Your fraud signals live in different systems. Authentication logs in one place. Application access patterns in another. Device fingerprints somewhere else. Geolocation data in yet another tool. Your security team knows what to look for, but manually correlating these signals across millions of daily sessions isn't feasible.
This is the operational reality for financial services companies running consumer-facing platforms. Here's how workflow automation solves it.
Consider a financial services company where customers access credit reports, monitor scores, and manage identity protection services. Sensitive data. High-value targets. Constant attack surface.
The fraud signals that need correlation span four categories:
Identity correlation signals
Multiple accounts sharing the same contact information. Frequent profile changes immediately after authentication. Accounts accessing from identical devices despite being "unrelated" users. Multiple identities originating from the same network locations.
Location and device signals
Impossible travel patterns (Boston login followed by Mumbai login 10 minutes later). Access from locations inconsistent with registered addresses. Unverified or anomalous endpoints. Device fingerprint mismatches.
Authentication behavior signals
Unusual authentication patterns or timing. Multiple failed verification attempts. Knowledge-based authentication responses that don't match historical patterns. Authorization behaviors inconsistent with account history.
Account activity signals
Unusual access timing or frequency. Navigation patterns that differ from typical user behavior. Suspicious profile modifications. Rapid progression to high-risk actions.
Each signal alone might be explainable. A user traveling for work. A new device. A forgotten password. But when you correlate five signals within a short timeframe, you're looking at fraud.
The problem: these signals existed in separate log sources across different security and application systems. Correlating them required either building complex SIEM queries that were brittle and slow, or having analysts manually investigate each alert by pivoting between tools.
Neither approach scaled.
Most organizations try to solve fraud detection by building rules in their SIEM or purchasing a dedicated fraud detection product. Both have fundamental limitations.
SIEM-based detection requires writing complex correlation rules across multiple log sources. These rules break when log formats change. They're slow to execute at scale. And they produce binary outputs—alert or no alert. No context. No investigative workflow. Just a pile of alerts that still require manual triage.
Dedicated fraud platforms work well for transaction fraud (payment processing, wire transfers) but struggle with identity and account fraud across custom applications. They don't have visibility into your authentication systems, your application logs, or your internal tools. They're solving a different problem.
What's needed is a system that can ingest logs from every relevant source, correlate signals across them, execute investigative workflows that humans would normally perform, and route cases based on risk and complexity.
The combination of workflow automation with AI agents that can see relevant logs wherever they live improves fraud detection. Here's the architecture, and how Strike48 implements each layer.
The first requirement: ingest logs from every system relevant to fraud detection. Customer portal access logs. Mobile application logs. Authentication and MFA systems. Device intelligence platforms. IP reputation services. Geolocation verification systems. Session tracking systems. Customer support interaction logs.
Centralizing everything into a single repository isn't realistic at the scale financial services companies operate. Strike48's approach queries logs where they already live—in your SIEM, in observability platforms, in compliance tools, in cloud provider logs—providing full visibility without requiring data migration.
When a potential fraud indicator triggers (unusual authentication pattern, impossible travel, device mismatch), the automation executes a multi-phase analysis workflow:
Phase 1: Alert Intake and Initial Enrichment. Collect the triggering event and related context. Retrieve account profile and historical activity patterns. Pull device and geolocation data for the current session.
Phase 2: Parallel Correlation Analysis. Check for identity correlation patterns across other accounts. Query IP reputation and geolocation verification services. Analyze authentication behavior against account baseline. Evaluate session activity for suspicious navigation patterns.
Phase 3: Risk Scoring and Evidence Collection. Weight indicators based on fraud impact (impossible travel scores higher than unusual timing). Correlate multiple lower-risk signals into a composite risk score. Package findings with specific evidence and timestamps. Generate a human-readable investigation summary.
Phase 4: Routing and Response. Route high-risk cases to fraud analysts with complete investigation context. Route medium-risk cases to automated response workflows (MFA challenge, temporary restrictions). Route low-risk cases to monitoring workflows for pattern tracking. Escalate critical indicators (known fraud patterns, regulatory triggers) immediately.
The workflow is deterministic. The same inputs produce the same analysis steps every time. But the cognitive steps—evaluating whether this behavior is anomalous for this specific user, determining appropriate risk weighting given account context—use Strike48's bounded micro-agents that reason within constrained scope.
Not all fraud signals carry equal weight. The workflow applies dynamic scoring:
High-impact patterns (automatic escalation): Impossible travel scenarios. Access from known fraud infrastructure. Multiple identity correlation signals within a short timeframe. Critical profile changes followed by high-risk actions.
Medium-impact patterns (weighted scoring): Device fingerprint mismatches. Unusual authentication timing. Navigation patterns inconsistent with user history. Moderate geolocation anomalies.
Low-impact patterns (contributing factors): Single failed authentication attempt. New device from a known location. Minor profile updates. Timing variations within normal range.
The system correlates these signals dynamically. Three medium-impact patterns within 15 minutes might trigger the same response as one high-impact pattern. The thresholds adapt based on account type, user history, and current threat patterns.
Different fraud scenarios require different responses. The workflow routes cases through specialized paths:
Each path is a distinct workflow with its own investigation steps, evidence requirements, and approval gates.
An account accesses a customer portal from a new device in a different state than their registered address. Authentication succeeds, but the device fingerprint doesn't match historical patterns.
The agentic workflow executed the same investigative steps a human analyst would perform, but ran them in parallel and delivered results with evidence trails and risk scoring already complete.
Under one minute for routine fraud investigations. Analysts shift from repetitive correlation work to complex investigations that require human judgment.
Higher detection accuracy. Correlating signals across all log sources simultaneously catches fraud patterns that analysts miss when they can only manually check a subset of signals.
Lower false positive rates. Dynamic risk scoring based on account context and historical patterns reduces unnecessary friction for legitimate users while maintaining detection efficacy.
Hours to update, not weeks. When new fraud patterns emerge, updating workflows doesn't require retraining models or rebuilding SIEM correlation rules.
Automatic audit trails. Every investigation step is logged with timestamps, evidence sources, and decision rationale. Compliance documentation is a byproduct of operations, not a reconstruction exercise.
While this use case focuses on consumer account fraud in financial services, the same workflow automation approach applies across fraud detection scenarios: account takeover detection in banking, fraudulent claims identification in insurance, unauthorized access monitoring for investment firms, and transaction pattern analysis for payment processors.
The common thread: fraud detection requires correlating signals across multiple log sources, executing investigative workflows, and routing cases based on risk and complexity. Deterministic workflows with bounded cognitive steps handle this better than black-box AI reasoning—they're auditable, consistent, and adaptable.
If your fraud detection currently depends on analysts manually correlating signals across tools, or on SIEM rules that break every time a log format changes, Strike48 offers a different architecture: full log visibility without centralization, workflow orchestration for multi-phase investigations, and bounded micro-agents for cognitive steps like risk scoring and pattern evaluation.
Request a demo to see how Strike48 executes fraud investigation workflows across your log sources, with the same consistency your analysts would provide, at AI speed.