Contact Us
FinTech Platform Development: AI-Powered Financial Product with 99%+ Fraud Detection

FinTech Platform Development: AI-Powered Financial Product with 99%+ Fraud Detection

Case Study

Mark Cuban argues that in the AI era, trade secrets matter more than patents. In fintech, everyone has access to Stripe, Plaid, and open-source ML frameworks. So when a fast-growing fintech company came to Dreamztech, they did not need generic technology — they needed a competitive advantage that could not be copied. Dreamztech built a custom AI-powered platform achieving 99.7% fraud detection accuracy and processing over $48M in transactions within the first year.

  • Industry: FinTech & Financial Services
  • Solution: AI-Powered FinTech Platform
  • Delivery: Full Stack Platform Development
Discuss Your Project
FinTech Platform Development: AI-Powered Financial Product with 99%+ Fraud Detection
FinTech Platform Development: AI-Powered Financial Product with 99%+ Fraud Detection
FinTech Platform Development: AI-Powered Financial Product with 99%+ Fraud Detection
FinTech Platform Development: AI-Powered Financial Product with 99%+ Fraud Detection
FinTech Platform Development: AI-Powered Financial Product with 99%+ Fraud Detection
Trusted By Startups, SMBs to Fortune 500 Brands
g2-gray.png
get-app-gray.png
software-advice-gray.png
software-connect-gray.png
source-forge-gray.png
upcity-ratings-gray.png

Quick Answers

  • What we built: AI-Powered FinTech Platform
  • Who it's for: FinTech & Financial Services
  • Delivery: Full Stack Platform Development

  • 1. Architecture & Infrastructure: Designed scalable cloud-native architecture on AWS with Kubernetes orchestration, multi-region deployment, infrastructure-as-code, and security-first design with encryption and WAF protection.
  • 2. Core Product Development: Built responsive frontend, microservices backend with RESTful and GraphQL APIs, real-time data pipeline for transaction processing, and partner integration layer for open banking connectivity.
  • 3. Custom AI Model Development: Trained proprietary fraud detection using ensemble models combining anomaly detection, behavioral analysis, and graph-based relationship mapping on the client's transaction data.
  • 4. Compliance & Security: Achieved SOC 2 Type II and PCI DSS certification during the build process with comprehensive audit logging, explainable AI documentation, and automated regulatory reporting.
  • 5. Launch & Scale: Staged rollout from beta to production, real-time monitoring, continuous model optimization, and capacity scaling as transaction volumes grew from thousands to millions.

Overview

A Series B fintech startup with a clear product vision for an AI-powered lending and payment platform had the domain expertise but lacked the engineering depth to build it. Their team of 8 developers was talented but insufficient for the scope. Off-the-shelf fraud detection gave every competitor the same 92% accuracy baseline — no differentiation. Building custom AI required ML talent they could not hire fast enough, while enterprise clients demanded SOC 2 and PCI DSS compliance from day one.

Dreamztech delivered end-to-end fintech software development: cloud-native platform architecture, custom AI models for fraud detection and credit scoring, real-time transaction processing, and concurrent compliance certification. The custom fraud detection model achieved 99.7% accuracy — significantly above the industry baseline — while the platform launched in 14 weeks and passed SOC 2 Type II and PCI DSS audits during the build process.

Challenges

  • Product vision requiring AI-powered lending, intelligent fraud detection, and real-time payment processing that exceeded the 8-person internal team's capacity
  • Off-the-shelf fraud detection solutions providing only 92% accuracy baseline, offering zero competitive differentiation against other fintech platforms
  • Enterprise clients and banking partners requiring SOC 2 Type II and PCI DSS compliance before signing contracts or processing transactions
  • Speed-to-market pressure with competitors raising Series C funding and shipping features faster, requiring concept-to-production in months not years
  • Need for explainable AI with full audit trails, bias monitoring, and regulatory reporting to satisfy financial regulators and compliance requirements

  • 1. Architecture & Infrastructure: Designed scalable cloud-native architecture on AWS with Kubernetes orchestration, multi-region deployment, infrastructure-as-code, and security-first design with encryption and WAF protection.
  • 2. Core Product Development: Built responsive frontend, microservices backend with RESTful and GraphQL APIs, real-time data pipeline for transaction processing, and partner integration layer for open banking connectivity.
  • 3. Custom AI Model Development: Trained proprietary fraud detection using ensemble models combining anomaly detection, behavioral analysis, and graph-based relationship mapping on the client's transaction data.
  • 4. Compliance & Security: Achieved SOC 2 Type II and PCI DSS certification during the build process with comprehensive audit logging, explainable AI documentation, and automated regulatory reporting.
  • 5. Launch & Scale: Staged rollout from beta to production, real-time monitoring, continuous model optimization, and capacity scaling as transaction volumes grew from thousands to millions.

How the platform works

  • 1. Architecture & Infrastructure: Designed scalable cloud-native architecture on AWS with Kubernetes orchestration, multi-region deployment, infrastructure-as-code, and security-first design with encryption and WAF protection.
  • 2. Core Product Development: Built responsive frontend, microservices backend with RESTful and GraphQL APIs, real-time data pipeline for transaction processing, and partner integration layer for open banking connectivity.
  • 3. Custom AI Model Development: Trained proprietary fraud detection using ensemble models combining anomaly detection, behavioral analysis, and graph-based relationship mapping on the client's transaction data.
  • 4. Compliance & Security: Achieved SOC 2 Type II and PCI DSS certification during the build process with comprehensive audit logging, explainable AI documentation, and automated regulatory reporting.
  • 5. Launch & Scale: Staged rollout from beta to production, real-time monitoring, continuous model optimization, and capacity scaling as transaction volumes grew from thousands to millions.

Solutions delivered

DreamzTech designed and implemented a comprehensive inspection management platform with multi-portal architecture, mobile capabilities, and integrated business operations:

  • Ensemble ML models combining anomaly detection, behavioral analysis, and graph-based relationship mapping
  • 99.7% fraud detection accuracy — 7+ percentage points above off-the-shelf industry baseline of 92%
  • Real-time scoring processing transactions in under 50 milliseconds with sub-second response times
  • Continuous learning pipeline that improves accuracy as more transaction data flows through the system
  • Custom credit scoring models trained on proprietary lending data for nuanced borrower assessment
  • Churn prediction and lifetime value estimation models for portfolio management
  • Explainable AI with feature importance analysis satisfying regulatory requirements
  • Dynamic risk pricing based on real-time model outputs for lending decisions
  • High-throughput payment processing handling thousands of concurrent transactions
  • Event-driven microservices architecture for horizontal scalability
  • Real-time data pipeline with Kafka streaming for transaction monitoring and analytics
  • Multi-currency support with automated FX and settlement processing
  • Real-time AML screening with automated suspicious activity report generation
  • Automated KYC verification reducing onboarding time from days to minutes
  • Comprehensive audit logging with tamper-proof transaction records
  • Regulatory reporting automation for quarterly and annual compliance submissions
  • Cloud-native architecture on AWS with Kubernetes auto-scaling and multi-AZ deployment
  • Infrastructure-as-code with Terraform for reproducible, auditable environments
  • Zero-downtime deployment pipeline with blue-green and canary strategies
  • 99.99% uptime SLA with automated failover and disaster recovery
  • Open Banking API gateway supporting PSD2 and partner integrations
  • Webhook-based event system for real-time partner notifications
  • White-label capabilities enabling B2B2C distribution model
  • Comprehensive API documentation with sandbox environment for partner onboarding

Success Metrics

99.7% Fraud Detection

Custom AI models achieved 99.7% accuracy — 7+ points above 92% off-the-shelf baseline

$48M Processed

Platform processed over $48 million in transactions within the first 12 months

14-Week Launch

From concept to production-ready platform in 14 weeks with full compliance

SOC 2 + PCI DSS

Both certifications achieved during the build process, not post-launch

2.1% False Positive

False positive rate of 2.1% vs industry average of 5-10%, reducing friction

99.99% Uptime

Enterprise-grade platform reliability with automated failover across regions

Conclusion

Custom AI is not just a technology choice — it is a competitive strategy. By building proprietary fraud detection trained on their own transaction data, this fintech company created a moat that competitors using off-the-shelf tools simply cannot replicate. The 99.7% accuracy compounding with every new transaction, SOC 2 and PCI DSS certification enabling enterprise partnerships, and a 14-week timeline that outpaced Big Four consultancy timelines by months — this is what fintech software development looks like when implementation beats innovation.
Schedule a Call

Leading Global Software Company

Trusted by Industry Leaders Worldwide

Trusted by startups to Fortune 500s, including DHL, Nestlé, and Stanford — partners who rely on us for high-impact, scalable software solutions.

Book a Discovery Call

    I Consent to Receive SMS Notifications, Alerts from DreamzTech US INC. Message frequency may vary. Message & data rates may apply. Text HELP for assistance. You may reply STOP to unsubscribe at any time.
    I Consent to Receive the Occasional Marketing Messages from DreamzTech US INC. You can Reply STOP to unsubscribe at any time.
    By submitting the form, you agree to the DreamzTech Terms and Policies

    Talk to our fintech & financial services experts

    Get a free consultation to discuss your project requirements and explore how we can help build a similar solution.
    Schedule a Call

    Frequently Asked Questions (FAQ)

    Typically 12-20 weeks for a production-ready platform. A focused MVP with core AI features can launch in 10-14 weeks. Enterprise-grade multi-AI platforms with full compliance may take 16-20 weeks. Regulatory requirements like SOC 2 and PCI DSS add 2-4 weeks when done concurrently. Dreamztech’s 450+ engineer bench enables aggressive timelines without quality compromise.
    Off-the-shelf solutions deploy fast but give every competitor the same capabilities — typically 90-92% fraud detection accuracy. Custom AI is trained on your proprietary data, delivering higher accuracy (99.7% in this case), lower false positives, and a competitive advantage that compounds over time. The trade-off is development time, but the long-term moat is worth it.
    Compliance is integrated from day one, not bolted on after development. Security architecture review during design, SOC 2 controls embedded in the CI/CD pipeline, PCI DSS requirements in data handling workflows, and continuous monitoring throughout. Dreamztech holds SOC 2 and ISO 9001 certifications, so there is zero delay between product-ready and compliance-ready.
    Yes. Dreamztech’s platform architecture includes an integration layer supporting RESTful APIs, webhooks, and Open Banking standards. The AI fraud detection can be deployed as a standalone scoring service integrated with existing payment processors or as part of a complete platform build.
    Custom models are trained on your specific transaction patterns, customer behaviors, and fraud signatures. Off-the-shelf models use generic training data. The difference is like using a general translator versus a native speaker — the custom model understands the nuances of your business and improves with every transaction.
    Post-launch support includes continuous model monitoring and retraining, performance optimization, security updates, compliance maintenance, and quarterly roadmap reviews. The AI models are continuously retrained on new transaction data to maintain and improve accuracy over time.

    Navigate

    Discuss your Project ➜

    Awards & Recognitions
    forbes-logo-white
    theeconomictime
    Deloitte-India-top-white
    capterra-best-value-black-white
    clutch.co_generative_ai_company_arizona_2025-black-white
    bronze_winner
    CIO-top-white
    GetApp-best-functionality&features 1
    softwareadvice-best-customer-support 1
    capterra-best-ease-use 1
    business-innovation
    india-IT-2025