Amazon asked our team to tackle the critical issue of measuring the risk of friendly fraud, also known as chargeback fraud. The client recognized the importance of identifying and mitigating this fraud to safeguard their business and maintain customer trust. To tackle this challenge comprehensively, we leveraged a dataset from 5,000 customers over a two-year study period. The central research question guided our investigation: “What is the probability of the risk of friendly fraud (aka chargeback fraud)?” To address this research question effectively, we developed an analytical algorithm and data modeling set to predict which customers would most likely request a chargeback.
Our analysis yielded a significant finding. Through our analytical algorithm and data modeling, we determined a 53.82% probability of encountering the risk of friendly fraud, also known as chargeback fraud. This finding highlighted the potential challenges associated with this type of fraud in the business landscape.
The client gained crucial insights into the risk of friendly fraud (chargeback fraud) through our research. These findings provided valuable information for their fraud prevention and risk management strategies. Armed with this knowledge and the provided analytical algorithm software module(s), the client could proactively identify and address potential instances of friendly fraud. This proactive approach had the potential to reduce financial losses, enhance customer trust, and protect the integrity of their business operations.
In summary, this project addressed Amazon’s need to measure the risk of friendly fraud (chargeback fraud). The research findings and analytical tools provided actionable insights that informed their fraud prevention efforts, ultimately contributing to more secure and trustworthy customer interactions.
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