Practical guides for risk teams building fraud detection on digital payment platforms.
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Most fraud teams find gaps in their stack when chargebacks start climbing, not before. A structured audit process lets you find the coverage gaps before attackers do -- mapping transaction flows, analyzing reason codes, identifying threshold decay, and measuring false positive cost.
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Digital wallet transactions have a fraud risk profile that differs from card-present and CNP. The controls that work on card networks often miss the wallet-specific patterns.
Velocity checks are one of the most widely used fraud controls and one of the most frequently misconfigured. A practitioner guide to calibrating thresholds and avoiding common mistakes.
Rules-based and ML scoring operate on fundamentally different representations of a transaction. A look at the pattern classes where each has the edge, and why the two approaches are complementary.
Chargebacks show up on your P&L. False positive declines are mostly invisible -- but the aggregate cost is often higher than the fraud you are preventing. Here's how to measure it.
Every fraud platform starts with rules. We looked at why risk teams were writing the same rules year after year and catching fewer new patterns. This is what we built instead.
Most fraud API integrations get bolted on after the gateway call -- which means the score arrives too late to affect authorization. Here's the correct integration pattern.
A marketplace customer lost $40K to card testing in a single week before we helped them close the pattern. A reconstruction of the attack, what their existing controls missed, and what stopped it.
Account takeover has a behavioral signature that most transaction-level rules engines miss entirely. A practitioner's guide to the signals worth watching and what your fraud stack needs.
The authorization window is 40-120ms -- not long, but enough to evaluate 200+ signals and return a scored result. A look at what data is available before auth and how to use it.