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BankSphere — Preventing $28M in Fraud Losses With Real-Time ML

BankSphere — Preventing $28M in Fraud Losses With Real-Time ML

Case Study2025-11-26

How Logicwerk deployed a real-time fraud detection platform using streaming ML, behavioral analytics, and automated risk scoring — reducing false positives by 45% and preventing $28M in annual fraud losses.

Case Study: BankSphere — Preventing $28M in Fraud Losses With Real-Time ML

Overview

BankSphere, a top-10 global digital bank, faced increasing fraud attempts across card transactions, ACH transfers, account takeovers, and mobile banking activity.
False positives were frustrating customers. Fraud leaks were growing.

Logicwerk deployed a real-time fraud detection and behavioral analytics platform powered by streaming ML pipelines — enabling the bank to identify and prevent fraud within milliseconds.


Challenges

  • Rising fraud losses driven by account takeover (ATO) attempts
  • Legacy rule-based fraud systems generating high false positives
  • Customer churn due to blocked legitimate transactions
  • Growing fraud vectors across mobile, card, and online banking
  • No real-time capability — 15–60 minute detection delays

Traditional rule engines were not sufficient.


Solution: Real-Time Fraud Detection Using ML & Streaming Analytics

1. Streaming Data Pipeline

Ingested real-time signals from:

  • Mobile banking
  • Card networks (Visa, Mastercard)
  • Device fingerprinting
  • Geo-location + VPN detection
  • Historical transaction graph
  • Behavioral biometrics

Built on Kafka + Flink + vector encoding.


2. ML-Based Risk Scoring

Models used:

  • Behavior anomaly detection
  • Historical similarity vectors
  • Transactional graph embeddings
  • Real-time user signature analysis

Models were optimized for:

  • Latency < 20ms
  • AUC-ROC > 0.96
  • Minimal false positives

3. Rule + ML Hybrid Engine

Combined:

  • Predictive risk score
  • Business rules
  • Regulatory constraints (PSD2, AML, KYC)

This ensured safe + auditable decisions.


4. Agent-Based Investigation Workflow

We deployed a Fraud Investigator Agent to:

  • Summarize flagged incidents
  • Propose recommended actions
  • Auto-generate SAR/STR compliance reports
  • Assist human analysts with explanations

All fully auditable and SOC2-compliant.


Results

🚨 $28M in fraud losses prevented (annualized)

Through real-time blocking of high-risk transactions.

🎯 45% reduction in false positives

Higher model precision = fewer frustrated users.

Detection latency reduced from 900ms → 18ms

Critical for seamless checkout experiences.

🔐 Stronger compliance with PSD2 & AML mandates

Automated audit trails and explainability.


Final Outcome

BankSphere transformed their fraud operations with real-time AI, resulting in safer transactions, happier customers, and dramatically reduced financial losses.


Work With Logicwerk

We help financial institutions deploy:

  • Fraud ML pipelines
  • Real-time scoring systems
  • Agentic investigation workflows
  • SOC2 & ISO-42001 AI governance

👉 https://logicwerk.com/contact