Ai Editorial: Chargeback guarantee + higher approval rates – is it for real?

First Published on 23rd November, 2018

Ai Editorial: As more fraud-related solutions get introduced, the promise of protection against chargebacks is getting stronger. What needs to be evaluated before opting for the same, probes Ritesh Gupta


Managing fraud liability and availing chargeback (a forced transaction reversal initiated by the cardholder’s bank) guarantee on every transaction approved at first go comes across as an attractive option when considered from a merchant’s perspective.

Whether there can be 100% prevention of chargebacks remains an interesting discussion, still merchants such as airlines have to work on a risk mitigation plan to cut down on the same.

According to Chargebacks911, chargebacks are caused by criminal fraud (1-10%), friendly fraud (50-80%), or merchant error (20-40%).

Liability shift 

As more fraud-related solutions get introduced, the promise of protection against chargebacks is getting stronger.

But is there any hidden factor that needs to be considered? What needs to be evaluated before opting for the same?

One of the factors is cost vs. chargeback protection.

“Some fraud solutions in the market today offer a guaranteed chargeback protection, which means that they will take financial responsibility for any approved order that turns out to be fraudulent. This shifts the liability away from merchants and onto these fraud specialists,” says Justin Lie, CashShield’s CEO. “However, not all e-commerce merchants will choose to take up the chargeback protection service, depending on their existing chargeback rates and business goals. For example, some solutions factor in the chargeback protection by increasing the cost of service, and a merchant with low chargeback rates may consider their fraud cost lower than the cost of deploying a chargeback protected fraud serviced.”

The cost of the chargeback protected service will be one of the important considerations - if the merchant ends up losing more on cost with the liability shift, then perhaps the merchant would be better off without the chargeback protection.

Second factor is the risk appetite of an e-commerce organization.

A shift in liability might also mean that the merchant would be open to accepting more risk, and therefore more fraud. With that in mind, the travel merchant must consider their risk appetite, whether or not accepting more risk is possible, or if their main goal is to minimize fraud as much as possible. At the same time, fraud rates must still be kept at an acceptable level and not be left too high, or the merchant may be left with warnings and suspensions from card issuers.

Focus shouldn’t be only on “guarantee” 

One can’t ignore the significance of accurately detecting fraud attempts and stopping fraudsters from succeeding in whatever they intend to do.

As specialists at Chargebacks911 point out, merchant errors which can be rather simple and inadvertent need to be curbed. For friendly fraud, a key option for merchant is to strategically argue unlawful chargebacks when they're issued. Each chargeback dispute conveys a powerful message to the issuing bank, asserts Chargebacks911. It also points out by doing so merchants end up restoring their innocence and also improving their association rapport with the issuer. Eventually a merchant freed of any apparent fault are subjected to lesser friendly fraud chargebacks.

In case of criminal fraud, the blend of machine learning with human forensics needs to deliver.

Deploying a multi-disciplinary approach combining different technologies - both supervised and unsupervised machine learning -  would better equip merchants to deal with fraud management. Unsupervised machine learning can be used to learn on the fly and identify fraudulent patterns even without having been trained with historical data, i.e. able to identify unknown fraud attacks. 

Machine learning systems are meant to be an improvement from rule-based systems, to reduce reliance on hard rules and to filter out fraud while passing more genuine users. However, machine learning systems only provide probability scores - or fraud scores - and would still require a team of manual reviewers to make sense of the score and thereafter a decision to pass or reject a transaction.


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