A Tier One Bank’s AI Revolution in Reconciliation
Introduction
AI-powered reconciliation is reshaping how financial institutions handle exceptions. This use case explores how a tier-one global bank partnered with NeoXam Aro to streamline its reconciliation workflows using machine learning. Faced with increasing transaction volumes and mounting operational risk, the bank turned to automation to gain scalability and efficiency.
Smarter Exception Handling in Action
NeoXam Aro empowered a global financial institution to automate exception classification and remediation across its high-volume credit card business. Discover how machine learning helped them scale operations, cut costs, and reduce risks.
Client
Tier one global bank
Industry
Banking –
Card Transactions
Solution
NeoXam Aro with
Machine Learning for
classification & automation
Challenge
Scaling manual
exception management
in high transaction volumes
Impact
Reduced breaks and faster
resolution across operations
How Machine Learning Supports Reconciliation
By analyzing historical data, AI-powered reconciliation predicts break causes, suggests resolutions, and applies fixes in real time—eliminating manual tasks and reducing errors. This intelligent approach helps financial teams scale operations, lower break values, and stay compliant more efficiently.
Results
NeoXam Aro transformed reconciliation for the client, turning a labor-intensive process into a scalable, intelligent, and automated function—resulting in measurable cost savings, risk reduction, and performance gains.
