Resolve ATM disputes on evidence, automatically — not on a backlog.
For banks and ATM managed service providers (MSPs) running high-volume dispute and complaint operations.
Who it's for
Banks and ATM managed service providers running or managing ATM networks with a high volume of customer disputes.
Typically championed by
The shift
A high volume of customer complaints and disputes — cash not dispensed, shortage queries, cash-jam and reconciliation issues — resolved through slow, manual evidence review, with customers waiting on outcomes that depend on someone manually reading logs and physical reports.
A substantial share of manual and partially-manual complaint volume becomes automation-ready or assisted-review, letting the operations team focus on genuine exceptions and final decisions rather than evidence reading. The same underlying engine already runs at very high monthly case volumes in an existing large-scale deployment.
How it works
A four-step chain applied consistently across every complaint type. The system reads encrypted or password-protected EJ files, validates counter slips and field images, and cross-references signed JVRs against electronic logs. Covers customer claims, shortage queries, recycler cash-jam/JVR discrepancies, physical shortage, and excess/short cash reconciliation.
- Step 1
Evidence In
EJ logs, counter slips, FCR and recycle reports, including encrypted or password-protected files.
- Step 2
AI Interpretation
Extracts and cross-references transaction data against host/switch logs and ATM counter data.
- Step 3
Confidence Scoring
Classifies each case as automation-ready, assisted-review, or manual-exception.
- Step 4
Closure Recommendation
An evidence-based recommendation reaches the analyst — only assisted-review and manual-exception cases are ever routed to a person, arriving with evidence already cross-referenced.
Trust & governance
Human-in-the-loop by design
DiscvrAI never makes the final liability decision — analysts retain control over every exception.
Confidence-scored and auditable
Every case gets a transparent score, not a black-box decision.
No core system replacement
Works alongside existing dispute management and switch systems.
Validated on your data
Validated on your own data before any commercial discussion.
Focused, short pilot
A focused, short pilot path to proof rather than a multi-quarter IT project.
Proven at scale
Proven at meaningful scale in an existing large-scale deployment.
Bucket-by-bucket coverage
| Case Bucket | What Changes |
|---|---|
| EJ interpretation | EJ data extracted and cross-referenced automatically; only true mismatches escalate. |
| Counter slip validation | Slip data extracted and matched automatically; discrepancies flagged with the slip image attached. |
| No-difference / shortage checks | Balance reconciliation and shortage claims run automatically against FCR/recycle data; only genuine differences route to manual review. |
| FCR & recycle report reading | Reports parsed automatically; reconciliation data available instantly as case evidence. |
Works either way (scope flexibility)
This applies whether the bank self-operates its ATM network or uses an outsourced cash-logistics/MSP partner for cash and hardware. Customer dispute liability sits with the card-issuing bank either way, and the bank's own digital disputes team still has to produce EJ, FCR and switch-report evidence to close a complaint — this product is exactly that layer. Self-operated networks are typically the simpler, faster starting point since there's no third-party data-sharing approval needed.
Start with a 2-week live pilot, one zone, zero commitment.
One zone, zero commitment, two weeks to a measured read on your own cases.
What we need from you
- • A sample of real, anonymised dispute cases from one zone
- • Access to EJ logs, counter slip images, FCR/recycle reports for that sample
- • One point of contact for clarifications
What you get at the end
- • A working system tested on your own dispute cases with a measured accuracy read
- • A clear, qualitative automation-ready read for your actual case mix
- • A scoped proposal for full rollout only if the pilot proves out
Week 1
Setup & First Pass
Week 2
Validation & Tuning
Related case studies
Private-Sector Bank
A leading private-sector bank's ATM operations team resolved disputes through slow, manual evidence review.
A substantial share of manual complaint volume became automation-ready or assisted-review.
ATM Managed Service Provider
An ATM managed service provider had no consistent way to cross-reference EJ logs against physical evidence.
Evidence cross-referencing that took analysts hours now happens automatically.
Public-Sector Bank
A public-sector bank's dispute team manually reconciled shortage claims against FCR and recycle reports.
Only genuine differences now route to manual review, on the same evidence-backed workflow.
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Get in touch
Frequently asked questions
Do you work with banks directly or only through MSPs?+
Both — the platform is used directly by banks and by ATM managed service providers on their behalf, and the same evidence layer applies either way.
What complaint types does this cover?+
EJ and transaction log reconciliation, counter slip and FCR/recycle report validation, and shortage-query handling — covering customer claims, cash-jam and physical shortage cases.
Can it read encrypted or password-protected EJ files?+
Yes.
Is there a low-commitment way to try this?+
Yes — a 2-week live pilot on one zone of your own, anonymised case data, with no commitment required to start.
Does the AI make the final decision on a dispute?+
No — analysts retain control over every exception; the AI classifies and recommends, it does not decide liability.
Does this replace our existing dispute management or switch systems?+
No — it works alongside your existing dispute management and switch systems rather than replacing them.
What do you need from us to run the pilot?+
A sample of real, anonymised dispute cases from one zone, access to the EJ logs, counter slip images and FCR/recycle reports for that sample, and one point of contact for clarifications.
Has this been proven at scale, or is it still experimental?+
It's proven — the same underlying engine already runs at very high monthly case volumes in an existing large-scale deployment.