Case Study: Context

Australia’s financial sector is a $140 billion engine of GDP and employs roughly 450,000 people—making scams not just a consumer issue but a systemic operating risk. The National Anti-Scam Centre has set out to “make Australia a harder target for scammers,” while ASIC has repeatedly taken down investment scam sites misusing celebrity images. Yet the attack surface keeps expanding: social engineering now rides on deepfakes, spoofed caller IDs and hijacked business email threads. Research from FICO and others signals a regulatory turn—globally, banks face growing responsibility to compensate victims of authorised push payment (APP) scams, mirroring the UK’s mandatory reimbursement framework. Awareness weeks help, but the strategic challenge is to prevent losses at source without breaking the customer experience.

Decision

ANZ’s move from education-first to prevention-led fraud strategy offers a blueprint. Between October 2024 and June 2025, the bank reported a 15% reduction in customer scam losses and prevented/recovered over $100 million. The decision thesis: treat scams as a controllable cost of doing business and invest in a multi-layer defence that integrates technology, human intervention and ecosystem cooperation. The bank prioritised controls that intercept scams early (pre-payment), slow them in-flight (cool-offs and interdiction), and recover funds post-event (rapid freezing and mule-hunting). Governance shifted accordingly—board-level risk appetite, clear loss-ownership across product lines, and funding tied to measurable prevention outcomes rather than campaign reach.

Implementation

ANZ combined customer-centric protections with deep analytics and network action. Five layers define the operating model:

1) Payment initiation controls. Features such as ANZ’s Digital Padlock give customers the ability to lock down higher-risk payment types unless explicitly authorised, creating opt-in friction at the moment of risk. Step-up authentication for first-time payees, dynamic warnings, and cooling-off periods for flagged transfers slow the typical scam playbook of hurry-and-pay.

2) Identity and payee verification. Banks increasingly deploy name-matching alerts (e.g., “NameCheck”-style warnings) to flag mismatches before funds move. Behavioural biometrics and device fingerprinting monitor anomalies: keystroke patterns, unusual session speeds, new devices, or remote-access tools active on a customer’s handset—common markers in remote support scams.

3) Real-time transaction analytics. Machine learning models score outbound and inbound payments using features such as value spikes, payee risk, network velocity and mule typologies. Models are refreshed with recent scam campaigns (e.g., impersonations of government agencies or telcos), and degrees of confidence drive treatment paths from soft nudges to hard blocks.

4) Human-in-the-loop escalation. Specialist scam teams blend scripted diagnostics (“Are you on a call right now?”) with evidence-based guidance. Branch or video callbacks can validate high-value payments for vulnerable cohorts (e.g., first-time investors) without blanket frictions. A midwestern US community bank found that training and rewarding frontline staff for spotting social engineering lifted internal scam reporting materially and prevented high-value losses—proof that people remain a critical sensor network alongside AI.

5) Ecosystem coordination. Losses often exit the originating bank within minutes. ANZ’s results reflect faster cross-bank freezing of funds, tighter mule account offboarding, and cooperation with telecoms on SMS sender ID protections. ASIC’s takedowns and platform ad-quality controls shrink the top of the funnel, while the National Anti-Scam Centre improves signal sharing. The strategy recognises that no single bank can win a networked crime alone.

Results (with numbers)

ANZ’s reported outcomes are notable in a hostile environment: a 15% reduction in customer scam losses over nine months and more than $100 million in prevented and recovered funds. While every portfolio mixes differently, these numbers demonstrate that pre-emptive controls can shift loss curves even as scam volumes rise. If a bank invested, say, $20–30 million in the first year across analytics talent, model ops, platform features and ecosystem participation, the avoided losses at ANZ’s scale indicate a plausible multi‑x return on investment—before considering secondary benefits like lower complaint handling costs and reduced regulatory capital for operational risk.

Regulatory dynamics sharpen the business case. The UK’s Payment Systems Regulator is implementing mandatory APP reimbursement across sending and receiving banks, explicitly designed to “shift incentives upstream.” Consumer sentiment is aligned: surveys show growing expectations that banks reimburse scam losses. In short, preventing the payment is cheaper than reimbursing it. Meanwhile, global data breach studies (IBM reports average breach costs near US$5 million) underscore the combined cost of identity theft and account takeover that often precede scams.

Lessons

- Prevention beats awareness. Behavioural science is clear: in-the-moment frictions and defaults outperform education alone. Contextual warnings with clear choices reduce compliance bias.

- Design for adversaries, not averages. Scammers pivot fast. Treat models and rules as living products with weekly refreshes and red‑team exercises simulating current scam scripts.

- Close the recovery loop. Rapid freezing, network tracebacks and mule closures compound prevention gains. Measure minutes-to-freeze and closure rates alongside detection precision.

- Make customers co‑defenders. Opt-in locks, payment scheduling holds and default daily limits give users defensive posture by design—without needing to be fraud experts.

- Align incentives across the ecosystem. Shared liability and common standards (messaging registries, confirmation-of-payee, takedown SLAs) compress the scammers’ arbitrage space.

Technical deep dive: what’s working now

Best-in-class stacks fuse supervised ML (gradient-boosted trees for tabular transaction data) with anomaly detection (autoencoders for behaviour baselines) and graph analytics for mule rings. ISO 20022 payment enrichment offers more features per transaction—remittance purpose codes, structured names—to improve precision. On-device signals (SIM swap checks, integrity attestation) mitigate spoofed sessions. Feature stores and model governance reduce drift and bias, while real-time decision engines route cases to human review when latency budgets allow.

Future outlook

Three shifts will define the next two years: first, liability migration from consumers to institutions will continue, accelerating investment in pre‑payment controls and cross‑bank intelligence. Second, generative AI will industrialise impersonation—voice cloning, synthetic documents—raising the premium on out‑of‑band verification and secure consent frameworks. Third, network‑level defences—confirmation-of-payee at meaningful scale, scam intelligence exchanges, and coordinated mule eradication—will emerge as competitive differentiators. Early adopters will convert fraud budgets into margin protection and trust equity, while laggards face rising reimbursement costs, regulatory scrutiny and brand damage.

For Australian banks, the message is clear: move beyond awareness. Treat scams as an engineering and ecosystem problem with board‑level ownership. The ANZ case shows that decisive investment can measurably bend the loss curve, protect customers, and create a reputational moat in a market where trust is the ultimate currency.