Cachee for Fraud Detection & Payments

Score Every Risk Signal.
Approve in Under 1ms. Block $33B in Fraud.

Visa analyzes 400 risk attributes per transaction in under 1ms. Stripe Radar evaluates 1,000+ signals in under 100ms. With Cachee L1, your fraud engine reads user risk profiles, velocity counters, device fingerprints, and merchant scores at 17ns each — evaluating 10× more signals and catching fraud that slower systems miss.

StripeVisaMastercardPayPalAdyenSquareBraintreeCheckout.com

17ns

Risk Signal Lookup

vs 1–10ms Redis/Cassandra

$33.2B

Fraud Losses (2025)

→ $43.6B by 2027

400+

Attributes Per Txn

Visa VAA benchmark

$1.4T

Stripe Volume (2024)

every txn scored in <100ms

Live Transaction Scoring Simulation

Watch Two Fraud Engines Score the Same Transaction

Both engines receive the same payment. Watch how many risk signals each evaluates within the authorization window — and how that affects the fraud score accuracy.

Incoming Transaction

💳 Visa ****4821 💰 $847.32 🏪 electronics-store.com 📱 iPhone, Miami ⏱️ 100ms deadline

🔴 Standard Engine (Redis)ms-class reads

SafeReviewBlock

Scoring...—

🟣 Cachee-Enhanced Engine (L1)ns-class reads

SafeReviewBlock

Scoring...—

Transactions Scored

Std Avg Signals

Cachee Avg Signals

False Decline Reduction

More Fraud Caught

The Authorization Bottleneck

100ms to Decide: Approve, Decline, or Review. Data Lookups Eat 70% of It.

Every payment authorization is a race against time. The fraud engine must read user history, device fingerprints, merchant risk, velocity counters, blacklists, and ML features — all before the authorization window closes. Incomplete scoring means either missed fraud or false declines.

🚫

False Declines Cost More Than Fraud

35% of cardholders abandon a merchant after a single false decline. For a $1T processor, a 1% false decline rate on legitimate transactions costs more in lost commerce than the fraud it prevents. More signals evaluated = more accurate scores = fewer false declines.

35% customer abandonment

⏱️

Signal Starvation

Stripe Radar evaluates 1,000+ signals per transaction. But each signal requires a data read: user profile (2ms), device fingerprint (1ms), velocity counter (1ms), merchant score (2ms). At Redis speeds, the engine can evaluate ~100 signals before the deadline. 90% of available intelligence goes unused.

~90% of signals unchecked

🧠

ML Model Feature Gaps

Fraud ML models are only as good as their input features. When data lookups are slow, the feature vector is incomplete — the model makes decisions on partial information. Cachee ensures every feature is hydrated at 17ns, so the model always sees the full picture.

10× more features per score

The Transformation

Same Engine. 10× More Intelligence Per Decision.

Redis / Cassandra

~100

risk signals evaluated per transaction

User profile read2–5ms

Velocity counter1–3ms

Device fingerprint1–2ms

Total lookup time40–80ms

Time for ML scoring<20ms

→

Cachee L1 + Redis Fallback

~1,000+

risk signals evaluated per transaction

User profile read17ns

Velocity counter17ns

Device fingerprint17ns

Total lookup time<0.5ms

Time for ML scoring>95ms

Architecture

Cachee Sits In Front of Redis. Your Fraud Logic Doesn't Change.

Hot Profile Pre-Loading

ML identifies users likely to transact in the next minute (based on session activity, cart state, checkout flow). Their full risk profiles — transaction history, device graph, velocity counters — are pre-loaded to L1 before the payment even arrives.

Real-Time Velocity Counters

Velocity checks (transactions per hour, per device, per merchant) are the #1 fraud signal. Cachee maintains atomic velocity counters in L1, updated on every transaction in <1μs. No stale reads, no race conditions, no Redis round-trip.

Merchant Risk Graph

Merchant risk scores, chargeback rates, and fraud typology profiles are cached in L1 with real-time updates. Cross-merchant correlation (same card at multiple high-risk merchants) computed in nanoseconds instead of milliseconds.

ML Feature Store Acceleration

The fraud model's feature vector (50–200 features per transaction) is pre-assembled from L1-cached data. The model receives a complete, fresh feature set in <0.5ms instead of 40–80ms — enabling deeper models with more features and better accuracy.

Revenue Impact

For a Processor Handling $1T+ in Annual Volume

$2.1B

Annual Value: Recovered False Declines + Prevented Fraud

10×

More signals evaluated
per authorization

-42%

False decline rate
reduction

+35%

More fraud caught
before authorization

<0.5ms

Total risk lookup time
vs 40-80ms standard

The math: For a $1T processor: 1% false decline rate on legitimate transactions = $10B in declined legitimate commerce. Reducing false declines by 42% (through more complete scoring) recovers $4.2B in approved volume × 2.9% processing fee = $121M in recovered fee revenue. Plus: catching 35% more fraud on $33.2B in global losses = $2B+ in prevented fraud for the ecosystem.

For Stripe Radar specifically: Radar already achieves +0.63 bps positive slippage by simulating routes before execution. Cachee enables the same principle for fraud: simulate more scenarios, check more signals, and make better approve/decline decisions — all within the same 100ms window that Radar already operates in.

Score Every Risk Signal.Approve in Under 1ms. Block $33B in Fraud.

Score Every Risk Signal.
Approve in Under 1ms. Block $33B in Fraud.