Ransomware Payments and AI: What Banks Should Fix

Ransomware payments jumped 77% in 2023—and then fell in 2024. That single swing tells you two things at once: criminals are still finding ways to get paid, and financial institutions are getting better at stopping the money flow.

For Australian banks and fintechs, this matters more than it seems. Ransomware isn’t “just” a cybersecurity incident inside a victim company. It’s a financial crime lifecycle that relies on payment rails, exchanges, mule accounts, and cash-out paths. If your transaction monitoring is slow, rules-heavy, or blind to cross-channel behaviour, you’re not just missing fraud—you’re helping ransomware economics keep working.

This post sits in our AI in Finance and FinTech series because the most practical way to respond isn’t another policy memo. It’s building AI-driven fraud detection and real-time transaction monitoring that spots ransomware-linked activity early, blocks cash-out, and produces defensible alerts your team can act on.

FinCEN’s trend line: what a 77% spike really signals

A 77% surge in ransomware payments in 2023 (as reported in FinCEN’s trend reporting) is a sign of scale and adaptation, not a one-off. Criminal groups aren’t only encrypting files and demanding payment; they’re professionalising operations: negotiating like sales teams, rotating infrastructure, and testing which payment paths are easiest to push through.

The decline in 2024 is encouraging, but I wouldn’t treat it as “problem solved.” Payment totals can drop for multiple reasons:

• Better detection and interdiction by banks, exchanges, and blockchain analytics providers
• More victims refusing to pay or restoring from backups
• Disruption of major groups’ infrastructure or arrests
• Migration to new typologies (extortion-only, data theft, harassment) that change how and when money moves

The operational takeaway for banks and fintechs: the environment is dynamic. If your controls rely on static rules (“flag any transfer over X to exchange Y”), attackers will route around you.

Why ransomware payment data is a finance problem

Ransomware is a crime where the payout often depends on:

1. Converting funds into crypto (or moving crypto between wallets)
2. Using money mules, nested services, or high-risk exchanges to obscure origin
3. Cashing out through off-ramps, OTC brokers, prepaid cards, or trade-based laundering

That’s a payments and monitoring problem. Your institution doesn’t need to see the malware to stop the business model. It needs to see the money pattern.

Why payments fell in 2024: the “proactive monitoring” effect

The most useful interpretation of the 2024 drop is this: the defenders are starting to win on friction. Ransomware operators succeed when payment is fast, reliable, and hard to claw back. When banks and fintechs increase friction—faster holds, smarter alerts, better mule detection—the economics weaken.

Here’s where AI in finance earns its keep. Traditional AML and fraud stacks often struggle with ransomware-linked activity because the signals are weak in isolation:

• A customer suddenly buying crypto isn’t automatically suspicious.
• A new payee isn’t automatically a mule.
• A higher-than-usual transfer might be legitimate.

But the combination of behaviours, timing, and network relationships is often distinctive. AI is good at combinations.

The reality: ransomware is a behavioural pattern, not a single red flag

Ransomware cash-out typically includes:

• Abrupt changes in transaction volume or destinations
• New device/login patterns (shared IPs, remote access tools, impossible travel)
• Burst activity (multiple transfers in short windows)
• Movement through known risk corridors (certain exchanges, payment aggregators, merchant categories)
• Links to mule networks (many inbound sources, rapid outbound consolidation)

A well-tuned behavioural model can treat this as a connected story instead of isolated events.

Where AI helps most: detecting the “payment moment” early

AI is most effective when it reduces time-to-intervention. Ransomware payments are time-sensitive: once funds are converted, mixed, and distributed, retrieval becomes unlikely.

1) Real-time transaction monitoring that adapts

Rules-based monitoring is brittle. Attackers learn thresholds and route around them. AI models—when governed properly—can score risk based on context:

• Customer history and baseline behaviour
• Payee and counterparty history
• Device and session risk
• Velocity, timing, and channel switching (mobile → web → branch)

This isn’t about replacing your AML program. It’s about making alerts earlier and fewer—fewer false positives, more true positives.

2) Behavioural analytics to spot mule accounts

Mule accounts are the connective tissue between ransomware and cash-out. In Australian fintech ecosystems, mule activity often shows up as:

• Many small inbound transfers from unrelated senders
• Rapid outbound transfers to a small set of accounts or exchanges
• Minimal balance retention (accounts act like pipes)
• Newly created accounts with thin identity signals

AI can classify mule-like behaviour using graph features (who transacts with whom), not just transaction amounts.

3) Network/graph analytics for “who’s connected to whom”

Ransomware ecosystems are networks: wallets, exchanges, shells, mules, devices, identities. Graph analytics can surface:

• Shared identifiers (device fingerprint, email patterns, IP ranges)
• Common beneficiary clusters
• Rapid movement through intermediary accounts

If you’re only looking at each account in a silo, you miss the network. Graph models make the network visible.

Snippet-worthy stance: “Ransomware survives on fast payments. Your job isn’t to find malware—it’s to slow down the money.”

Practical playbook for banks and fintechs (Australia-focused)

Australian banks and fintechs operate under strong regulatory expectations and a fast-moving payments landscape. That combination makes a clear playbook valuable.

Step 1: Treat ransomware as a first-class financial crime typology

If ransomware indicators live only in cybersecurity or only in AML, you’ll react late. Set up a shared typology library with:

• Known ransomware payment behaviours (cash-in, conversion, cash-out)
• Internal historical cases (confirmed fraud/AML incidents)
• High-risk corridors (entities, geographies, channels, product types)

This becomes training data for both humans and models.

Step 2: Add “conversion risk” to your monitoring

Ransomware payouts frequently involve conversion steps (fiat-to-crypto, crypto-to-crypto hops). Strengthen monitoring around:

• First-time crypto purchases after a long dormant period
• Sudden increases in on-ramp volume
• New beneficiaries tied to exchange rails
• Transactions that follow “panic patterns” (customer behaviour shifts immediately after account access changes)

Step 3: Improve alert quality with layered scoring

A common failure mode is drowning investigators in generic alerts. A layered approach works better:

1. Fast rules for obvious red flags (sanctions hits, blocked entities, known bad endpoints)
2. ML scoring for subtle behavioural anomalies
3. Graph risk for network links and mule clusters
4. Case orchestration that merges all signals into one narrative

The goal is fewer, richer cases—alerts that read like: “Customer X deviated from baseline, funded exchange Y, then transferred to beneficiary cluster Z associated with mule ring behaviour.”

Step 4: Build an “intervention ladder” that’s operationally realistic

Not every alert should trigger an account freeze. Create graduated controls:

• Step-up authentication and friction (extra verification)
• Short holds with rapid review queues
• Beneficiary confirmation (out-of-band checks)
• Limits on first-time beneficiaries or new device transfers
• Full freeze/escalation for high-confidence, multi-signal cases

This matters for customer experience. It also matters for defensibility when you need to explain actions to regulators or customers.

Step 5: Measure what actually changes outcomes

If you’re doing AI-driven fraud detection, measure beyond “model AUC.” Track operational metrics that reflect ransomware disruption:

• Median time from first suspicious event to investigator review
• False positive rate by segment (SME, retail, corporate)
• % of high-risk transfers stopped before leaving the bank
• Mule ring disruption rate (accounts offboarded, beneficiaries blocked)

If these aren’t moving, the program is theatre.

Common questions teams ask (and straight answers)

“Does the 2024 drop mean ransomware is fading?”

No. It means attackers are facing more friction in some channels, and they’re adapting. Your controls should assume displacement, not disappearance.

“Is ransomware mostly a crypto problem?”

Crypto is a frequent rail, but ransomware is a payments problem overall. Mules, domestic transfers, and alternative cash-out methods still show up in bank data.

“Will AI reduce investigator workload or add more noise?”

It can do either. AI helps when you:

• Use high-quality labels (confirmed cases)
• Combine transaction, device, and identity signals
• Tie scores to clear intervention steps

If you bolt a model onto messy alert workflows, you’ll just create faster noise.

What to do next: turn the 2024 drop into a repeatable advantage

The 77% rise in 2023 and the decline in 2024 is the clearest message FinCEN-style reporting can send: payment ecosystems can either amplify ransomware or starve it.

If you’re leading fraud, AML, risk, or product in an Australian bank or fintech, the next move is practical: map your ransomware payment pathways, identify where you can intervene fastest, and upgrade from static rules to AI-driven transaction monitoring and behavioural analytics that investigators trust.

A useful question to pressure-test your program: If a ransomware victim tried to move funds through our rails this afternoon, would we catch the conversion and cash-out steps before the money disappears—or would we file a report after the fact?