Agentic AI in the
Payments Division

Where it adds value, what it costs, and where to start. A twelve-month view for the executive team.

Prepared for the Payments Division executive team  ·  July 2026
The Answer

Three places agentic AI pays for itself in your Payments Division within 12 months.

We prototyped the flagship, so you can start where the value is, with the risk already worked down.

01

Intelligent reconciliation

Match payments across systems and investigate the breaks.

Low risk · runs in parallel
02

Fraud alert triage Prototyped

Investigate every alert and hand analysts a decision-ready case.

Prototyped · where we start
03

AML compliance co-pilot

Draft suspicious-matter reports for an officer to review and lodge.

Highest stakes · last
Agentic AI · Payments Division
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The Opportunity

The bottleneck isn’t detection.
It’s the investigation behind every alert.

30k
Fraud alerts a month
~90%
Are false positives
~12min
Of manual work, per alert

Your fraud engine already raises the alerts. Agentic AI earns its place by automating the human investigation behind each one, not by replacing the detection you already trust. Figures are illustrative of a large Australian bank.

Agentic AI · Payments Division
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A regulated bank climbs the capability ladder deliberately. You earn the right to more autonomy by proving governance at each rung.
The strategic principle. Why we don’t ship the most autonomous system first
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Use case 01

Intelligent reconciliation and exceptions

Today
  • Ops staff match payments across core banking, scheme files and the ledger by hand.
  • Breaks are worked in spreadsheets, and the same exception types recur every cycle.
  • Month-end is a scramble, and the backlog is invisible until it is late.
With an agent
  • Matches across systems and investigates each break by pulling the underlying records.
  • Drafts the correction with its reasoning attached, then queues it for an approver.
  • Learns the recurring break types, so the queue shrinks rather than just moving.
Value lever
Cost-out, back office
Risk rung
Low
No customer impact, fully reversible
Indicative ROI
30 to 40% less manual exception handling
Illustrative, to be calibrated in Discovery
Phase 1: one high-volume break type, on one rail, running in shadow mode.
Agentic AI · Payments Division
05 / 15
Use case 02

Fraud alert triage The one we built

Today
  • Analysts open every alert the engine raises, roughly 30,000 a month.
  • Each one means pulling history, profile and device data from three systems.
  • About 12 minutes per alert, and around 90% turn out to be false positives.
With an agent
  • Investigates each alert proactively, gathering the same context without being asked.
  • Assembles a decision-ready case: signals, precedent, and a scored recommendation.
  • Stops at a human gate. It recommends, and an analyst decides and is accountable.
Value lever
Productivity and loss avoidance
Risk rung
Medium
Recommends only, never acts
Indicative ROI
~$2.5M a year of analyst capacity
Illustrative, method on the ROI slide
Phase 1: a six-week shadow pilot on live alerts, measured against analyst decisions.
Agentic AI · Payments Division
06 / 15
Use case 03

Real-time compliance and AML co-pilot

Today
  • Officers review monitoring hits against AML typologies case by case.
  • Suspicious-matter reports are written by hand, each one assembled from scratch.
  • Backlogs push against AUSTRAC reporting deadlines rather than sitting clear of them.
With an agent
  • Monitors patterns against typologies and assembles the supporting evidence.
  • Drafts the report with citations back to the underlying transactions.
  • Routes it to an officer to review and lodge. It never lodges anything itself.
Value lever
Regulatory-risk reduction
Risk rung
High
AUSTRAC-facing, highest stakes
Indicative ROI
Faster turnaround, fewer late lodgements
Illustrative, risk-avoidance not cost-out
Phase 1: drafting assistance only, with the officer reviewing and lodging every report.
Agentic AI · Payments Division
07 / 15
The 12-month view

Three rungs, twelve months

Normally you would start at the lowest-risk rung. The prototype changes that. Triage’s method is already proven, so you start where the value is bigger without taking more risk. Investment bands are illustrative. RISK HIGH MED LOW MONTHS 0–4 · YOU ENTER HERE Fraud alert triage ★ $$ · the prototype, taken into your environment MONTHS 3–8 · CAN RUN IN PARALLEL Intelligent reconciliation $ · the low-risk cost-out win, no customer impact MONTHS 8–12+ AML compliance co-pilot $$$ · builds on the proven controls DECISION GATE evals must clear first The human gate and audit trail built at triage are reused by both rungs behind it, so the governance cost is paid once. Month 0 4 8 12+
Agentic AI · Payments Division
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The flagship, up close

The triage assistant: four specialist agents, a human gate, and an audit trail

Fraud alert synthetic JSON Triage Orchestrator 1 · Intake normalise → case file 2 · Enrichment proactively gathers context 3 · Risk precedent + rule-based score 4 · Case analyst-ready summary Human-in-the-loop gate approve · override · request more digging Decision taken by a person the agent never acts alone proactively calls tools get_transaction_history() get_customer_profile() check_device_fingerprint() search_similar_cases() Audit trail (JSONL) every step, tool call & reason logged · the "trace" a regulator can read Deterministic (rules, no model) Model-driven (LLM) Human gate Tool Audit store Prototype = orchestrator-workers + human gate. Production swaps stubs for MCP integrations, LangGraph/ADK orchestration, and an eval harness.
Agentic AI · Payments Division
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Proof · your starting point

We didn’t just recommend it. We built it.

Escalate
100 / 100 · high confidence
Likely account takeover
New device, foreign IP, new payee, contact just changed.
Clear
0 / 100 · high confidence
Clean false positive
Known device, home city, small amount above average.
Monitor
35 / 100 · low confidence
Asks for human judgement
Crypto payee, and precedent genuinely conflicts.
A human gate. The agent recommends. Nothing is actioned until a person decides.
A full audit trail. Every step, tool call and reason logged for a regulator to read.
Runs on a laptop. A local model, one dependency, no API key and no data leaving the bank.
Consider it the starting point for discovery, not a demo.
Agentic AI · Payments Division
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Why This Is Fundable

Governance is the licence to operate, not the brake

The agent recommends. A person decides

  • Under the Financial Accountability Regime, a named executive is personally accountable
  • That accountability cannot be delegated to a model

A regulator-readable audit trail

  • Every step, tool call and reason is logged
  • It turns a black box into something you can deploy

Built for the Australian stack

  • An operational-risk fallback under APRA CPS 230
  • Where the model runs is a design choice (CPS 234, Privacy)

It lands on a live tailwind

  • The 2025 Scams Prevention Framework raises the bar
  • Banks are being told to reduce scam losses now
Agentic AI · Payments Division
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The Business Case

The economics of the fraud-triage rung

~$0M
Analyst capacity recovered per year
~50% less handle time across ~30,000 alerts a month, redeployed to complex cases. Illustrative.
Months
Payback on a six-figure pilot
Pays back on recovered capacity alone, before counting any fraud-loss reduction. Illustrative.
Upside
Loss reduction and fewer disputes
Faster action on irreversible rails, plus fewer complaints reaching AFCA. Illustrative.
All figures are illustrative and would be calibrated in a Discovery. The method is on the appendix slide so you can challenge it.
Agentic AI · Payments Division
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Proof it works · Australian financial services

This isn’t theoretical. It is proven in Australian financial services

95%
lower cost per claim

HBF automated health-claims assessment with agentic AI, a “Missing Information Agent” that reasons over incomplete claims. Turnaround fell from weeks to seconds at 94%+ extraction accuracy, above human reviewers, with zero disruption to core systems.

HBF · Mantel Group case study
Australia’s first agentic AI home loan, shipped in 16 weeks, with audit trails and human-override built into every workflow.

Lendi’s “Guardian”: 10+ agents, “compliance by design”. The same human-in-the-loop and audit-trail model this proposal is built on, proven in a regulated Australian lender.

Lendi Group · Mantel Group case study
Agentic AI · Payments Division
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Start where the proof already exists. A six-week engagement that takes the triage prototype into your environment.
Your data, your analysts, shadow mode. Fixed price, and a decision gate at the end
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Thank you · questions welcome
The agent investigates and recommends.
A human decides and is accountable.
The triage prototype is yours to keep.
It runs on a laptop against a local model. No API key, and no data leaves the bank
Nhial Majok  ·  agentic-ai-payments-division.vercel.app
15 / 15
Reference material
Appendix
How it goes to production, a live run, the ROI method, and sources. Shown only if the room asks.
Appendix
Appendix 1
The Prototype · to production

What changes on the way to production

ComponentIn this prototypeIn production
ModelLocal open model via Ollama (qwen2.5:3b), called directlyA governed endpoint, on-shore or contracted (CPS 234, Privacy Act)
ToolsPython stubs over JSON fixturesMCP connections: transaction store, KYC/CRM, device intelligence, a vector store of cases
OrchestrationA single Python pipelineLangGraph or Google ADK: stateful, auditable, resumable graphs
Risk scoringTransparent rules in codeGoverned, back-tested and model-risk reviewed
Audit trailA JSONL fileTrace observability and an immutable evidence store
AssuranceTool-call reliability not yet measuredAn eval harness on labelled alerts, then a shadow-mode pilot
Human gateA console promptAn analyst workbench showing reasoning, confidence and dissenting signals
Agentic AI · Payments Division
Appendix
Appendix 2
The Prototype · live run

What the agent actually does, alert by alert

  • Intake normalises the raw alert into a case file. No model: a rule is cheaper, faster and cannot be wrong.
  • Enrichment decides for itself which tools to call, pulling transaction history, customer profile and device intelligence.
  • Risk searches historical precedent, then scores with transparent weighted rules that return a reason for every point.
  • Case writes the analyst-ready summary, then the run stops at the human gate.
The score is deterministic rules, not the model.
A bank should not act on a number a black box invented. The model does the investigation and the narrative
4 tools called proactively
100% of steps in the audit trail
0 actions taken. The agent never acts alone
Agentic AI · Payments Division
Appendix
Appendix 3
The Business Case · method

How the number is built, so you can challenge it

  1. 30,000 alerts a month × 12 minutes of handle time = 6,000 investigation hours a month.
  2. A 50% reduction in handle time = 3,000 hours a month recovered, roughly 18 to 20 FTE of capacity.
  3. At a fully-loaded analyst cost of ~$130,000 a year, about $70 an hour = ~$210,000 a month.
  4. Annualised: ~$2.5M a year of recovered capacity, before any fraud-loss reduction.

What would move this

The alert volume and the handle time are the two sensitive inputs. Both are yours to supply, and both are measurable in a Discovery rather than estimated.

The 50% reduction is the assumption doing the most work. A shadow pilot measures it directly against analyst decisions instead of asserting it.

Every figure here is illustrative of a large Australian bank. None of it is a commitment.

Agentic AI · Payments Division
Appendix
Appendix 4
Resources

Take the deck with you

Sources
Method and tools
Free training course
AI Agent Code
Presentation download
Download PDF Download PPTX
Agentic AI · Payments Division
Appendix
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