Fintech Startup Builds Real-Time Analytics Platform
The Challenge
A rapidly scaling digital payment fintech serving 2.5 million active users was processing 45 million transactions monthly but lacked real-time visibility into fraud patterns, transaction anomalies, and customer behavior. Their existing batch-based analytics ran overnight, meaning fraud detection had a 12-24 hour lag, resulting in $2.8M annual fraud losses. Regulatory compliance (PSD2, GDPR) required real-time transaction monitoring and reporting capabilities that didn't exist in their infrastructure. The company's data stack consisted of fragmented PostgreSQL databases, with analysts manually exporting CSV files and building reports in spreadsheets. Critical business decisions were made on day-old data. The data engineering team had attempted to build a real-time analytics solution using custom Python scripts, but the system was unreliable, difficult to maintain, and couldn't handle the velocity of incoming transaction streams. As the company prepared for Series B funding, investors demanded better operational metrics and fraud prevention capabilities. The CEO mandated a complete real-time analytics transformation within 90 days.
The Strategy
- 1 Design event-driven architecture using Apache Kafka for real-time data streaming
- 2 Implement Dremio as unified query layer for sub-second analytics across data sources
- 3 Build automated fraud detection system using real-time pattern recognition
- 4 Create executive dashboards with live KPIs for operational decision-making
🔄 Real-Time Data Streaming Architecture
The Problem We Found
Transaction data was trapped in PostgreSQL operational databases with no streaming infrastructure. ETL jobs ran once daily, creating 24-hour data latency. No event-driven architecture existed for real-time processing.
Our Approach
- Deployed Apache Kafka cluster with 3 brokers and topic-based streaming for transaction events
- Implemented Change Data Capture (CDC) using Debezium to stream PostgreSQL changes to Kafka in real-time
- Created Kafka consumers for fraud detection, compliance monitoring, and analytics workloads
- Established schema registry for event schema management and backward compatibility
- Built monitoring framework using Confluent Control Center for topic lag and throughput visibility
The Result
Achieved real-time data streaming with sub-second latency from transaction to availability for analytics. Processing 45M+ events monthly with 99.9% delivery guarantee. Event-driven architecture enabled 15+ downstream consumers for various use cases.
Metrics
⚡ Unified Query Layer with Dremio
The Problem We Found
Analysts had no self-service query capability and waited days for data engineering to extract data. Queries against operational PostgreSQL databases caused production slowdowns. No unified view existed across multiple data sources.
Our Approach
- Deployed Dremio as data lakehouse query engine connecting PostgreSQL, Kafka topics, and S3 data lake
- Created virtual datasets with joins across sources, eliminating need for physical data movement
- Implemented data reflections (accelerated materialized views) for frequently-accessed datasets
- Established row-level security policies for GDPR compliance and data access governance
- Built semantic layer with business-friendly naming and pre-aggregated metrics
The Result
Analysts gained self-service access to all data sources through single SQL interface. Query performance improved from minutes to sub-100ms through data reflections. Eliminated 90% of ad-hoc data extraction requests to engineering team.
Metrics
🛡️ Real-Time Fraud Detection & Dashboards
The Problem We Found
Fraud detection relied on manual daily reviews, missing 85% of fraudulent transactions in real-time. No automated alerting existed for suspicious patterns. Executive team lacked visibility into key business metrics.
Our Approach
- Built real-time fraud detection rules engine analyzing transaction patterns, velocity, and anomalies
- Implemented machine learning model scoring transactions in-stream using historical fraud patterns
- Created automated alerting system triggering immediate notifications for high-risk transactions
- Deployed Tableau dashboards with live connections to Dremio showing real-time KPIs
- Established compliance reporting framework automatically generating PSD2 transaction reports
The Result
Fraud detection now catches suspicious transactions within 200ms, blocking them before settlement. Reduced fraud losses by 78%. Executive dashboards provide real-time visibility into transaction volume, revenue, user growth, and fraud metrics. Compliance reporting automated, reducing manual effort by 95%.
Metrics
Impact & Results
The real-time analytics platform transformed the fintech's operational capabilities and competitive position. Fraud losses decreased 78% from $2.8M to $615K annually through immediate detection and blocking of suspicious transactions. The company now processes transactions with confidence, knowing fraudulent activity is caught in milliseconds rather than hours. Analyst productivity increased dramatically with self-service access to all data sources through Dremio, eliminating the backlog of data requests that previously plagued the engineering team. Executive dashboards provide live visibility into critical KPIs, enabling data-driven decisions in real-time rather than relying on day-old reports. The scalable architecture successfully supported the company through Series B funding, with investors citing the advanced analytics capabilities as a key differentiator. Compliance reporting automation reduced manual effort by 95%, ensuring regulatory requirements are met effortlessly.
"Zatsys delivered a real-time analytics platform that exceeded our expectations. Going from 24-hour data latency to sub-second queries changed how we operate. We caught and blocked a $450K fraud attempt in real-time last month - that alone justified the entire investment. Our investors were blown away by our operational dashboards during due diligence."
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