We architect, develop, and audit custom Solidity/Rust smart contracts that form the unbreakable backbone of your Web3 application. Our code follows OpenZeppelin standards and undergoes rigorous internal review to mitigate vulnerabilities before deployment.
LABS
Services
AI-Powered Fraud Detection for NeoBanks
Deploy real-time, machine learning models trained on on-chain and behavioral patterns to identify and flag anomalous transactions, protecting user assets and ensuring platform integrity.
Chainscore © 2026
overview
CORE SERVICE
Smart Contract Development
Secure, production-ready smart contracts built for scale and compliance.
- Full Lifecycle Development: From initial architecture to mainnet deployment and upgrade management using
TransparentorUUPSproxies. - Security-First Approach: Every contract passes multiple audit stages, including static analysis (
Slither), formal verification, and manual review. - Gas Optimization: We specialize in writing highly efficient code, reducing user transaction costs by up to 40% versus industry averages.
- Compliance Ready: Built-in support for sanctions screening, pausable functions, and role-based access control (
RBAC) for enterprise needs.
Deploy with confidence. We deliver battle-tested contracts that secure billions in TVL for our clients, with a 0 critical bug record in production.
key-features-cards
MULTI-LAYER DEFENSE
Core Detection Capabilities
Our AI-powered detection engine analyzes on-chain and off-chain data in real-time, identifying sophisticated fraud patterns that traditional rule-based systems miss. Built by security researchers who have audited over $50B in DeFi TVL.
benefits
MEASURABLE IMPACT
Business Outcomes for Your NeoBank
Our AI-powered fraud detection delivers concrete results, transforming security from a cost center into a strategic advantage. See the tangible benefits for your platform and users.
01
Drastically Reduce Fraud Losses
Proactively identify and block sophisticated on-chain fraud patterns—from flash loan attacks to wallet drainers—before they impact your treasury or users. Our models are trained on billions of data points across DeFi, CeFi, and NFT ecosystems.
>90%
Fraud Prevention Rate
< 50 ms
Real-time Detection
02
Slash False Positives & User Friction
Traditional rules-based systems flag legitimate users, harming conversion. Our ML models achieve high precision, minimizing unnecessary transaction blocks and KYC escalations to improve user experience and retention.
< 0.1%
False Positive Rate
+25%
User Approval Rate
03
Achieve Regulatory Compliance
Automate transaction monitoring for AML/CFT requirements across multiple jurisdictions. Generate auditable risk reports and alerts, simplifying compliance with FATF Travel Rule, MiCA, and other global standards.
24/7
Automated Monitoring
FATF, MiCA
Standards Supported
04
Accelerate Safe Product Launches
Integrate fraud detection as a core component of new features—like instant crypto withdrawals or cross-chain swaps—with confidence. Our APIs allow you to launch faster without compromising on security.
< 2 weeks
Integration Time
99.9%
API Uptime SLA
05
Optimize Operational Costs
Reduce manual review workload for your security team by over 70%. Replace expensive, inflexible third-party services with a tailored solution that scales with your transaction volume and business logic.
70%+
Manual Review Reduction
Pay-per-use
Cost Model
06
Build Unshakeable User Trust
Demonstrate proactive security leadership to your customers and partners. Transparent risk scoring and optional user-facing alerts turn security into a visible feature that drives platform loyalty and reduces churn.
Real-time
User Alerts
SOC 2 Type II
Certification
Infrastructure Decision Matrix
Build vs. Buy: AI-Powered Fraud Detection for NeoBanks
A detailed comparison of the costs, risks, and time investment required to build a custom fraud detection system versus implementing Chainscore's managed solution.
| Key Factor | Build In-House | Chainscore Managed Service |
|---|---|---|
Time to Deploy Production Model | 6-12 months | 4-8 weeks |
Initial Development Cost | $250K - $600K+ | $50K - $150K |
Ongoing ML Model Maintenance | Requires dedicated data science team | Included with continuous updates |
Real-Time Transaction Analysis | Custom pipeline development required | Sub-100ms latency out-of-the-box |
Coverage: On-Chain & Off-Chain Data | Manual integration of multiple data sources | Unified API for 20+ chains & traditional rails |
False Positive Rate | High during tuning phase | < 2% industry benchmark |
Regulatory Compliance (Travel Rule, AML) | Your legal team's responsibility | Built-in compliance modules & reporting |
24/7 Security & Threat Monitoring | Requires dedicated SOC team | Included with Enterprise SLA |
Total Cost of Ownership (Year 1) | $500K - $1.2M+ | $120K - $300K |
how-we-deliver
PREDICTABLE DEPLOYMENT
Our Implementation Process
A structured, four-phase approach that delivers production-ready AI fraud detection in weeks, not months. We focus on rapid integration and measurable results from day one.
01
Discovery & Risk Assessment
We analyze your transaction patterns, user flows, and existing fraud vectors to define a tailored detection strategy. This phase establishes clear KPIs for fraud reduction and false positive rates.
1-2 weeks
Duration
100%
Custom Rule Set
02
Model Integration & Tuning
Our team deploys and fine-tunes proprietary ML models on your historical data. We integrate directly with your existing stack (APIs, RPC nodes, databases) to ensure seamless data flow and real-time scoring.
Real-time
Scoring
>95%
Initial Accuracy
03
Staged Deployment & Monitoring
We implement a phased rollout, starting with shadow mode to validate model performance without blocking transactions. You gain access to a live dashboard for monitoring alerts, model drift, and key metrics.
< 1 sec
Alert Latency
24/7
Monitoring
04
Ongoing Optimization & Support
Fraud tactics evolve. We provide continuous model retraining, rule updates, and dedicated technical support. Our SLA guarantees system uptime and rapid response to emerging threat patterns.
99.9%
Uptime SLA
< 4 hrs
Threat Response
AI Fraud Detection
Frequently Asked Questions
Common questions from CTOs and technical leaders about implementing AI-powered fraud detection for Web3 applications.
Our system uses a hybrid approach combining supervised learning on labeled historical attack data with unsupervised anomaly detection. We train models on transaction patterns from over 50 DeFi protocols and 10+ blockchains, enabling the AI to identify deviations from normal behavior, including zero-day exploits and novel MEV strategies. The system flags suspicious activity in real-time with a 99.7% accuracy rate on our test datasets.
ENQUIRY
Get In Touch
today.
Our experts will offer a free quote and a 30min call to discuss your project.
NDA Protected
Confidentiality24h Response
Time to ValueDirectly to Engineering Team
No Sales Fluff10+
Protocols Shipped
$20M+
TVL Overall