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Saving $1M through Intelligent Transaction Monitoring

Streamlining Retail Dealer Operations with AI-Driven Auditing and Insights

Goal

Improve transaction accuracy & fraud detection, and provide actionable insights for retail mobile phone dealers.

Solution

Platform aggregating data, enhancing quality, AI engine flagging discrepancies, frauds, refunds & chargebacks. Enabled flagging, tracking, and action.

Result

Saved client over $1 million, detected intentional and unintentional losses & reduced fraud detection time to 4 days.

Business Introduction

In the US, many retail dealers operate under various wireless carriers to drive device and service sales. These dealers sell using tools offered by the carrier and some third-party tools. Sprint was one such carrier whose dealers faced challenges due to underutilized data and recurring issues of chargebacks, lease frauds, and transactional discrepancies.

Inefficient Manual Transaction Processing and Fraud Detection

Improving transaction processing in terms of time and accuracy
Flagging transactions with potential fraud or discrepancies
Making the analysis actionable for decision-making and recording
Providing holistic access to transactions across the organization

AI-Powered Sales Auditing and Transaction Monitoring

Created a robust data engineering pipeline using RPA and Python scripts to fetch data from multiple sources and ingest it into a data warehouse.

Built data transformers to parse and process data for proper conversion between changing formats.

Implemented a rule-based AI (GOFAI) engine to flag discrepancies, frauds, and assess refunds and chargebacks.

Developed a platform for auditors, management, and employees to track the transaction vetting process and actions taken based on insights.

Implemented email bounce analysis and robocalling to verify fraudulent transactions.

Created a robust data engineering pipeline using RPA and Python scripts to fetch data from multiple sources and ingest it into a data warehouse.
Built data transformers to parse and process data for proper conversion between changing formats.
Implemented a rule-based AI (GOFAI) engine to flag discrepancies, frauds, and assess refunds and chargebacks.
Developed a platform for auditors, management, and employees to track the transaction vetting process and actions taken based on insights.
Implemented email bounce analysis and robocalling to verify fraudulent transactions.

Comprehensive Auditing and Actionable Insights

Data aggregation from multiple sources
Data quality enhancement with missing data handling
Robust process to handle changing requirements
Accurate commission reconciliation
Actionable insights and tracking for remedial actions

Measurable Results and Impact

Achieved client cost savings exceeding $1 million within 1.5 years
Enabled detection of both intentional and unintentional losses by employees
Reduced fraud and discrepancy detection time from 4 weeks to 72 hours

Key Achievements

Developed a robust data engineering pipeline and AI-powered auditing platform
Integrated multiple data sources and handled changing data formats
Provided actionable insights and enabled tracking of remedial actions
Achieved significant cost savings and reduced fraud detection time

Conclusion

Through our AI-driven sales auditing and transaction monitoring platform, we empowered retail dealers to streamline their operations, improve transaction processing accuracy, and detect frauds and discrepancies efficiently. The platform’s ability to aggregate data, enhance data quality, and generate actionable insights led to substantial cost savings, reduced fraud detection time, and improved overall operational efficiency.

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