Mitigating Chargeback Disputes with the Power of AI, ML & Data Analytics

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Due to a dwindling economy, higher inflation and a cash-starved population, retailers are facing a tough battle from the standpoint of acquiring new customers or retaining the existing ones. Adding to their woes, the sum total effect of the above mentioned problems has opened a new front, the chargeback frauds. 

According to a Bloomberg report , the average merchant fraud-to-transaction ratio increases by almost 50% during times of recession. The payment sector has been programmed so that it gives excess leverage to customers’ interests rather than retailers aiding such biases. Unfortunately, the battling times due to the COVID-19 pandemic, wars, and excessive money printing have set up the narrative for a long-stretched recessional period ahead. Hence, chargebacks will become a daily part and parcel for retailers. 

How’d they be impacted? Firstly,  retailers will have to prove that the transaction is legit in order to avert their losses. Secondly, if they fail to do so, not only do they lose the goods but also have to bear an added chargeback cost that banks slap on them. So, there’s a double jeopardy involved in this equation, and they do not know how to deal with this mess. 

Enter: AI, ML and Data Analytics

What is AI/ML?

AI/ML represents an evolution in using data, which could have a far greater impact on how business efficacies could be achieved. Through the use of AI/ML, not only are business processes getting smoothly transitioned, but it is also impacting the rise of financial crimes in the business realm by challenging them from the front through its actionable, decisive data intelligence and insights to avert such frauds.

Role of AI/ML & Data Analytics in Dealing with Chargebacks Disputes

Understanding the Causality Metrics

Every single piece of data has a story; you just need to have the eyes and ears to comprehend the same. In the chargebacks, there are some patterns and common triggers we couldn’t identify independently. However, when AI, ML or Data Analytics come into the picture, they collectively look at data from a vantage point and identify the patterns.

Once that has been validated, they would self-program a defence mechanism against such patterns. For example, in every transaction, there’s the need for transaction value, product SKU, and credit card type; however, the missing piece is IP data, device type, VPN, proxy or TOR usage.

When the IP data, device type and proxies are baked into an AI/ML tracker algorithm, they provide a much more detailed analysis of the online habits that perpetrators usually follow for conducting such financial crimes.

Upon figuring that out, it becomes very simple to design rule-based actions that can analyze and avert false chargeback triggers. Some of the battle-tested ways that have averted chargebacks are single parameter and multiple complex window-based assessments. These rules can provide key determinants like customer logging habits and buying patterns.

Reviewing such historical data provides a trend analysis, and these inputs can be hard-coded into the systems to analyze through behavioural patterns how the customers would be reacting to the e-commerce storefronts using debit and credit cards. Once the system researches the same and provides a replica behaviour through its database, the system can create an auto-defence mechanism and prevent such transactions from happening.

In this way, it protects retailers and businesses from getting victimized by fraud and cooks. If the users genuinely provide all the details required that could justify the causality metrics, in such a case, the chargeback can be labelled as legit.

Authenticating Identity

Identity authentication is another major key point of failure in chargeback fraud. Most users contemplate stories based on these loopholes. For example, they present justifications like the compromise of the password/ username. The PSD2 and 3DS2 ML/AI validation systems break the authentication bottlenecks into two parts:

  1. PSD 2 asks the customer to provide information in three categories, namely, knowledge, possession, and inherence. Meaning, in addition to providing information like security questions, credit card numbers, authorized numbers.
  2. The 2FA shall ask for other collective information on the payment channels for validation. Once that is figured, only then will the transaction be validated for a purchase. However, if the user fails to produce the same within a stipulated time frame, the transaction will not be executed, and it will be rolled back to the original state.


As digital dwelling has been increasing incessantly over time, perpetrators have found ways to abuse user presence. However, the evolution of technology has also enabled countermeasures in the form of AI/ML to identify trends and take necessary steps to avert such frauds. AI, ML and data analytics have a long journey to pursue and deliver unprecedented results when protecting the payment network. At this moment, we have just touched the tip of the iceberg when it comes to navigating through the complexity of the chargebacks using AI/ML.

Here’s where VE3 can help you. In this ever-evolving cybersecurity landscape, our cutting-edge AI and machine learning capabilities can effectively detect, prevent, and combat payment fraud and chargebacks. By harnessing the power of our expert solutions, businesses can stay one step ahead in the ongoing battle against fraudulent activities, ensuring the integrity of their payment networks and the safety of their customer’s financial transactions. As we move forward into this era of digital commerce, our expertise stands as a reliable partner in the ongoing quest for security and trust in the digital realm.


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