Fintechs, payment providers, acquirers, BNPL, remitters and EMIs live and die on approval rates, chargeback ratios and uptime. We engineer the fraud-prevention systems, real-time monitoring, chargeback tooling and security that keep money flowing and losses down — vendor-independent, and built to run in your stack.
You move money over the internet at scale, often with a small team. That combination is exactly what attackers look for — and every control you add pushes on approval rate, conversion and cost.
In a card-not-present world — hosted checkouts, in-app payments, recurring billing, digital wallets, pay-by-link — the attacker never has to hold a card. They only need the data. Stolen credentials are bought in bulk, tested against small transactions, then cashed out against whoever has the weakest controls. For a fintech or its acquiring partner, the exposure compounds: you carry the direct fraud loss, the downstream chargebacks, the scheme fees, and the reputational risk of landing in a card-network monitoring program if your ratios drift.
Card-not-present fraud is the dominant vector for anyone moving funds online, and no single control stops it. Authentication can be socially engineered, rules can be probed and evaded, and machine-learning models decay as behaviour shifts. Chargebacks and first-party ("friendly") fraud sit directly on the profit-and-loss line — and a dispute raised by the genuine cardholder cannot be caught by any authentication control, because the transaction was real. It has to be defended after the fact, with evidence. Account takeover turns a legitimate customer's own credentials against them through credential stuffing, bot attacks and SIM-swap-assisted resets. Onboarding and synthetic-identity fraud lets fabricated or stitched-together identities walk straight through a light KYC check and open accounts built purely to defraud.
And underneath all of it runs the false-decline tension: it is trivial to drive fraud toward zero by blocking enough transactions — the problem is what you destroy on the way. Every legitimate transaction your controls wrongly reject is a real customer turned away, and in aggregate false declines frequently cost more than the fraud they prevent, while never showing up on the fraud line. The hard part is not stopping fraud. It is stopping fraud and keeping the good money flowing, at scale, without a large fraud department. That is a systems problem — and systems are what we build.
An engineering-led financial-security firm working across fintechs, PSPs, acquirers, BNPL providers, remitters and EMIs — we find the holes the Big 4 don't, because we build in the same layer the attacker does.
Fraud-prevention systems built from rules and machine-learning scoring together — the deterministic layer catches what you know, the models find what you don't, and the two are tuned to your money flows rather than a vendor's defaults.
Transaction monitoring built and tuned for payments — velocity rules, risk scoring and step-up logic that decide in-flight, so you catch card testing and organised fraud without drowning good customers in friction.
Device fingerprinting and behavioural analytics engineered into your checkout, so one device cycling through many cards — or many "customers" on one machine — stands out early, with almost no friction for real users.
An authentication strategy that leans on the frictionless flow and reserves challenges for genuinely risky transactions — capturing the liability shift and protecting approval rate instead of blanketing every payment with a step-up.
Systems that assemble authentication records, device and IP history, delivery proof and transaction history into evidence files matched to each reason code — turning disputes from an unmanaged cost into something you actively contest and win.
When funds move before you can stop them, we trace them across accounts, rails and chains, reconstruct the flow, and produce the forensic evidence needed to support recovery and action.
Bespoke fraud engines, risk-scoring services and dispute tooling exposed as clean APIs and built to integrate with your existing payment stack — engineered for your business, not shelved as a policy pack.
Synthetic-identity detection at onboarding, plus authentication, MFA and step-up design, bot and credential-stuffing defence, and login-anomaly detection — stopping account takeover and fraudulent accounts at the front door.
Bank-grade application, cloud and infrastructure security, penetration testing and secure architecture across the systems that move and hold funds — because a fraud engine is only as safe as the platform it runs on.
There is a large market of fraud, authentication, orchestration and dispute vendors, and no single platform is best at everything. We architect for that reality.
Effective card-not-present defence is layered — each control is imperfect on its own, but together they raise the cost and lower the success rate for an attacker. Device fingerprinting spots organised fraud and card testing; behavioural signals separate a genuine customer from a script; velocity rules catch rapid abuse; machine-learning scoring finds the novel patterns rules miss; and risk-based step-up applies friction only where the signals warrant it. We build these layers so each catches what the others let through, and we tune the whole set to an explicit risk appetite rather than optimising any single number in isolation.
Because we are vendor-independent, we choose components on merit and keep your ability to swap them — no locking your entire defence into one supplier's roadmap. Where a payment-orchestration layer helps you route, test and combine tools without re-plumbing every integration, we use it; where a bespoke engine serves you better, we build it. And we design the feedback loop that keeps the whole thing alive: confirmed fraud and chargeback outcomes feed back to retrain models, retune rules and sharpen representment, so the system compounds instead of decaying silently as fraud shifts.
The difference from a document-led review is that we work in the same layer as the attacker. We build the authentication logic, the monitoring rules, the APIs and the infrastructure — so we find the holes a slide deck never surfaces. If you want the full detail on the payment-fraud stack, our fintech playbook for payment fraud and chargebacks walks through the CNP landscape, 3-D Secure 2, the chargeback lifecycle and the KPIs that matter. To see the full capability set, visit our services. When you are ready to find where payment fraud is really costing you, talk to a specialist or see pricing.
The services fintechs, PSPs and acquirers most often bring us in for — each one built and run, not just advised on.
Layered CNP controls — rules, scoring, device and behavioural signals — designed to stop fraud before it lands without crushing approval rate.
Learn moreReal-time monitoring and detection engineering for payments — model logic, thresholds and tuning that catch what matters and cut false positives.
Learn moreBespoke fraud engines, risk-scoring services and chargeback tooling, exposed as APIs and integrated with your payment stack.
Learn moreAuth, MFA and step-up design, bot and credential-stuffing defence, and synthetic-identity detection at onboarding.
Learn moreFollow the money across accounts, rails and chains when funds move before you can stop them — with evidence built to support recovery.
Learn moreApplication, cloud and infrastructure security, penetration testing and hardening across the systems that move and hold funds.
Learn moreWe build them. Financial Crime Advisory is an engineering-led financial-security firm — we write the fraud engines, detection logic, real-time monitoring, chargeback tooling and APIs that run in your payment flow, and we integrate them with your stack. Advice is part of the work, but the deliverable is a working system, not a slide deck. Everything we build is vendor-independent, so you are never locked into one supplier's roadmap.
By treating false declines as a real cost, not an afterthought. We tune the trade-off between fraud caught and good transactions kept using layered signals — device fingerprinting, behavioural signals, velocity rules and machine-learning scoring — so low-risk customers pass frictionlessly and only genuinely risky transactions are stepped up. Risk-based 3-D Secure 2 authentication concentrates challenges where they matter, protecting approval and conversion while cutting third-party fraud.
Yes. We build and tune the chargeback and representment operation: assembling authentication records, device and IP history, delivery proof and transaction history into evidence files matched to each reason code, and automating the parts that scale. We also cut disputes at the source through clear billing descriptors and frictionless refund paths, since many friendly-fraud claims start with a customer simply not recognising a charge.
That is exactly who we build for. Rather than adding headcount, we engineer controls that scale automatically — real-time monitoring, risk scoring and automated dispute handling — and stand up a lightweight operating model with clear ownership and feedback loops. You get bank-grade defence without a bank-sized team, and you keep the ability to run it yourself or have us tune it as your volume grows.
The Big 4 hand you a report and a bill; software vendors hand you a box to figure out. We engineer and run the actual systems that stop fraud, and because we build them we find the holes that document-led reviews miss — in your authentication logic, your monitoring rules, your APIs and your infrastructure. We are vendor-independent, so our recommendations are driven by what protects your money flows, not by a platform we are reselling.
Whether you're standing up a fraud program, fighting a rising chargeback ratio, or losing good customers to false declines — tell us how money moves through your business and where it hurts. We'll point you to the right first move.