Condense-Powered Real-Time Fraud Detection and Transaction Blocking

Business Context 

In the digital financial ecosystem, real-time transaction velocity has become a double-edged sword. While it enables fast, seamless banking experiences, it also opens doors for increasingly sophisticated fraud—ranging from synthetic identity attacks to transaction laundering and account takeovers. 

Static fraud scoring systems, batch validation engines, or rule-based legacy solutions cannot respond at the speed modern fraud schemes evolve. Financial institutions now require real-time event orchestration, context-aware decisioning, and automated fraud intervention to safeguard assets and consumer trust. 

This document outlines how Condense, a vertically optimized real-time streaming platform, can be used to design and deploy an end-to-end fraud detection and blocking system. It integrates multiple data signals—transactional, behavioral, and contextual—to detect fraud and trigger immediate action, all within milliseconds. 

Objectives 

• Ingest and monitor real-time financial activity: card swipes, logins, fund transfers, and payment requests 

• Cross-validate location, device, behavior, and risk indicators across multiple streams 

• Trigger immediate block decisions, alerts, and risk escalations 

• Deliver high observability and auditability for compliance and investigation 

Maintain performance under high throughput while ensuring low-latency decisions 

Problem Statement 

Current Challenges 

• Delayed Detection: Batch fraud systems detect fraud post-facto, often too late to intervene. 

• Isolated Risk Signals: Device fingerprinting, IP reputation, and transaction data are analyzed in silos. 

• Manual Review Overload: Alerting systems generate high false positives without contextual correlation. 

• Latency-Sensitive Events: Decisions on high-value transfers must happen in under 500 milliseconds. 

• Integration Complexity: Multiple upstream and downstream systems (core banking, CRM, compliance) must interoperate securely and in real-time. 

Technical Requirements 

• Real-time ingestion of transactional and behavioral event streams 

• Stateful and contextual joins across sessions, devices, and geographies 

• Flexible logic authoring for scoring, blacklisting, and decision trees 

• Seamless integration with fraud management dashboards, CRM, and enforcement APIs 

• Auditability, schema governance, and replay capabilities for compliance 

Data Ingestion and Schema Design 

Condense natively supports secure, high-throughput connectors for financial-grade data ingestion. All streams are schema-validated and version-controlled. 

A. Transaction Event Connector 

• Source: Card processors, core banking systems, mobile payment APIs 

• Protocol: Kafka, HTTP/Webhook 

• Fields: transaction_id, user_id, amount, timestamp, merchant_code, location, channel, device_id 

B. Login & Session Activity Stream 

• Source: Online/mobile banking frontends 

• Fields: login_id, user_id, timestamp, ip_address, device_fingerprint, geo_location, browser, login_method 

C. Fund Transfer Initiation Stream 

• Source: Payment gateway or internal transfer services 

• Fields: transfer_id, from_account, to_account, amount, requested_by, origin_app, session_id 

D. Device Risk Feed 

• Source: Internal device intelligence module or third-party risk provider 

• Fields: device_id, trust_score, geo_velocity, previous_fraud_flags, device_type, anomalies 

All streams are ingested into schema-bound topics, enabling schema evolution, backward compatibility, and enforcement at transform runtime. 

Real-Time Stream Processing in Condense 

Condense allows organizations to construct intelligent processing pipelines using prebuilt transforms (NCLC logic blocks) and custom-coded decision logic. 

A. Cross-Session Correlation 

• Merge Utility: Join login and transaction events based on user_id, device_id, and time window 

• Window Utility: Look back 15–30 minutes to establish session context 

• Result: Unified stream with transaction enriched by login metadata and device risk context 

B. Rule-Based Filtering and Pattern Detection 

• Conditional Utility: Flag transactions with mismatched IP geolocation and card swipe region 

• Threshold Evaluation: Raise alert if fund transfer value exceeds average historical band by 2x 

• Blacklist Lookup: Real-time join with dynamic watchlists for device IDs or accounts 

C. State Tracking and Frequency Analysis 

• State Store: Tracks velocity (number of logins or transactions per minute per user/device) 

• Alert Utility: Generates fraud signals when behavior exceeds normal operational baselines 

Advanced Detection Logic via Condense IDE 

For use cases requiring advanced logic—such as adaptive scoring, ML models, or conditional flows—Condense provides a built-in polyglot IDE. 

Developer Workflow 

• Author logic in Python, JavaScript, Java, or Go using Condense inbuilt IDE 

• Integrate directly with GitHub or GitLab for branch management and version control 

• Simulate on historical data, validate outputs, and deploy on live stream with single-click activation 

• Observe code performance in real-time alongside event flow and output rates 

Sample Use Cases 

• Risk score computation using weighted metrics (device trust, transfer velocity, merchant risk) 

• Geofencing logic to detect login or transactions outside configured safe zones 

• Cross-account activity correlation (e.g., same device initiating from multiple user accounts) 

• Decision tree to determine whether to block, hold, escalate, or approve transaction 

Fraud Response and Downstream Connectors 

Condense integrates seamlessly with both internal enforcement tools and external platforms via secure downstream connectors. 

A. Enforcement API 

• REST/Webhook call to core banking or payment system 

• Action: Block, hold, or allow transaction 

• Additional Actions: Insert into quarantine ledger or flag for manual review 

B. Notification & Escalation 

• Push alerts to internal fraud teams via Slack, Teams, or Email 

• Trigger outbound call to affected customer via integrated CRM system 

• Store flagged events in case management platform (e.g., Actimize, SAS, in-house) 

C. Audit and Reporting 

• Persist flagged transactions, fraud alerts, and blocking actions to secure object storage 

• Used for forensic analysis, compliance audits, and internal investigations 

Observability, Resilience, and Recovery 

Condense includes monitoring, alerting, and state visibility tools out of the box. 

Metrics Tracked 

Average time from event ingestion to fraud decision 

1. Computation 

• Each transaction or login event is automatically timestamped upon ingestion. 

• When the fraud decision is triggered (block/allow/alert), the decision timestamp is captured. 

• A built-in transform computes the latency by subtracting the two and emits this as a metric. 

B. Implementation in Condense: 

• Use the Compute Latency prebuilt transform or embed a timestamp delta function in a custom transform. 

• Output this value to a metrics stream (fraud.decision.latency). 

• Visualize via dashboard builder like PowerBi (e.g., average over 5-minute windows). 

Percentage of blocked vs. approved transactions 

1. Computation 

• Each decision transform writes a decision_type field to the output event (blocked, approved, held, escalated). 

• A Count by Type aggregate transform groups events by decision_type over a rolling time window. 

B. Implementation in Condense: 

• Use the NCLC “Group + Count” utility on fraud.decision stream. 

• Display as a ratio or percentage chart on the dashboard in PoweBi (e.g., pie chart or time-series bar). 

Velocity of event types (per user, per device, per region) 

1. Computation 

• State stores in Condense maintain counters per user_id, device_id, or geo_region. 

• A rolling window (e.g., last 60 seconds) is used to count login attempts or transactions. 

B. Implementation in Condense: 

• Use the Window + Count transform and key by relevant field (user_id, etc.). 

• Thresholds (e.g., >5 logins in 60 seconds) trigger alerts via the built-in Alert Builder utility. 

• These counts are published into a velocity.metrics stream for visualization. 

Number of false positives vs. confirmed frauds 

1. Computation 

Fraud decisions are enriched later by tagging them as either confirmed or false_positive, either via feedback from downstream systems (manual review tools, customer call centers) or automated reconciliation. 

B. Implementation in Condense

• Use a Join transform between fraud.decision and fraud.outcome.feedback streams (merged by transaction_id or alert_id). 

• Apply a filter to label each as confirmed_fraud or false_positive. 

• Count these over time and publish to fraud.feedback.metrics. 

Observability Tools 

• Live Stream View: See raw input, transformed output, and triggered actions in real time 

• Log Panel: Drill down into transaction-level decisions with timestamps and rule hits 

• Dashboard Builder: Create fraud operations dashboards filtered by event types, outcomes, or risk levels 

• Alert Builder: Trigger notifications if alert volume or false positive rate exceeds target 

Failure Handling 

• Retry: All outbound actions are retry-configurable 

• Dead Letter Queue (DLQ): Unprocessable or invalid records routed to DLQ for reinspection 

• Manual Replay: DLQ records can be reviewed and reprocessed via the Condense UI 

Platform Advantage: Why Condense Excels in Real-Time Financial Protection 

Domain-Centric Modules 

Condense provides financial-grade source connectors and risk-aligned transforms—reducing the burden of building and maintaining bespoke ingestion and enrichment layers. 

Unified No-Code + Full-Code Platform 

Risk analysts can use no-code utilities to define logic for velocity alerts, while developers write custom fraud scoring models in the same pipeline—promoting collaboration and agility. 

GitOps and Production Readiness 

Integrated Git support allows full version control, deployment traceability, and CI/CD integration, enabling enterprise-grade governance and reproducibility. 

Secure, Auditable, and Compliant 

All transformations are logged, all schemas versioned, and all user actions traceable—providing end-to-end audit trails required for regulated financial environments. 

Real-Time Response at Scale 

Condense delivers sub-second fraud detection across millions of events per day—without sacrificing flexibility, observability, or control. 

End-to-End Real-Time Pipeline 

The platform provides an integrated experience—from data ingestion and enrichment to decisioning and alerting—without needing external orchestration tools, schedulers, or microservices. 

Continuous Data Workflows, Uninterrupted 

Condense ensures uninterrupted execution of transforms for continuous streaming data processing, delivering precise outputs to end applications. From hosting to running your applications, Condense manages the entire backend with guaranteed uptime and effortless scalability. Whether scaling during peak loads or maintaining stability for critical workflows, our fully managed platform adapts to your demands with ease. With consistent performance and reliability, Condense empowers your business to thrive every step of the way. 

Summary and Strategic Fit 

The real-time fraud detection use case requires a platform that can analyze transactional and contextual data together and make risk decisions in milliseconds. Condense fulfills this need by combining industry-optimized ingestion, in-stream logic, and downstream response capabilities. 

By deploying this system entirely on Condense, financial institutions achieve rapid time-to-value, operational flexibility, and enterprise-grade performance in combating fraud. Condense is not merely a streaming data pipeline—it is a fully integrated platform for building real-time financial intelligence applications. From ingestion to decisioning, from fraud alerting to enforcement, Condense offers a production-grade, developer-empowered framework for rapid, scalable fraud prevention. 

Financial institutions using Condense gain

• Faster detection and automated blocking of fraudulent activity 

• Reduced false positives through contextual correlation 

• Operational transparency and audit-readiness 

• Faster deployment cycles and lower integration overhead 

Condense enables banks and fintech to go from data to decision in milliseconds.