Big Data Fraud Detection Software
Implement real-time fraud detection across massive datasets, combining advanced analytics, machine learning, and pattern discovery.
Skylight’s scalable platform ingests and analyzes transaction records, user activity, device signals, and third-party feeds at the speed and scale that modern fraud environments demand, surfacing threats that remain invisible to systems built for smaller data footprints.
Why it matters
The fraud that causes the most damage is the fraud that hides in plain sight
At enterprise scale, the sheer volume of data generated by transactions, logs, and customer events creates a signal-to-noise problem that traditional fraud tools were never designed to solve.
Traditional tools are insufficient at modern data volumes
Organizations now process vast volumes of transactions, logs, and customer events continuously across multiple channels and geographies. Tools designed for lower-volume environments introduce latency, miss cross-source patterns, and require manual triage at a scale that makes comprehensive coverage structurally impossible.
Large-scale fraud schemes are designed to evade narrow analysis
Large-scale fraud schemes deliberately distribute activity across accounts, channels, and timeframes to stay below individual detection thresholds. Without comprehensive data analysis that connects events across sources, these coordinated patterns remain invisible until the accumulated loss makes them impossible to ignore.
Real-time detection is essential at big data scale
Real-time fraud detection in big data environments is essential to stop threats before material loss occurs. Batch processing and delayed analysis leave windows that sophisticated fraudsters actively exploit. The intervention that matters is the one that happens before funds move, not the report reviewed hours or days later.
How it works
Ingestion to investigation, processing at enterprise scale
Four integrated capabilities handle the full detection lifecycle, from pulling in data across every source through to scoring, surfacing, and investigating the threats that emerge from it.
High-Volume Data Ingestion
The platform ingests transaction records, user activity logs, device signals, and third-party feeds to support fraud detection using big data analytics. Data from disparate sources is normalized and made available for analysis as it arrives, eliminating the delay between an event occurring and it becoming visible to your detection models.
Dynamic Risk Scoring
Every event is scored in real time using models trained on historical behavior, environmental context, and peer group comparisons to enable big data fraud detection at scale. Scoring reflects the full data environment around an event, not just its surface characteristics, which is what makes detection meaningful at the volumes modern organizations operate.
Behavioral and Network Analytics
Machine learning and graph analysis identify unusual patterns, link suspicious actors, and detect anomalies within large datasets. Network analytics are particularly effective at exposing coordinated fraud rings, where the relationship between accounts and behaviors reveals the scheme that no individual transaction would betray on its own.
Central Investigator Dashboard
Investigators can slice data by customer, channel, device, or event type, view risk scores and historical trends, and manage cases from a unified interface. The ability to explore the data around a flagged event, rather than just viewing the alert in isolation, is what enables investigators to understand and resolve complex cases efficiently.
What sets us apart
Built for live data pipelines, managed by analysts, explainable by design
Skylight integrates with streaming platforms and databases via flexible APIs, gives analysts direct control over policy rules and risk thresholds, and makes every model decision explainable to the people acting on it.
Fraud detection at big data scale only delivers value if it operates in real time on live data, not on a delayed extract. Skylight is designed to connect directly to your data pipelines, so detection happens as events occur rather than after a processing window has passed.
Flexible API integration with streaming platforms and databases
The system integrates with streaming platforms and databases via flexible APIs, enabling seamless connection to live data pipelines without requiring a separate data extraction layer. Skylight operates on your data as it flows, not on a copy that is already out of date by the time it is analysed.
Analyst-managed policy rules and risk thresholds
Policy rules and risk thresholds can be managed by analysts without code changes, keeping detection logic responsive to the fraud environment your team is observing in real time. New patterns can be addressed with a rule update rather than a development sprint, which matters when fraud tactics are evolving faster than engineering cycles can accommodate.
Cloud-native architecture that scales with data volume
The cloud-native architecture scales automatically to meet processing needs as data volume grows, without manual capacity planning or performance trade-offs during periods of peak load. Your detection capability grows with your data environment, not behind it.
Explainable risk model decisions
Risk model decisions and signal drivers are explainable to support investigator understanding and compliance. In a big data environment where model complexity is highest, the ability to surface clear reasoning for each decision is what separates a detection system investigators trust and act on from one they treat with skepticism.
Use cases
Detection and investigation at the scale your data demands
From real-time transaction surveillance across global operations to retrospective analysis that informs future controls, Skylight handles the full spectrum of fraud detection tasks that big data environments require.
Real-time transaction surveillance
Monitor activity across cards, payments, account behavior, and third-party feeds simultaneously with scoring decisions made as events arrive rather than after a batch window. Multi-source surveillance at scale surfaces the coordinated patterns that single-channel monitoring cannot see, because the signal in big-data fraud detection is in the connections between events, not the events themselves.
Network analysis
Identify coordinated fraud rings by mapping the relationships between accounts, devices, behaviors, and events at scale. Network analysis surfaces the organizational structure of sophisticated fraud operations that individual transaction monitoring would never detect, because the signal is in the connections, not the individual events.
Anomaly detection
Uncover unusual activity spikes or pattern shifts across large datasets that indicate emerging fraud campaigns or changes in attacker behavior. Anomaly detection at big data scale catches the early signals of a new scheme before it has caused significant loss, giving your team time to respond and update controls proactively.
Investigative and retrospective review
Explore historical data to understand fraud patterns, validate detection models, and refine controls based on what has actually occurred across your environment. Retrospective analysis turns past incidents into structural improvements, so each fraud campaign your team investigates makes your future detection more robust.
Harness the scale and speed of big data to fight fraud
Request a live demo, launch a pilot, or speak with one of our fraud data experts about what comprehensive big data detection could look like in your environment.
