Unnamed APAC Bank

A major APAC bank's financial data analysis process was heavily manual, requiring analysts to extract, validate, and synthesize data from diverse financial documents. The process was slow, error-prone, and expensive to scale.

The bank partnered with Amdocs to deploy an AWS-powered MLOps and GenAI pipeline that automates the financial data analysis process. The solution uses large language models for document understanding, data extraction, and analysis, with MLOps infrastructure ensuring reliable model deployment and monitoring.

The automated pipeline delivered a 90% reduction in operational costs and 52x productivity gains compared to the manual process. Analysts were freed to focus on higher-value interpretation and decision-making rather than data extraction and formatting.

• Financial data analysis automation delivers extreme ROI (52x productivity) because the manual baseline involves highly repetitive extraction tasks.
• MLOps infrastructure is essential for production GenAI in banking — ensuring model reliability, monitoring, and governance.
• A 90% cost reduction makes previously uneconomical analysis tasks viable at scale.