[ AI fraud detection ]

Stop Bad Transactions Without Blocking Good Customers

We build real-time risk models that score payments, logins and claims in milliseconds, explain each decision for your analysts and learn from every confirmed case.

Plan Your Fraud PilotHow It Works
Card payment terminal printing a receipt on an orange background
Event (example)RiskDecision
  • Card ending 44216Approve
  • New device, new country91Decline
  • Card ending 01933Approve
  • Shared address, 4 cards72Review
  • Card ending 778114Approve
[ The problem ]

Why Rule-Only Fraud Systems Fall Behind

Rules catch yesterday's fraud. Attackers change patterns faster than teams can write new ones.

  1. 01

    False declines

    Strict rules block real customers, who rarely come back after a declined payment.

  2. 02

    Rules pile up

    Hundreds of overlapping rules become hard to tune and harder to explain.

  3. 03

    Coordinated fraud is invisible

    Rings using shared devices, cards or addresses look normal one transaction at a time.

  4. 04

    Slow feedback

    Chargebacks arrive weeks later, so models and rules learn too slowly.

[ How we build it ]

How We Build Your Fraud System

Models run in shadow mode next to your current controls until the numbers prove they are better.

  1. 01Fraud loss map

    Loss review

    We study confirmed fraud, chargebacks and false declines to see where money and customers are lost.

  2. 02Validated risk models

    Model build

    Behavioral, device and network features feed supervised and anomaly models trained on your labeled history.

  3. 03Side-by-side results

    Shadow testing

    Scores run alongside your rules on live traffic with no customer impact, so you compare catch rate and declines.

  4. 04Live scoring and case queue

    Go live

    Thresholds, review queues and appeal paths are set with your risk team, then monitored for drift.

[ Features ]

What the System Does

Built for analysts and auditors as much as for engineers.

01 / 06

Real-time scoring

Low-latency API in the payment, sign-up or claims flow, with a safe fallback if it times out.

[ Inputs ]

Data the Models Use

We map each source in the first weeks and only use the ones that improve the result.

  • Transactions and payment metadata
  • Device and session data
  • Account and KYC details
  • Chargebacks and confirmed cases
  • Login and password events
  • Claims or refund history
Your modelFraud Detection
[ What we measure ]

What We Measure With You

Targets are agreed during shadow testing; outcomes depend on your traffic and fraud mix.

01
Catch rate
Share of confirmed fraud stopped at decision time
02
False declines
Good transactions blocked per thousand
03
Decision latency
Time added to checkout or login
04
Review load
Cases sent to manual review per day
[ Industries ]

Where It Fits

  • 01Fintech and paymentsCard, wallet and bank transfer fraud at checkout.
  • 02BankingAccount takeover, mule accounts and first-party fraud.
  • 03eCommercePromo abuse, refund fraud and stolen card orders.
  • 04InsuranceClaims scoring and organized fraud rings.
  • 05Digital assetsWallet risk, withdrawal anomalies and sanctions screening support.
Woman entering card details on a laptop while shopping online

Shadow mode first

Prove It on Live Traffic Before It Decides Anything

Models score real transactions next to your current rules with no customer impact, so you compare catch rate and declines side by side.

Book a Fraud Call
[ Stack ]

Fraud Detection Stack

Low-latency serving with full audit trails.

Models

  • XGBoost
  • PyTorch
  • Graph neural networks
  • Isolation forest

Streaming

  • Apache Kafka
  • Redis
  • Apache Flink

Data

  • PostgreSQL
  • Neo4j
  • Snowflake

Serving

  • AWS SageMaker
  • FastAPI
  • Docker
  • Kubernetes
[ Cost ]

Fraud Detection Cost

Indicative starting prices. Cloud compute is billed to your account.

  1. 2 to 3 weeks

    from$2,500

    Fraud model assessment

    Loss review and a backtest on historical cases

  2. 8 to 12 weeks

    from$15,000

    Production scoring

    Real-time API, shadow testing and review queue

  3. 4 to 6 months

    from$40,000

    Fraud platform

    Multiple models, link analysis and case management

Get a Fixed Quote
[ Client voices ]

What Risk Teams Say

Rated 4.9/5 on average across the platforms clients review us on.

  • Clutch5.0
  • GoodFirms5.0
  • Google5.0
  • Upwork4.9
I had the pleasure of working with Sajal Tech on a travel website, and it was an incredible experience from start to finish. They demonstrated professionalism, clear understanding of requirements, and excellent communication throughout.
DKDaniel KwekaCEO, Darius Digital
[ FAQ ]

Fraud Detection Questions

Still deciding? A 30-minute call usually answers the rest.

Ask Us Directly

  • Not at first. Models usually run beside your rules, then take over the decisions where they perform better. Hard compliance rules stay in place.

  • Scoring APIs are designed to respond within the time budget of your checkout or login flow, with a fallback rule set if a call times out.

  • Yes. Every decision carries its main reasons and supporting evidence, which also helps with disputes and audits.

  • Yes. Anomaly detection and link analysis work with limited labels, and supervised models improve as reviewed cases build up.

  • We design to keep card data tokenized and out of the model where possible, and work within your PCI DSS scope and controls.

  • In your cloud account or data center. We work under NDA and follow ISO 27001 practices.

[ Related solutions ]

[ Contact ]

Let's build your next product together

Book a free strategy call and leave with a clear plan and estimate. No commitment.

  1. 01Pick a time that suits you
  2. 0230 minutes on scope, stack, timeline and budget
  3. 03Fixed-price proposal, NDA on request
Prefer email? contact@sajaltech.com