How the use of artificial intelligence in retail transactions saved over $1 million
About the case
The challenge: Tackling transaction inefficiencies and fraud risks
Manual transaction processing led to inefficiencies and increased risks of fraud in sales auditing.
Time-consuming and error-prone transaction processing.
Fraudulent transaction and discrepancy detection was difficult.
Lack of actionable analysis for better decision-making.
Limited organizational access to holistic transaction data.
No integration with fraud prevention systems.
Limited scalability for high-volume processing.
Redefining auditing standards with AI integration
An AI-powered audit platform improved transaction monitoring and made decision-making easier.
Generated a robust RPA and Python data pipeline.
Created data format transformers for evolving data formats.
Rule-based AI for fraud detection, discrepancies, and refunds.
Developed a platform for tracking vetting and remediation.
Detected fraud through email bounce analysis and robocalls.
The measurable impact
Saved over $1 Million for the client within 1.5 years.
Optimized data quality enhanced decision-making.
Uncovered employee losses, intentional and accidental.
Reduced fraud detection time from 4 weeks to 72 hours.
Operational efficiency increased through automated auditing.
Project Snapshot
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