What we build
A fraud stack that catches in milliseconds — and learns from every resolution.
Each capability is a production component — not a proof-of-concept — wired into your stack, documented for your risk committee, and monitored continuously.
Real-time decisioning (sub-100ms)
Transactions, logins, and applications scored inline before they commit. P99 under 100ms across the stack.
Network graph analytics
Account, device, and identity graphs detect ring fraud, collusion, and synthetic-identity clusters that single-applicant scoring misses.
Device + behavioural biometrics
Device fingerprinting, typing cadence, mouse movement, mobile sensor patterns — passive signals that don't add friction.
Application + synthetic-identity scoring
First-party and synthetic-ID models catch fraud at origination, before credit is extended.
Analyst case management
Built-in investigation workflow — case assignment, similar-case links, outcome capture — with the model learning from every resolution.
Adaptive thresholds + rules hybrid
ML scores plus deterministic rules, with thresholds auto-tuned to your false-positive tolerance and the cost of each fraud type.
Fraud domains we cover
One engine, every fraud vector.
Same ensemble scoring, graph layer, and analyst workflow — tuned per domain. Card, ATO, application, AML, merchant, insider all share the same infrastructure; only the feature set and thresholds change.
Card fraud (CNP + card-present)
Real-time authorisation scoring, 3DS step-up routing, chargeback reason analytics — for card issuers, acquirers, and PSPs.
Account takeover (ATO)
Login and session monitoring with device, behavioural, and geolocation signals — stops credential-stuffing and phishing-origin takeovers.
Application fraud + synthetic identity
First-party and synthetic-ID detection at origination — network-graph analytics surface rings that individual scoring misses.
AML transaction monitoring
Rule + model hybrid for suspicious patterns, structuring detection, and TBML typologies — with case workflow for SARs / STRs.
Merchant + e-commerce fraud
Checkout scoring, device trust, BIN analytics, and refund-fraud detection — for marketplaces and DTC brands.
Insider + first-party fraud
Employee-access anomalies, loan-stacking, friendly-chargeback patterns — models tuned to the tell-tales of internal or customer-driven fraud.
Model families we deploy
No single model catches every fraud. So we ensemble.
Each model family covers a distinct vector — device, graph, velocity, rules — blending their outputs into a single decision-ready score an analyst can always override.
Device fingerprint, network signals, typing cadence, mouse and touch patterns — high-signal features that cost the user nothing to generate.
Account, device, identity, and transaction graphs reveal rings, collusion, and synthetic-identity clusters in minutes, not quarters.
Burst detection, impossible-travel, unusual-merchant and unusual-amount patterns — calibrated to your own transaction history.
Hard-coded compliance rules (BIN blocks, sanctions, policy caps) sit alongside the ML layer, with per-rule confidence tracking for trust calibration.
Data sources wired into every model
Every signal that moves the decision — integrated.
Pulled in parallel, normalised into a single feature schema, versioned alongside the model that consumes them.
Explainability, not just blocks
A decline alone doesn't satisfy the chargeback committee. A trail does.
Every allow, challenge, block, or flag is accompanied by the exact signals that triggered it, per-model contributions, rule-engine hits, and the case record an analyst (or a regulator) can walk through.
- Per-model score contributions per decision
- Feature + rule-version provenance logged
- Customer-facing decline explanations
- Aligned to PCI DSS, MAS 626/656, PSD2 SCA
Compliance & governance
Built to pass the card scheme, the regulator, and the internal audit.
Regulator-ready from day one. Delivery includes documentation, model-risk review, champion/challenger infrastructure, and the monitoring your risk team will need when chargebacks get escalated.
PCI DSS — Level 1 ready
Card data handled to PCI DSS v4.0 controls. Tokenisation, segmentation, access control, and logging all built to the standard auditors expect.
MAS Notice 626 + 656
Real-time fraud monitoring and suspicious-transaction reporting aligned to Singapore's current AML/CFT supervisory expectations.
Model risk management (SR 11-7)
Every fraud model versioned, back-tested across regimes, documented for independent validation. False-positive + detection-rate trade-offs reviewed on a cadence.
GDPR / PDPA data handling
Personal and behavioural data processed with lawful-basis documentation, data-minimisation, and regional residency — auditable per record.
PSD2 SCA orchestration
3DS2, exemptions handling, risk-based authentication routing — all tunable to your regulator's current posture on RTS exemptions.
Champion / challenger + retraining
New fraud models ship as challengers alongside the champion — performance compared on a shadow-book before promotion, with one-click rollback.
Why Axccelerate for fraud
Not a rules box.
A fraud stack.
A rules vendor gives you a decline. Our stack gives you device, behavioural, graph, velocity, adaptive thresholds, and a case-aware analyst workflow — the infrastructure a modern fraud team actually needs.
Pricing
Priced to the fraud domain, not the transaction volume.
Fraud deployments are custom — we scope against your threat model, channels, and integrations before quoting.
Glossary
The vocabulary behind every decision.
A quick reference for the acronyms that show up in fraud operations — the terms your risk team, scheme contact, and regulator will all use.
- ATO
- Account Takeover
Unauthorised access to a customer's account, typically via credential stuffing, phishing, or SIM-swap.
- CNP
- Card-Not-Present
Transactions where the card is used without physical presence — online checkout, MOTO, recurring billing.
- CP
- Card-Present
Transactions where the physical card is used at a point-of-sale terminal — chip-and-PIN, EMV, contactless.
- 3DS
- 3-D Secure
The authentication protocol for CNP transactions — 3DS1 (legacy) and 3DS2 (current, with frictionless-flow support).
- SCA
- Strong Customer Authentication
The PSD2 requirement in Europe for two-factor authentication on most electronic payments; with risk-based exemptions.
- SAR
- Suspicious Activity Report
A report filed by a regulated institution to its FIU when it detects transactions suspected of money laundering or terrorism financing.
- STR
- Suspicious Transaction Report
The Asian (especially Singapore / Malaysia) term for the same filing — submitted to the Suspicious Transaction Reporting Office.
- OFAC
- Office of Foreign Assets Control
The US Treasury office that administers sanctions programs; its SDN list is one of the world's most widely screened.
- MCC
- Merchant Category Code
A 4-digit code assigned to merchants by card networks — used in fraud analytics, reward programs, and AML risk tiering.
- PCI DSS
- Payment Card Industry Data Security Standard
The set of security requirements for any entity that stores, processes, or transmits cardholder data.
- FP / TP
- False Positive / True Positive
Fundamental fraud-model metrics — FP is a legitimate transaction flagged as fraud; TP is a fraud correctly caught.
- EMV
- Europay, Mastercard, Visa
The global chip-card standard — underpins CP authentication and limits the skimming attack surface.
- TBML
- Trade-Based Money Laundering
Moving illicit value across borders through over- or under-invoiced trade transactions; a priority AML typology.
- SIM swap
- SIM swap fraud
Social-engineering attack where the attacker ports a victim's mobile number to their own SIM to intercept 2FA SMS; a common ATO vector.
Your fraud chain, in real time.
30-minute scoping with a fraud operator. You'll leave with a threat-model sketch, integration plan, and realistic timeline.