We deliver AI fraud detection services for fintech platforms, payment processors, eCommerce marketplaces, and digital wallets that need to score risk in real time. ScalaCode has 13 plus years of engineering delivery, ISO 9001 certified processes, AWS Advanced Tier status, and 250 plus engineers serving clients across 45+ countries.
Whether you are scoring transactions in milliseconds, screening new account openings, detecting account takeover patterns, or surfacing first-party fraud rings, we ship models that target measurable outcomes. Our teams tune for false positive rate, fraud loss reduction in basis points, and sub-100ms decision latency at the edge of the payment flow.
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AI fraud detection services build systems that score financial and behavioral risk in real time. The work covers transaction scoring, account opening fraud screening, account takeover detection, anti-money laundering pattern detection, refund abuse identification, and synthetic identity detection. Fintech platforms, payment processors, eCommerce marketplaces, and digital wallets use AI fraud detection services to reduce fraud loss, lower false positive rates, and meet regulatory reporting requirements.
We deploy gradient boosted models on Kafka and Flink with Triton Inference Server for sub-100ms scoring. Each transaction passes through feature lookup, model inference, and rule overlay before authorization. We build the data path, the model, the serving layer, and the fallback rules for outages.
Our onboarding fraud stack combines device fingerprinting, behavioral biometric signals, and document verification API calls in one risk decision. We build the orchestration layer, score new applicants in under two seconds, and route borderline cases into your manual review queue with a clear reason code.
We build graph based AML systems using Neo4j or TigerGraph for network analysis across accounts, devices, and counterparties. These systems flag structuring patterns, circular transfers, and sanctions list matches. Alerts feed your case management tool with a ranked priority score and a network map.
Our account takeover models score login attempts, session navigation, and post-login actions against the user historical pattern. We flag credential stuffing waves, impossible travel signals, and bot driven session replays. Step up authentication is triggered through your existing auth provider with a one line API call.
We build models for return fraud rings, promo code abuse, and friendly fraud chargebacks. The system clusters customers by behavior, flags coordinated abuse patterns, and supports policy decisions with evidence. Risk operations gets a dashboard. Product teams get an API for in app friction.
Synthetic identity rings cross many platforms before they cash out. We build identity graphs that correlate signals across emails, devices, addresses, and phone numbers. The graph surfaces clusters that pass single record checks but fail at the network level. Output goes to your decision engine as a risk score.
We map your fraud surface, review historical loss data, and rank the highest value vectors to attack first. The output is a written threat model, a target metric for the pilot, and a data inventory you can sign off on.
We assemble training data, build features, select algorithms, and run backtests against your last six months of labeled fraud cases. We share a model card with precision, recall, lift curves, and the business case for the chosen score cutoff.
We ship the model into your production payment flow or onboarding path, run a shadow mode review, then cut over with rollback gates. Risk operations gets a runbook for incidents, score overrides, and emergency kill switches before go live.
We monitor drift, retrain on fresh fraud labels, and review model output with your risk team every two weeks once live in production. Quarterly model rebuilds are scoped, billed against the retainer, and follow the same audit trail as the first build.
Rate transparency. Hourly $13 to $25. Monthly $1,200 to $4,000.
13 plus years of payments and risk engineering experience across our delivery teams.
ISO 9001 certified delivery with audit ready documentation for every model release.
IP transfer on day one. Models, weights, training data, and code stay with you.
Engineering managers run delivery, not project managers. Technical calls happen fast.
Sub-100ms transaction scoring requires Kafka, Flink, and Triton mastery.
Account takeover and synthetic identity detection use Neo4j or TigerGraph patterns.
PCI DSS, AML, KYC, and audit logging requirements vary by jurisdiction.
Production systems should track and improve FPR weekly.
Monthly retraining with drift detection is standard for production fraud models.
Feast, Tecton, and custom feature stores have different operational profiles.
Fraud detection requires PII handling. Engineers should work inside customer VPCs.
A fraud focused pod of data scientists, ML engineers, and risk analysts working as your extended team. Monthly billing. Direct reporting line.
One fraud vector, one model, one production deployment. Defined scope, defined price, shipped and tuned. Good first project for new partners.
Your existing fraud platform is missing losses or blocking good customers. We audit the system, ship a fix plan in two weeks, and execute.
| Decision Factor | ScalaCode | Fraud SaaS Vendor | In-House Build |
|---|---|---|---|
| Model ownership | You own models, weights, and code | Vendor owns the model | You own it |
| Time to first model in production | 8 to 12 weeks | 2 to 4 weeks for stock model | 6 to 9 months |
| Tuning to your fraud patterns | Yes, retrained on your labels | Limited tuning | Yes, full control |
| Pricing model | Hourly $13 to $25 or monthly retainer | Per transaction fee | Salary plus tooling |
| Audit logs and documentation | ISO 9001 documented | Vendor controlled | Depends on team |
Typical AI fraud detection engagement structures and cost bands. These figures represent observed market ranges, not ScalaCode quotes.
| Engagement Type | Typical Duration | Typical Cost |
|---|---|---|
| Initial Risk Model | 8 to 12 weeks | $80K to $180K |
| Real-Time Transaction Scoring | 12 to 18 weeks | $120K to $280K |
| AML Pattern Detection | 16 to 24 weeks | $150K to $380K |
| Account Takeover System | 12 to 18 weeks | $120K to $280K |
| Onboarding Fraud Screening | 10 to 16 weeks | $90K to $220K |
| Production Stabilization | 30 day window | $20K to $50K |
Account opening risk, transaction monitoring, AML alerting, and case investigation workflows for compliance teams.
Authorization scoring, chargeback prediction, merchant onboarding risk, and acquirer side rules tuning at scale.
Refund abuse, seller fraud, account takeover detection, and cross border payment risk scoring at checkout.
Bonus abuse rings, multi accounting, payment fraud, and responsible play signal detection across deposits.
SIM swap fraud, subscription fraud, dealer commission abuse, and roaming usage anomaly detection across prepaid and postpaid lines.
Claims fraud scoring, application risk, provider network anomaly checks, and staged accident pattern detection on motor lines.
$13-$15/hr
$18-$20/hr
$23-$25/hr
$1,200-$1,500/month
$1,800-$2,400/month
$2,600-$3,200/month
$3,200-$4,000/month
I looked around at several developers to compare costs, but they didn’t fit within my budget. Finally, I reached out to a company in India called ScalaCode. We set up several online meetings over a couple of weeks and came up with an app that did exactly what I wanted within my budget. I can confidently say that ScalaCode has been an excellent choice for me.
Ruddy McKenzie
Founder of RM EPOSIn this heartfelt testimonial, James Ellis, the founder of TipStars, shares his transformative experience working with ScalaCode. He highlights how ScalaCode's expert team helped turn his vision of a tipping platform for artists and art lovers into a reality. James praises their innovative approach, dedication, and seamless project execution, which played a crucial role in the success of TipStars. This platform now empowers artists and enhances the experience for art enthusiasts, thanks to ScalaCode's exceptional development skills.
James Ellis
Owner, Artist-Tipping PlatformScalaCode provides great results, uplifting the collaborative experience with their impressive project management style. The team always delivers as expected, which is manifested by the length of the ongoing relationship with us. Overall, their services have been impressive.
Jaa St. Julien
Pres. & Chief Strategy Officer - St. Julien CommunicationsI have been working with ScalaCode for almost a year and half now. I have this project 4Sale, it’s a marketplace application. I contacted them for the project and we started around 2021. The company is very responsive and always take the extra mile to help you out. I highly recommend them; if you have a project, contact this company. They always respond on time even though there’s a time difference.
Manuel
CEO, 4SaleThe application was basically built from scratch, and was complicated, as the software was to be integrated with a certain Medical EHR software. As the CEO of SHG, I was very pleased with the services, expertise, and support we received from ScalaCode, from the beginning directly through the first LIVE implementation.
Stephen Holmes
CEO, Steve Homes GroupThe iOS and Android apps exceeded the expectations of the internal team. ScalaCode crafts high-quality products that are easy to use and fit the requirements of the client. The team is technically experienced, hard-working, and knowledgeable.
Carolyn Dare
Director, Empowered AchieverI needed a reliable team on-hand, and ScalaCode delivered. Their excellent availability and project oversight made a big impact.
Faid Lalji
Learn ArenaOur XR project had unique hurdles, but ScalaCode grasped it fast and delivered beyond expectations with excellent collaboration.
Alessandro
CEO / Founder (XR Company)A focused pilot runs eight to twelve weeks from kickoff to production. Shadow mode review starts at week six, and full traffic cutover follows after sign off.
We use proven libraries like XGBoost, LightGBM, and PyTorch and tune them on your data. Custom architectures get built only when the fraud pattern requires it.
We work inside your cloud account or VPC, never copy raw PII out, and follow your data classification rules. Engineers sign NDAs and use named accounts only.
Yes. We integrate as an API call in your authorization path or as an asynchronous score consumer on your event bus. Both patterns are common in our builds.
We monitor population stability index, score distribution, and label feedback every day. Retraining is scheduled monthly or triggered when drift breaches thresholds your team sets.
Every decision is logged with input features, model version, score, and reason codes. Logs are retained per your policy and exported to your audit system on demand.
We track fraud loss in basis points of authorized volume, false positive rate at the chosen score cutoff, and review queue volume. Reports go to your risk team weekly.
Our XR project had unique hurdles, but ScalaCode grasped it fast and delivered beyond expectations with excellent collaboration.
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