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Shelly used her credit card on Amazon and got declined. Shelly uses her other card. This time the transaction is approved. She immediately called the bank that declined the transaction and was told the transaction was flagged as fraud. Shelly finished the call feeling frustrated and generally unhappy with the response.
What Shelly doesn’t know is that the decline is the result of the issuer bank’s attempt to protect her and her money. Issuer banks and merchants have these safeguards in place to stop fraudulent transactions and approve all legitimate transactions. However, with the limited data shared between merchants and banks today sometimes these split-second decisions result in false positives. But Shelly took that as a personal insult.
Shelly’s story is one of many. Consumers worldwide are frustrated by their inability to buy what they want, when they want, due to the increasing rates of false positives. The ripple effect of this frustrating experience is massive.
So massive in fact that Fiserv, a provider of financial services technology, revealed that the average monthly amount spent on a card after two or more false-positive denials is decreased by 15% on average in the 6 months after the false denials.
Until today the current tools and payment model algorithms available to banks couldn’t provide an alternative to Shelly’s experience without taking on additional risk. To change this reality, banks must overcome one challenge.
Every time Shelly and other cardholders “swipe their card,” it triggers the usual payment model authorization process that analyses the chances of the transaction being fraudulent.
Based on Kipp’s clients’ data, 36% of declines are related to fraud prevention. More than 80% of cardholders who experienced false decline said it was outright embarrassing. According to Sapio Research, 33% of falsely-declined new shoppers give up on the transaction and retailer entirely and never try again.
However, there are (at least) two sides to each transaction: the merchant and the card issuer. Today’s risk analyses are siloed. The moment the payment authorization is triggered merchants run in-depth analysis of the transaction and consumer behavior, data, and device interaction (such as device ID, etc) to check the likelihood of fraud. Next, issuer banks run their own fraud analysis based on their internal data. But instead of sharing relevant real-time data, banks are left to rely on internal data and profiles that don’t always accurately portray the risk assessment of a particular transaction.
While this automatic process takes seconds, it doesn’t account for richer data elements and communication with the merchants. The bank’s clients are the merchants’ clients too. Merchants can provide additional data that can enrich the bank’s payment model to make smarter, more accurate decisions; Decisions that keep their cardholders satisfied, happy and loyal.
Real-time communication and data sharing between card issuers and merchants are the keys to approving more legitimate transactions. The way we see it, sharing specific data elements between the parties associated with every transaction is the most accurate way to combat the rising wave of the false-positive.
Kipp’s AI-based solution relies on the shared interest of merchants and issuer banks to approve more payments. Kipp leverages the merchants’ willingness to authorize the transaction by sharing rich data and the cost of risk with the issuer in real-time.
Here’s how it works.
Make card holders like Shelly feel confident to rely on their credit card for their shipping needs without worrying so much about false declines. Kipp’s solution is the response to the rising trend of false positives that result in abandoning credit cards and opting for other payment solutions.
If you want happier, satisfied, and loyal cardholders Kipp can help you provide the shopping experience they expect and deserve. Want to learn more about this innovative approach to fraud assessment? Contact us.