Financial Services
ML Infrastructure for
Financial Services
Real-time feature serving, enterprise security, and regulatory compliance. The ML platform trusted by banks and financial institutions.
Use Cases
From fraud detection to personalization — ML use cases across the financial services value chain
Fraud Detection & AML
Real-time transaction scoring with deep learning. Detect anomalies, smurfing, and gather-scatter patterns that rule-based systems miss.
99% reduction in false positives at Swedbank
Credit Risk Scoring
Real-time credit decisions with ML models. Combine traditional credit data with alternative signals for better risk assessment.
Sub-millisecond decisioning
Customer 360 & Personalization
Unified customer features across all touchpoints. Power recommendations, next-best-action, and personalized offers.
Single source of truth for ML
Algorithmic Trading
Low-latency feature serving for trading strategies. Combine market data with alternative data sources in real-time.
<1ms feature retrieval
Churn Prediction
Identify at-risk customers before they leave. Trigger retention campaigns with real-time propensity scores.
Proactive intervention
Loan Default Prediction
Continuous monitoring of loan portfolios. Update risk scores as new data arrives, not just at origination.
Dynamic risk assessment
Why ML in Finance Is Hard
Unique challenges that generic ML platforms weren't built to handle
Real-Time Requirements
Financial decisions happen in milliseconds. Batch ML doesn't cut it when you need to score every transaction before it clears.
Regulatory Compliance
GDPR, PCI DSS, SOX, Basel III. Every model needs audit trails, explainability, and full data lineage.
Training-Serving Skew
Models trained on historical data fail in production when features are computed differently. Consistency is non-negotiable.
Data Silos
Customer data lives in dozens of systems. Building ML features requires joining data that was never meant to be joined.
Built for Financial Services
Enterprise-grade ML infrastructure that meets the demands of financial institutions
Real-Time Feature Serving
Sub-millisecond retrieval powered by RonDB. Handle millions of lookups per second.
Unified Feature Store
Same features for training and serving. No skew, ever.
Enterprise Security
GDPR, PCI DSS, HIPAA compliant. Full encryption and RBAC.
Feature Monitoring
Detect drift and anomalies before they impact models.
Data Lineage
Track every feature from source to prediction. Full auditability.
ML Pipelines
Orchestrate feature engineering, training, and deployment.
<1ms
Feature retrieval
40TB+
Feature data at scale
99%
FP reduction (fraud)
SOC 2
Type II certified
Customer Stories
How financial institutions deploy ML with Hopsworks
FRAUD DETECTION
Swedbank
Scandinavia's largest bank reduced false positives in fraud detection from 99:1 to 1:2 using deep learning on Hopsworks.
40TB+ of features in production
GANs for anomaly detection
Graph embeddings for pattern detection
99:1
Before (rule-based)
1:2
After (ML)
America First Credit Union
MLOps transformation
"Our journey with Hopsworks has been an amazing transformation."
Regulatory Compliance
Financial services ML requires more than just good models. Regulators demand explainability, audit trails, and data governance. Hopsworks is built for this.
Security
Encryption at rest and in transit, RBAC, SSO
Certifications
SOC 2 Type II, GDPR, PCI DSS, HIPAA ready
Governance
Full lineage, versioning, audit logs
The EU AML Package and AMLA 2020 explicitly encourage machine learning approaches for financial crime detection — but require demonstrated effectiveness through data.
Hopsworks provides the infrastructure to not just build ML models, but to prove they work: complete data lineage, feature versioning, model registries, and comprehensive audit trails.
Ready to Modernize Your ML Stack?
Join banks and financial institutions already running production ML on Hopsworks.