Case Study
Embedded Analytics Support for a major Australian Payments Provider
The Challenge
We were brought in by a major Australian payments provider shortly after a merger across several subsidiaries. The corporation had a strong need to unify data systems and strategy across multiple business divisions handling billions of annual transactions.
Our Expertise
We supported the development of a comprehensive cloud-native data platform to consolidate disparate systems, enable advanced analytics, and deliver secure external data sharing capabilities. The solutions transformed how the organisation leveraged data across all business operations while meeting strict financial services compliance requirements.
The Solution
These projects evolved into an ongoing 3 year data partnership, comprising of several key phases:
Multi-Source Pipeline Development
Built robust data ingestion pipelines connecting disparate systems including cloud platforms, CRM databases, and legacy batch processing formats, establishing automated ETL/ELT processes.Cloud Data Warehouse Implementation
Supported the design and deployment of scalable AWS + Snowflake architecture capable of processing billions of annual transactions with optimised storage and compute separation for cost efficiency.Data Model Harmonisation
Created unified schemas standardising customer, merchant and institutional data across previously siloed business units, enabling consistent reporting and cross-divisional analytics.Advanced ML Model Development
Built and deployed XGBoost predictive models and customer segmentation models for commercial and marketing use cases.Geospatial Analytics Implementation
Created location prediction capabilities analysing transaction history to determine customer geographic patterns, supporting location-based business intelligence and risk management.Secure External Data Sharing
Implemented SFTP-based encrypted data exchange systems enabling secure B2B data sharing with external partners while maintaining compliance with financial services regulations.Natural Language Data Access
Supported development of semantic layers enabling natural language processing with chatbots, allowing business users to query complex financial data using conversational interfaces and receive instant insights without technical expertise.Our Impact
We delivered a unified data foundation enabling real-time cross-divisional analytics and automated external partner data sharing. Successfully consolidated multiple legacy systems into scalable cloud architecture, established advanced ML capabilities for predictive customer insights, and created a secure, compliance-ready data governance framework supporting future digital transformation initiatives.
We have also since become the company’s partner for data analytics, data engineering and AI implementation, deeply embedded within their team and working closely with their technology, finance, sales and operational teams.
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