According to our own research, 60% of all chargeback claims will be fraudulent in 2023. In fact, you might be surprised to learn that third-party, or “criminal”, fraud actually makes up a small portion of overall chargeback issuances. Data from Visa shows that 75% of chargebacks can be traced to errors, miscommunication, or deliberate misuse of the chargeback process. In fact, non-criminal chargebacks are expected to cost merchants more than $100 billion in 2023 alone.
Businesses want to avoid chargebacks, and so it’s essential they lay the groundwork to collect and analyze data to help improve decisioning on future cases. While many industries are benefiting from so-called ‘big data’ – the automated collection and analysis of very large amounts of information – chargebacks face a problem. The information that is given to merchants concerning their chargeback claims tends to be very limited, being based on response codes from card schemes (‘Reason 30: Services Not Provided or Merchandise Not Received’), meaning that merchants would have to do a great deal of manual work to reconcile the information that the card schemes supply with the information that they have on hand. While Visa’s Order Insight, Mastercard’s Consumer Clarity, and the use of chargeback alerts have reduced the number of chargebacks, merchants still have very little data on chargeback attempts.
With that said, let’s look at the ways in which merchants can improve the level of data they receive on chargebacks and how they can use this data to create actionable insights on how to improve their handling of chargebacks.
Big data under the microscope
Big data refers to the large and complex data sets that are generated by various sources, including social media, internet searches, sensors, and mobile devices. The data is typically so large and complex that it cannot be processed and analyzed using traditional data processing methods.
In recent years, big data has become a crucial tool for businesses and organizations looking to gain insights into customer behavior, improve decision making, and enhance operational efficiency. To process and analyze big data, companies are increasingly turning to advanced technologies like artificial intelligence (AI) and machine learning.
One example of a company that is using big data to drive innovation is ChatGPT, a large language model trained by OpenAI. ChatGPT uses big data to learn and understand language patterns, enabling it to engage in natural language conversations with users. To train ChatGPT, OpenAI used a large and diverse data set of text, including books, websites, and social media posts. The data set included over 40 gigabytes of text, which was processed using advanced machine-learning algorithms to create a language model with over 175 billion parameters.
By using big data to train ChatGPT, OpenAI was able to create a language model that is more accurate and effective at understanding and generating responses than previous models. This has enabled ChatGPT to be used in a wide range of applications, including customer service chatbots, language translation services, and virtual assistants. Currently, technology very similar to ChatGPT is being used by Bing to replace traditional web searches, with mixed results, but, like self-driving cars, it is a matter of ‘when’, not ‘if’ this technology will become widespread.
AI and chargeback fraud
Chargeback fraud is a growing problem for businesses of all sizes. In fact, the National Retail Federation previously estimated that retailers lose $50 billion annually to fraud, with chargeback fraud making up a significant portion of that total. With the significant rise of online shopping, this type of fraud has become even more prevalent, as it is much easier for fraudsters to make purchases using stolen credit card information, forcing victims of fraud to then dispute the charges with their credit card issuer.
Chargeback fraud occurs when a customer disputes a valid charge made on their credit card, claiming that they did not make the purchase or that the merchandise they received was not as described. If the dispute is upheld, the merchant is forced to refund the money to the customer, along with any associated costs, and is typically charged a penalty fee by their payment processor. This not only results in a financial loss for the merchant, but can also damage their reputation and lead to increased scrutiny from payment processors.
A tailored solution
To address this issue, it’s important to understand and develop AI solutions specifically designed for chargebacks. Models, for example, that are built on big data sets collected over multiple years, making them ideal for businesses looking to address the growing issue of chargeback fraud. These models enable real-time detection and prevention of fraud by utilizing AI algorithms that are specifically tailored to identify fraudulent transactions. Utilizing AI in chargeback fraud prevention is crucial as it aids in the identification of fraudulent transactions by processing vast amounts of complex data, a task that traditional methods often fail to achieve.
The challenge with AI in chargeback fraud prevention is ensuring that the models address specific problems like fraudulent transactions rather than generalized problems. This differentiation can mean the difference between a successful AI model that produces actionable insights and one that generates erroneous responses.
About Author:
Monica Eaton is the Founder and CEO of Chargebacks911 and Fi911, as well as Chief Information Officer of Global Risk Technologies. Monica has worked tirelessly to educate merchants and financial institutions about hidden threats in the rapidly changing payment fraud landscape. Leading Chargebacks911, was founded in Tampa Bay, Florida, expanding internationally also to become Europe’s first chargeback remediation specialist to tackle the chargeback fraud problem. In ten years, Chargebacks911 has successfully protected more than 10 billion online transactions and has recovered over $1 billion in chargeback fraud.
Recognizing that the impact of chargebacks goes beyond merchants, Fi911 provides unrivaled support to financial institutions with innovative back-office management technologies. Fi911’s pioneering DisputeLab™ tool streamlines chargeback management for acquirers, automating legacy processes and standardizing methods that simplify and speed the end-to-end workflow, improving the customer experience and accountability for all stakeholders.
Monica is a passionate diversity advocate committed to developing and sharing innovative solutions that empower the global fintech space. She has earned numerous awards, distinctions and special recognitions, including the Retail Systems Awards, where she received the ‘Outstanding Individual Achievement Award’ and was named ‘Global Leader of the Year’ at the Women in IT Awards.
Uma Rajagopal has been managing the posting of content for multiple platforms since 2021, including Global Banking & Finance Review, Asset Digest, Biz Dispatch, Blockchain Tribune, Business Express, Brands Journal, Companies Digest, Economy Standard, Entrepreneur Tribune, Finance Digest, Fintech Herald, Global Islamic Finance Magazine, International Releases, Online World News, Luxury Adviser, Palmbay Herald, Startup Observer, Technology Dispatch, Trading Herald, and Wealth Tribune. Her role ensures that content is published accurately and efficiently across these diverse publications.