Retail Case Study

An AI-Driven Fraud Mitigation Framework

Business Problem:

A national furniture retailer faced numerous suspected fraudulent orders from its online store. The retailer relied on a labor-intensive and subjective loss prevention process involving painstaking manual checks of customer information against public records. Consequently, it often failed to intervene before fraudulent orders slipped through due to delays inherent in this manual approach.

Our AI-Powered Solution:

Leveraging state-of-the-art automation techniques, Proactive Technology Management transformed the customer validation process into a streamlined, data-driven workflow. The project began with a comprehensive mapping of the current manual procedures, isolating tasks ripe for algorithmic intervention. Proactive then engineered an intelligent application that evaluates online transactions in real-time. The system employs sophisticated rules and dynamic scoring mechanisms to flag potentially fraudulent orders and seamlessly route them into a dedicated review queue.

Key features include:

• Automated Risk Assessment: Real-time analysis of customer data to detect anomalies and flag high-risk transactions.
• Dynamic KYC Integration: SMS-based prompts and automated generation of KYC questions (e.g., previous addresses, partial SSN, place of birth) facilitate rapid identity verification for semisuspicious cases.
• Instantaneous Decision-Making: Explicitly fraudulent orders are automatically declined, reducing the window for financial loss.
• Seamless Legacy Integration: The solution integrates effortlessly with the retailer’s existing eCommerce platform and legacy systems, ensuring minimal disruption while enhancing overall operational security.

Example Timeline:

• Phase 1: Discovery & Mapping (1 week): Analyze manual fraud workflows and identify automation opportunities.
• Phase 2: Design & Prototyping (4 weeks): Build scoring logic and KYC features; validate with historical data.
• Phase 3: Integration & Testing (4 weeks): Integrate with legacy systems and test performance.
• Phase 4: Deployment & Optimization (3 weeks): Launch system and fine-tune based on early results.

Outcome:

By automating its core fraud detection processes, the retailer significantly reduced human error and accelerated response times. The business now intercepts most suspicious transactions before shipment, achieving monthly savings of up to $160,000. Furthermore, it leverages this enhanced efficiency to process more orders with a leaner workforce, cutting annual labor costs by approximately $150,000 and bolstering security -achieving a transformative shift through AI and advanced automation.

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