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From reactive to real-time,
a lending business that can trust its data.
The challenge
The client was processing over 100 loan approvals every day, a volume that demands fast, accurate, and data-driven decision-making. But the data infrastructure underneath the business was not keeping up.
Transactional, KYC, payment, and loan origination data sat across seven separate source systems with no integration between them. There was no data warehouse, no unified view, and no analytics layer. Reporting was a manual exercise that produced results the following day, by which point the data was already stale. Fraud detection was entirely reactive: the team would identify fraudulent activity after the fact, with no systematic way to catch patterns before losses occurred.
For a lending business operating at this pace, the gap between when fraud happens and when it is detected is where money walks out the door.
What PGI delivered
PGI-Data designed and built a cloud-native data warehouse on Google BigQuery from the ground up, structured across three layers:
- Ingest: All 7 source systems, loan origination, payment, KYC, and transactional data, piped into BigQuery via automated ETL pipelines, eliminating manual data handling entirely
- Model: Data modelled into a unified schema, clean, deduplicated, and structured for both analytical queries and fraud rule evaluation simultaneously
- Analyse: Fraud detection logic and analytics dashboards built on top, enabling near real-time flagging and same-day reporting
Between 300GB and 700GB of data was consolidated into a single, clean, queryable warehouse. A fraud detection layer was built on top using pattern recognition and rule-based logic across transaction history, shifting the team from reactive investigation to proactive intervention on active loan applications.
Measurable outcomes
unified into one DWH
fraud losses
→ Hours
turnaround
Technology & capabilities
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