Field guide
AI for fraud & financial crime
Real-time fraud scoring, AML alert triage and KYB/sanctions screening, built to survive a model examiner and a false-positive budget.
Financial crime is where AI in finance meets a hard operational constraint: every false positive is an analyst hour and an annoyed customer, and every false negative is a fine or a headline. The interesting engineering is not the classifier, it is the feature layer, the real-time scoring path, and the audit trail that lets a compliance officer defend a decision.
This guide covers the crime-fighting stack the way we build it: sub-second payment-fraud scoring, transaction-monitoring models tuned against a false-positive budget, AML alert triage and SAR drafting with a human in the loop, and KYB, sanctions and synthetic-identity checks wired into onboarding. Each piece is explainable enough to pass BaFin, FinCEN and EU AI Act scrutiny.
Current signals
- Instant-payment mandates leave a sub-second budget for the whole authorization path, so fraud models run on streaming features, not batch scores.
- Consortium and device-intelligence signals are now table stakes; first-party history alone misses first-time and mule accounts.
- AI-assisted alert triage is reframing AML productivity: models rank and enrich, analysts dispose, and the SAR narrative is drafted, not hand-written.
- Deepfake and synthetic-identity attacks are pushing verification from document checks toward behavioural and liveness signals.
In this guide
Transaction monitoring for crypto and stablecoin flows
On-chain data is public and unfamiliar. Here is how we build monitoring that ties chain analytics to your customers and your reporting obligations.
ReadBehavioural biometrics for continuous authentication
A password proves who logged in, not who is typing now. Here is how we use behavioural biometrics for continuous, privacy-aware authentication.
First-party vs third-party fraud: why the split matters
The same loss looks different depending on who caused it. Here is why separating first-party from third-party fraud changes the model and the treatment.
Adverse-media screening that cuts the noise
Name-match adverse-media screening buries analysts in irrelevant hits. Here is how entity resolution and relevance scoring make it usable.
Perpetual KYC: automating ongoing customer due diligence
Periodic KYC reviews are a backlog machine. Here is how we move to event-driven, perpetual KYC with a human review path for real changes.
Graph embeddings for AML and fraud
Hand-built graph features miss patterns embeddings can learn. Here is how we use graph representation learning for AML without losing explainability.
Automating chargeback and dispute handling
Disputes are a document and evidence problem at volume. Here is the extraction, evidence-assembly and outcome-prediction workflow we build for disputes.
Detecting friendly fraud and refund abuse
Friendly fraud looks like a real customer because it is one. Here is how we separate genuine disputes from abuse without punishing good customers.
Velocity rules and rate limiting for fraud, done right
Velocity rules are the oldest fraud control and the easiest to get wrong. Here is how we tune them against a false-positive budget alongside models.
Detecting money-mule accounts
Mule accounts look normal until the money moves through them. Here are the behavioural and network signals we use to find them before the payout.
Detecting scams and authorised push-payment fraud
In APP fraud the customer authorises the payment. Here is how we score intent and coercion signals to intervene without blocking legitimate transfers.
Risk-scoring merchants at onboarding
A bad merchant approved today is chargebacks and laundering tomorrow. Here is the data and scoring we build for acquirer and PSP onboarding.
Detecting transaction laundering in payments
Transaction laundering hides illegal sales inside a legitimate merchant account. Here are the signals and models we use to surface it.
Catching fraud rings with graph networks
Per-transaction scoring misses coordinated fraud because the signal lives in the connections between accounts. How we use graph features and GNNs to surface the rings.
Account takeover detection: the signals that actually work
Account takeover hides inside legitimate sessions. Here are the behavioural and device features, and the model design, we use to catch it without locking out real users.
Defending finance workflows against deepfakes and AI-agent abuse
Attackers now use generated voices, documents and their own agents against onboarding and payments. Here are the failure modes and the guardrails we build in.
Consortium data vs first-party data for fraud models
Network signals catch fraud your own data cannot see, but they add governance and lineage debt. Here is how we decide what to source where and keep it auditable.
Drafting SAR narratives with LLMs, safely
A suspicious activity report narrative is a legal document, not a chatbot answer. Here is how we generate grounded, reviewable SAR drafts that cite their evidence.
Automating KYB: business verification without the manual pack
KYB means resolving a business across registries, UBOs and documents nobody standardises. Here is the extraction, entity-resolution and risk-scoring pipeline we build for onboarding.
Detecting synthetic-identity fraud at application time
Synthetic identities pass traditional KYC because the pieces are real. Here are the data and model patterns we use to catch fabricated applicants before they book.
Sanctions screening that stops matching on spelling
Legacy sanctions screening fires on every fuzzy name match. Here is how entity resolution and context features cut hit volume while defending true-positive recall.
Automating AML alert triage with a human in the loop
Most AML alerts are noise, but you must review them anyway. Here is how we automate enrichment, prioritisation and disposition drafting while keeping the analyst accountable.
Real-time payment fraud scoring: an architecture for sub-second decisions
Scoring a payment for fraud in under a second means the model is the easy part. Here is the streaming feature path, latency budget and fallback design we build around it.
Cutting false positives in transaction monitoring with ML
Rule-based AML monitoring drowns analysts in false alerts. Here is the feature engineering and model layering we use to cut false positives without missing real suspicious activity.
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