Financial ServicesFeatured2023

Real-Time Analytics Dashboard

Enabling data-driven decisions with millisecond latency insights

A Fintech Company

5 months

Sydney, Australia

PythonApache KafkaTensorFlowReactPostgreSQL+1 more

5 months<100ms Data Latency

About the Client

A Fintech Company

A leading fintech company providing trading and investment services to institutional clients, processing high-volume daily transactions.

Industry

Financial Technology

Company Size

150+ employees

Location

Sydney, Australia

The Challenge

Problem Statement

The client needed real-time analytics to monitor trading activities, detect anomalies, and generate compliance reports. Their existing system had 15-minute data delays and couldn't handle the volume of transactions.

Trading decisions were being made on stale data, leading to missed opportunities and increased risk exposure. Compliance reporting required 3 days of manual work each month.

Goals & Objectives

Achieve sub-second data latency
Implement real-time anomaly detection
Automate compliance reporting
Handle 1M+ events per second
Enable self-service analytics for traders
Reduce operational costs

Our Strategy

Our Approach

How we planned and executed the solution

Data Architecture Review

Analyzed existing data flows and identified bottlenecks

Stream Processing Design

Architected event-driven system for real-time processing

ML Model Development

Trained custom models on historical data for anomaly detection

Dashboard Design

Created intuitive visualizations for complex financial data

The Solution

What We Delivered

A high-performance analytics platform that processes millions of events per second and delivers actionable insights in real-time.

Core Features

Real-time streaming data pipeline
ML-powered anomaly detection
Interactive drill-down dashboards
Automated compliance report generation
Custom alert configuration
Historical data analysis and backtesting
Role-based access control
API for third-party integrations

Technologies Used

Streaming

Apache KafkaApache FlinkRedis Streams

ML/AI

TensorFlowPythonScikit-learn

Frontend

ReactD3.jsWebSocket

Infrastructure

AWSKubernetesPrometheus

Project Timeline

Implementation Phases

Total project duration: 5 months

Phase 1

Infrastructure Setup

3 weeks

Kafka cluster, data lake, and processing pipeline

Phase 2

Data Integration

4 weeks

Connecting all data sources and establishing pipelines

Phase 3

ML Development

5 weeks

Training and deploying anomaly detection models

Phase 4

Dashboard Development

4 weeks

Building interactive analytics interface

Phase 5

Testing & Optimization

2 weeks

Performance tuning and UAT

Obstacles Overcome

Challenges & Solutions

Real challenges we faced and how we solved them

Challenge

Processing 1M+ events/second

Solution

Implemented distributed processing with Kafka partitioning and parallel consumers

Challenge

ML model accuracy

Solution

Ensemble approach combining multiple models with continuous retraining

Challenge

Data consistency at scale

Solution

Implemented exactly-once semantics with idempotent producers

Results

The Impact

Measurable outcomes that made a real difference

<100ms

Data Latency

Real-time data processing

99.5%

Anomaly Detection

Accuracy rate

-80%

Report Generation

Time reduction

Significant

Cost Savings

Annual operational savings

Final Outcome

Project Summary

The client now makes data-driven decisions in real-time, significantly reducing risk exposure and improving trading performance. The automated compliance reporting saves 3 FTE equivalents annually.