Last Updated on July 11, 2024 by Asfa Rasheed
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New Patent Unveils Advanced Database System for Proactive Refund Risk Analysis
Customer satisfaction can often hinge on the simple availability of a refund for an unwanted, defective, or damaged item. As e-commerce and online retail surge, refund policies are a safety net for consumers, so that they can feel safe about shopping online. A good refund policy boosts satisfaction and builds trust.
But this same safety net can be exploited by fraudulent activities – because unfortunately, there will always be individuals who engage in systematic abuse of these policies, often spreading their activities across various merchants to fly under the radar. Traditional refund tracking mechanisms, largely siloed within individual merchants, are ill-equipped to handle such sophisticated fraud patterns.
This is where Sushma Daggubati’s patent on “Database system architecture for refund data harmonization” steps in, introducing a robust solution designed to thwart refund fraud before it occurs.
The Intricacies of Refund Fraud Detection
Refund fraud can take many forms – sometimes fraudsters will return intentionally used, damaged, or dirty products. Sometimes they’ll substitute the original items with counterfeits or even just random items purchased elsewhere (for less money!). A fraudster will even place a large online order and claim (falsely) that they received nothing but an empty box on their doorstep.
One thing is for sure: fraudsters are getting more inventive. So the systems designed to catch them must evolve simultaneously. Existing systems, which flag individuals based on past abuses, typically operate within the confines of a single merchant’s dataset. They are reactive rather than proactive, intervening only at the point of return or refund – a stage where the fraudulent act has already been committed.
Anyone who has worked in customer service can relate to customers who have been “black flagged” – that one customer who always seems to be missing the most expensive item in their order, or who always claims they didn’t receive packages which show to be delivered by the carrier. One company can mark them as a fraudster, but that doesn’t protect any other retailer from getting scammed by these thieves.
Daggubati’s patent transforms this landscape by introducing a Refund Tracking (RT) computing device, which harmonizes transaction data across different merchants and formats into a unified database system. This device doesn’t just track refund activities; it predicts refund risks by analyzing transaction patterns across a diverse array of merchants.
Technical Overview of the Refund Tracking System
At the core of Daggubati’s patent is the RT computing device, equipped with a processor and a memory. This device receives and parses historical transaction data from various sources, which may arrive in multiple formats. Critical data fields such as cardholder identifiers, transaction dates, amounts, and merchant identifiers are extracted and stored in a harmonized refund data structure within a database. This harmonization is a game-changer, allowing for the cross-referencing of transactions against a broader dataset to identify potential fraud.
When a current transaction occurs, the system retrieves relevant harmonized refund data structures by matching the primary account number involved. It then calculates a refund risk score by comparing the current transaction data with historical data patterns. This score is indicative of the likelihood that a transaction could result in a fraudulent refund request.
Strategic Intervention and Fraud Prevention
What sets this system apart is its ability to proactively alert merchants of potential risk at the time of purchase. By providing a refund risk score, the RT computing device empowers merchants to take preemptive action. This could range from enhanced scrutiny at the time of return to flagging certain transactions for closer monitoring. The strategic intervention before a fraudulent activity materializes is a pivotal shift from existing post-refund intervention mechanisms.
The Architecture and Methodology
The RT computing device’s methodical approach to identifying fraud begins with the aggregation of historical transaction data. By parsing and storing this data in a harmonized format, the system builds a comprehensive dataset that serves as a benchmark for evaluating future transactions.
The determination of the refund risk score is a sophisticated process that involves data analytics and pattern recognition. The system actually compares the new transaction data with the harmonized historical data, and is thus able to identify discrepancies or patterns that suggest fraudulent behavior.
Implications and Advantages
The potential implications of Daggubati’s patent are significant for the retail and e-commerce industry. This technology creates a centralized database system that facilitates the sharing of refund data across merchants, thus paving the way for a more unified front against refund fraud. And by intervening at the point of sale rather than post-refund, it reduces the financial and operational burdens associated with refund abuses.
Fighting Fraud
Sushma Daggubati’s “Database system architecture for refund data harmonization” stands as a testament to the innovative application of data harmonization principles in combating retail fraud. Because it predicts refund fraud risks with precision and enables timely interventions, this patent addresses a critical pain point in the retail sector. As the retail and e-commerce industry continues to grow, such technological advancements will be crucial in safeguarding the integrity of consumer-merchant transactions and protecting businesses from the escalating threat of refund fraud.
Learn more: https://patents.google.com/patent/US11321653B2/en
