Financial fraud is an unchecked epidemic, draining billions from the global economy while leaving businesses, taxpayers, and consumers to foot the bill.
In 2023 alone, global payment card fraud losses surged to $33.83 billion (Nilson Report), while the U.S. insurance industry lost a staggering $308.6 billion to fraudulent claims (Coalition Against Insurance Fraud). Medicare fraud alone is estimated to cost $68.7 billion annually, diverting resources from those in genuine need and eroding public trust.
As threats escalate, AI fraud detection has become a critical capability for financial institutions seeking to stay ahead. Traditional detection systems are no longer fast enough or intelligent enough to detect evolving attack vectors at scale. That’s why forward-looking institutions are embracing AI infrastructure and data intelligence platforms to transform their fraud prevention strategies.
By leveraging AI data platforms that unify structured and unstructured data across environments, fraud teams can shift from reactive analysis to proactive protection. These platforms enable real-time risk scoring, behavioral analysis, and AI-driven anomaly detection at petabyte scale–all in milliseconds.
This whitepaper outlines five essential steps to modernize fraud detection using AI-optimized data storage, unified pipelines, and scalable hybrid cloud architecture. With insights from real deployments and technology leaders, we’ll explore how AI storage solutions like DDN Infinia are redefining speed, accuracy, and ROI in financial fraud prevention.
Rethink Fraud as A Data Intelligence Problem
Fraud detection is fundamentally a data coherency problem, much like quantum entanglement – where no single transaction exists in isolation, but rather as part of an interconnected network of financial interactions. Traditional fraud prevention systems operate like classical physics, treating each transaction as an independent event, relying on predefined rules, and reacting only after anomalies surface. But fraud, much like quantum mechanics, exploits uncertainty, hidden correlations, and systemic blind spots. Stopping it isnʼt about flagging isolated red flags – itʼs about recognizing the entanglements between data points: location history, device fingerprints, merchant risk, spending behaviors, and transaction velocity.
This is where AI-driven, high-speed data platforms like DDN Infinia become essential. Detecting fraud in real-time requires analyzing petabytes of streaming data at sub-millisecond latencies, identifying nonlinear patterns, and making instant, AI-driven decisions before fraudsters can exploit the lag. The old approach – scanning static datasets, applying rules, and chasing fraud after the fact – is already obsolete. Without a coherent, AI-powered, high-speed data infrastructure, institutions are playing catch-up in a game where milliseconds determine billions in losses.
Break Free from Legacy Fraud Workflows
Legacy Workflows Can’t Keep Up
Traditional fraud detection systems were built for a slower, simpler time – when human-initiated fraud was the norm and batch processing was “fast enough.” But today’s attackers operate at machine speed, and these outdated workflows are no longer equipped to respond in real time.
Legacy architectures rely on:
- Rule-based models that can’t adapt dynamically
- Batch processing pipelines that delay response times
- Disjointed systems that slow down feedback loops
This rigidity leaves financial institutions vulnerable to fast-evolving fraud tactics and complex attack patterns.
A Look at the Legacy Pipeline
Here’s how yesterday’s fraud systems typically worked:
- Ingest: Real-time transactions were pulled from payment terminals and apps
- Enrich: Apache Spark added context like geolocation, device ID, and merchant risk
- Score: Processed events were stored in MapR-DB and assessed for risk
- Detect: XGBoost models flagged fraud based on predefined thresholds
- Respond: Risk events were sent to MapR-ES and routed to analysts or automated blocks
- Retrain (Eventually): Historical fraud data was used to retrain models – but only after the fact, and far too slowly
Each step relied on structured pipelines that introduced latency at every turn, especially as fraud patterns grew more relational and harder to classify.
Why This No Longer Works
- XGBoost struggles to detect coordinated, graph-based fraud rings
- Retraining cycles are delayed due to slow access to stored data
- Latency gaps between detection and response leave institutions exposed
- Legacy storage creates a bottleneck for modern AI models
Simply put, these systems are stuck in the past – reacting to fraud after it happens, instead of preventing it in real time.
What’s Needed Now
The next generation of fraud prevention must:
- Ditch batch-based bottlenecks
- Enable sub-millisecond decisioning
- Harness AI-powered anomaly detection
- Support real-time retraining and inference
- Scale across hybrid infrastructure environments
Breaking free from legacy systems isn’t a technology upgrade – it’s a competitive necessity.
Build a Real-Time AI Fraud Detection Architecture
Traditional fraud detection systems struggle with the scale and complexity of modern financial crime. Fraudsters operate in networks, not isolated transactions, which is why that Graph Neural Networks (GNNs) and real-time inference are now essential.
However, powering AI at this scale presents massive technical challenges:
- Handling massive graph datasets,
- Supporting ultra-low-latency inference
- Enabling continuous retraining at speed
To overcome these obstacles, institutions need a high-performance AI Infrastructure capable of petabyte-scale data movement, GPU acceleration, and sub-millisecond decisioning. This requires more than compute – it depends on a scalable AI-Data Platform that tightly integrates storage, analytics, and model execution.
This is where DDN Infinia, our next-generation Data Intelligence Platform, becomes mission-critical. Infinia delivers:
- High-speed AI Storage for real time model execution
- Native support for graph embeddings and inferencing pipelines
- Seamless GPU access without CPU bottlenecks
With Infinia, fraud teams can deploy real-time AI pipelines that detect and stop fraud as fast as it happens.
Integrate Cloud and On-Prem with Hybrid AI Infrastructure
Legacy platforms like Vertica, Hadoop, and MapR are no longer equipped to handle the demands of modern, AI-driven fraud detection. Fraud moves too fast, and batch-driven systems simply can’t keep up.
Today’s fraud prevention architectures must be:
- Hybrid by design – integrating cloud and on-prem seamlessly
- Real-time at the core – enabling query execution in seconds
- AI-ready – capable of handling structured and unstructured data, graph analytics, and streaming workloads
Modern cloud data platforms like BigQuery and Redshift provide massive scale and performance, but they’re only one part of the solution. Many institutions still require:
- On-prem systems for edge processing and compliance
- Historical model retraining and storage
- Real-time access to raw and enriched fraud signals
DDN Infinia acts as the connective tissue across this hybrid environment:
- Enables data mobility between cloud and on-prem
- Supports multi-protocol access (S3, GCS, POSIX)
- Eliminates performance bottlenecks in cross-environment workflows
By unifying hybrid data movement and fraud detection operations, Infinia ensures your infrastructure moves at the speed of fraud–regardless of where the data lives.
Unify Your AI Stack for End-to-End Performance and ROI
To compete in real-time fraud prevention, institutions must modernize their entire stack–not just add AI on top of legacy systems. This means:
- Ingesting real-time graph and transaction data
- Training models on massive, high-dimensional datasets
- Serving predictions in milliseconds
- Retraining continuously as threats evolve
Here’s how a unified AI stack should perform:
- Real-Time Graph Data Ingestion & Feature Engineering
Stream data from payments, logs, and accounts
Enrich with location, device, behavioral, and risk signals - AI-Driven Risk Scoring with GNNs
Use GNNs to analyze relational fraud patterns
Accelerate access to embeddings via AI-optimized storage - Real-Time Inference & Decisioning
Flag fraud before approval using GPU-accelerated pipelines
Replace batch scoring with live fraud interception - Continuous Model Retraining
Track graph drift and update models in near real-time
Leverage historical embeddings without degrading performance
DDN Infinia enables this full-stack modernization by combining:
- AI Data Storage with NVMe-speed and GPU-direct movement
- AI Infrastructure built for streaming, training, and inference
- A flexible, hybrid-ready AI Data Intelligence Platform
The result? Institutions stay ahead of evolving fraud, while improving ROI, performance, and operational agility.
Conclusion
AI-enabled fraudsters are evolving daily – faster, more coordinated, and more sophisticated. Every millisecond lost to data bottlenecks, slow inference, or outdated models increases an institutions risk exposure.
To stay ahead, financial organizations must modernize with an AI Data Intelligence Platform that delivers real-time performance, hybrid flexibility, and built-in scalability. That means moving beyond reactive fraud workflows and embracing a unified AI Infrastructure designed for speed, scale and adaptation.
DDN Infinia combines high-performance AI Data Storage with real-time analytics and model execution capabilities – creating an AI Data Platform purpose-built for modern fraud detection. It enables institutions to act on streaming data in milliseconds, retrain models continuously, and scale fraud prevention across cloud, on-prem, and hybrid environments.
With Infinia, fraud detection isn’t just faster ø it’s smarter, leaner, and future-ready.
Learn more about how DDN is helping financial institutions evolve fraud detection with next-generation AI Storage and Data Intelligence Platforms. Contact us today for a tailored strategy.
