What problem does it solve?
ATM problems arrive at the worst moment: the customer needs cash now, the machine kept their card or debited the account without paying out, and the branch is closed. The customer calls, waits, explains the machine location and time, and is told a claim will take days. Behind the scenes, the bank checks the claim against the device and transaction records.
The work splits into three kinds of request. Simple information (where is the nearest machine that has cash and accepts deposits). Card and PIN problems after a retained card or too many PIN attempts. And cash disputes, which are regulated: in the United States, for example, Regulation E in the general case gives the bank 10 business days to investigate an incorrect amount from an electronic terminal, or up to 45 days if it provisionally credits the account, with longer limits for new accounts and for withdrawals outside the US. Each kind needs a different mix of lookups, authentication and human review.
How does it work?
- Recognize the device problem. The agent detects the intent ("the ATM took my card", "no cash came out") and asks for the machine, time and amount, pulling the candidate transaction from the account instead of asking the customer to type it.
- Authenticate before acting. Card and PIN actions and any claim require step up authentication in the app or on the phone.
- Resolve the simple cases. It locates working machines, explains what happens to a retained card, blocks it and orders a replacement through approved card APIs.
- Open the cash dispute correctly. For a withdrawal that did not pay out it opens the claim with the required data, explains the investigation timeline and any provisional credit the rules require, and tracks the status.
- Hand over when needed. Claims that fail automatic reconciliation, suspected fraud or distressed customers go to a person with the case already assembled.
- Audience
- Customer facing
- Autonomy
- Supervised agent
- Adoption
- Emerging
- Channels
- Mobile app, Web chat, Phone and voice, Kiosk and branch
What is it worth?
Benchmarks are computed from the public deployments below: one data point per organization per KPI, with who made each claim.
| KPI | Median | Reported range | Data points | Claimed by |
|---|---|---|---|---|
| Interactions handled | Not pooled | 10.8 million | 1 | 1 organization |
| Satisfaction uplift | Too few to pool | 150% | 1 | 1 organization |
Value drivers: Customer experience, Lower cost to serve, Speed and cycle time, Compliance quality.
Indicative value
A retail bank with a network of about 2,000 ATMs
USD 60,000 to USD 400,000
Contact and claim handling cost avoided per year
How this is calculated
Formula: deviceContacts * resolvedShare * costPerContact. The low scenario uses every low input, the high scenario every high input.
| Input | Low | High | Basis |
|---|---|---|---|
| Contacts per year about ATM and device problems deviceContacts, contacts per year | 50,000 | 100,000 | Editorial assumption, replace with your own contact reason and claim volumes. |
| Share of those contacts the agent resolves or files without a human resolvedShare, fraction of contacts | 0.3 | 0.5 | Editorial assumption; no deployment on this page discloses a rate for ATM journeys. |
| Cost of a human handled contact or manually filed claim costPerContact, USD per contact | 4 | 8 | Editorial assumption, replace with your own fully loaded cost. |
What it leaves out: Handling cost only. It leaves out the cost of the AI and integrations, faster reconciliation in back office operations, fewer regulatory breaches on dispute deadlines and the customer value of getting help when branches are closed.
Who already uses it?
1 public deployment, strongest evidence first. Grades: A regulator or audit, B the organization itself, C vendor case study, D anonymous or estimate.
NatWest Group
United Kingdom · Banking · 2025
NatWest routes a wide range 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 cancelling 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
How do you implement it?
A model agnostic playbook: what to prepare, the order to build in, and what goes wrong.
Data you need
- ATM and device inventory with location, status, cash and deposit capability
- Transaction data that links a withdrawal to a device and time
- Dispute rules per market, including deadlines and provisional credit
- Contact reason data for device related contacts
Systems to integrate
- ATM monitoring or device management system
- Card management platform (block, replace, PIN unblock)
- Core banking transactions and the dispute or claims system
- Step up authentication in the app and on the phone
- Contact centre platform for handover
Complexity: Medium
Machine location and card actions are standard integrations. Cash disputes are harder: the agent needs the transaction record, the device journal or reconciliation result and the dispute rules of each market, and the claim must be auditable.
- 1
Split the intents
Separate information requests, card and PIN problems and cash disputes. Launch the first two, which need no investigation, before the dispute journey.
- 2
Wire the dispute journey to the rules
Encode deadlines, required data and provisional credit logic per market as configuration owned by the disputes team, not as model instructions, and test it against past claims.
- 3
Use the data the bank already has
Prefill the machine, time and amount from the transaction record and reconciliation data so the customer confirms rather than types, which cuts errors in filed claims.
- 4
Close the loop
Send status updates on open claims in the same channel and let customers ask about a claim without calling.
Guardrails
- Step up authentication before any card, PIN or claim action
- Card numbers and PINs never pass through the model; card data is tokenized before it reaches the conversation
- Dispute deadlines and provisional credit decided by rules, not generated by the model
- Suspected fraud, repeated claims and distressed customers always go to a person
- An auditable record of every claim the agent opened, with the data it used
KPIs to instrument
- Share of device contacts resolved or correctly filed without a human
- Median days from claim to resolution
- Share of filed claims that were complete on first submission
- Dispute deadline breaches
- Customer satisfaction on device journeys
Human in the loop
Dispute analysts own every claim that does not reconcile automatically and every refusal. The disputes team signs off the rules the agent follows and reviews a sample of filed claims each week for completeness.
Common failure modes
- Wrong promises on refunds
- The agent tells a customer money will be back by a date the rules do not support. Generate timelines from rules and approved wording only.
- Card data in the conversation
- Customers type card numbers or PINs into chat. Detect and tokenize them before they reach the model or logs, and tell the customer not to share a PIN.
- Stale device data
- The agent sends a customer to a machine that is out of cash. Use live device status and say when data may be out of date.
What are the risks and rules?
EU AI Act
Limited risk (transparency)
A customer facing assistant must tell people they are interacting with an AI system unless that is obvious (Article 50(1)). It does not evaluate creditworthiness (Annex III point 5(b)) or eligibility for public assistance benefits (point 5(a)), so it is not high risk; biometric verification whose sole purpose is to confirm identity is excluded from Annex III point 1(a).
Guidance
- § 1005.11 Procedures for resolving errors (Regulation E) (Consumer Financial Protection Bureau, North America). The US rule for investigating electronic fund transfer errors, including receipt of an incorrect amount of money from an electronic terminal such as an ATM. In the general case the bank has 10 business days to investigate, or up to 45 days if it provisionally credits the account. For transfers within 30 days of the first deposit to a new account, the limits are 20 business days (instead of 10) and 90 days (instead of 45). For transfers not initiated in the US, such as a withdrawal abroad, and for point of sale debit card transfers, the 45 day limit becomes 90 days. An example of the dispute rules the agent must follow.
- Article 50, transparency obligations for providers and deployers of certain AI systems (European Union, Europe). People must be informed that they are interacting with an AI system unless this is obvious from the context.
Controls to put in place
- Dispute rules per market as reviewed configuration with a named owner
- PCI DSS scoping of every component that could see card data
- Audit trail of every card action and claim with the authentication level used
- Regression tests on past claims for every change
- Monitoring of deadline breaches and complaints about device journeys
Frequently asked questions
- Do banks already use AI assistants for ATM problems?
Yes, as the entry point. NatWest tells customers whose ATM withdrawal did not pay out to start the dispute by typing "ATM dispute" to Cora, its AI assistant, in online or mobile banking. NatWest reports a 150% improvement in customer satisfaction for Cora+ overall, but public outcome figures for ATM journeys specifically are scarce.
- Can the agent refund a failed withdrawal automatically?
Only where the bank's rules allow it, for example when reconciliation confirms the machine did not dispense. Everything else is an investigation with regulated deadlines, owned by a human analyst.
- How quickly must a bank resolve an ATM cash dispute?
It depends on the market. In the general case, US Regulation E requires the bank to decide within 10 business days of the notice, or within 45 days if it provisionally credits the account within those 10 business days. For transfers within 30 days of the first deposit to a new account, the limits are 20 business days (instead of 10) and 90 days (instead of 45). For transfers not initiated in the US, such as a withdrawal abroad, and for point of sale debit card transfers, the 45 day limit becomes 90 days. The rule covers consumer accounts only. The agent should quote these timelines from configured rules, never from the model.
How to cite this page
Blits.ai AI Use Case Library, "AI agent for ATM and self service device assistance", last verified 27 September 2026, https://www.blits.ai/ai-use-cases/atm-and-self-service-device-assistance. Licensed under CC BY 4.0. Method: how we verify use cases.
Changelog
- 27 September 2026: First published