Customers give up rather than get helped, which looks like containment in the dashboard. Count repeat contacts and measure satisfaction per intent.
Handover without context
The customer has to start again with a human, which is worse than no assistant. Pass the summary and the authentication state.
Scope creep into risky actions
New actions are added without their own risk review. Treat every new action as a change with sign off.

What are the risks and rules?

EU AI Act

Limited risk (transparency)

Article 50(1): people must be informed that they are interacting with an AI system, unless that is obvious from the context. Servicing existing accounts and cards is not an Annex III use. It would become high risk under Annex III point 5(b) if the agent itself evaluated the creditworthiness of a natural person, for example to decide a credit limit increase.

Guidance

Controls to put in place

  • AI disclosure at the start of every conversation
  • Inventory entry for the assistant with an accountable owner and a documented action allow list
  • Immutable audit trail of every action the agent took, with the authentication level used
  • Change control and regression tests for every new intent or action
  • Outcome monitoring for vulnerable customers and complaint trends

Frequently asked questions

What share of servicing requests can an AI agent resolve?

It depends on the intent mix, the channel and whether the agent can act as well as answer. Microsoft reports that at Commonwealth Bank about 84.6% of self service messaging interactions were resolved end to end in May 2026, and DBS reports that DBS digibot resolved nine in every ten queries digitally in the first half of 2026. Both figures are for messaging at large banks, so plan more conservatively for voice and for a first launch.

Is a banking servicing chatbot high risk under the EU AI Act?

Usually not. It falls under the transparency duty of Article 50: customers must know they are talking to AI. It would become high risk under Annex III point 5(b) if it evaluated the creditworthiness of a natural person, for example by deciding a credit limit increase itself, so keep credit decisions in the bank's existing credit process.

Which requests should stay with humans?

Disputes, suspected fraud or scams, hardship and financial difficulty, complaints and any conversation where the customer shows signs of vulnerability.

How to cite this page

Blits.ai AI Use Case Library, "AI agent for account and card servicing", last verified 27 September 2026, https://www.blits.ai/ai-use-cases/account-and-card-servicing-agent. Licensed under CC BY 4.0. Method: how we verify use cases.

Changelog
  • 11 November 2026: Added deployments: Arab Bank, Emirates Islamic, Garanti BBVA, MONETA Money Bank, Trust Bank, Vision Bank.
  • 14 October 2026: Added deployments: Simple (Attijariwafa bank).
  • 14 October 2026: Added deployments: Abu Dhabi Islamic Bank (ADIB), Emirates NBD.
  • 1 October 2026: Added deployments: KB Kookmin Card.
  • 30 September 2026: Added deployments: BPCE Group.
  • 30 September 2026: Added deployments: Abu Dhabi Commercial Bank (ADCB), Equity Bank Kenya, Zenith Bank.
  • 29 September 2026: Added deployments: NatWest.
  • 27 September 2026: First published

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