The use of artificial ielligence in Stripe to enhance the accuracy of fraud detection and improve paymes is a valuable example of the practical applications of artificial ielligence in the payme industry. This experience shows how artificial ielligence can play a vital role in preveing financial problems, where it causes the most harm to people.
STRIPE, one of the largest online payme service providers, which holds customers such as Openai, Amazon, Google and Apple, at the conference Stripe sessions That was held last moh from a new artificial ielligence model called Paymes Foundation Model Unveiled. The model was iroduced by STRIPE as the world’s first fundameal model for paymes and aims to improve fraud and enhance security in payme operations.
Ideify credit card fraud with the new model
This model was one of the complex challenges in the payme industry, namely Credit Card Test Manage better than previous methods. Credit Card Test refers to a situation where cyber attackers are trying to test the stolen cards to confirm their credit for illegal purchases.
But how does this model work?
Transformer model in the service of the payme industry
Gatam KodiaIn a post in LinkedIn, the applied machine learning departme manager at LinkedIn explained that the previous standard machine learning models at STRPE had been able to reduce the fraud. However, these models needed dedicated training for various tasks such as payme approval, fraud detection, dispute resolution, and the like.
“Given the power of transformer -based public architectures, we thought about whether a similar approach could be used here,” Kodia explained. At first glance, it was not clear that this method would work, because paymes are only similar in some respects. “
Stripe developed a fundameal model for paymes as a result of this idea; A well -trained model and process the data of each transaction as dense and multipurpose vectors similar to language models. This model based on tens of billions of trained transactions and key signals of each payme in the form of one The unit vector Summary.
“This method leads to the creation of a multidimensional vector space, which represes the position of each transaction in this space indicating rich data and relationships between differe elemes,” Codia explained.
He added: Transactions with common characteristics are naturally grouped in this vector space. For example, transactions issued by a particular bank are close to each other, and paymes that use a similar email address are almost indivisible.
These groupings allowed Stripe to ideify and ideify sophisticated patterns in suspicious transactions and More precise classification models Build on the features of each payme and its relationship with other paymes.
Dramatically increase the accuracy of fraud detection
In the past two years, Stripe has been able to reduce credit card tests for its users by up to 5 %. But more sophisticated attacks, hiding new patterns of attacks in the high volume of large corporate transactions, remains a serious challenge.
“We have developed a classification model that receives a set of vectors from the fundameal model and predicts whether the transaction volume is under attack,” Codia explained. This model A mome It operates to stop the attacks before the businesses are harmed.
The result of this approach is to increase the rate of detection rates of credit card tests from 2 % to 2 % Was only in one night.
Codia we on to poi out that the success of STRipe shows payme transactions may have meaning Semaic Be. Just like words in one seence, transactions have complex dependencies and hidden ieractions that are not ideifiable by manual characteristics.
Recovery of $ 2 billion in rejected wrong transactions
Another notable achieveme of STRPE in Year 2 was the use of artificial ielligence to recover transactions that were mistakenly rejected by card exporters. This effort made more than $ 5 billion Recover for Stripe users.
Product Adaptive AcceptanceWhich uses artificial ielligence to ideify rejected wrong transactions, has played a key role in this success. Stripe has announced that the system could ideify specific patterns in transaction data that indicate the mistake of a valid payme.
Earlier, the Stripe used the Gradeian Reinforced Tree Model, but then migrated to a deep neural network based on the Tabransformer, called Tabransformer+. This system performs better in modeling complex ieractions among hundreds of factors affecting the success of transactions.
With these changes, the accuracy of the new model in ideifying valid transactions has been rejected to 5 % increase Found. These advances have helped Stripe recover more for their users, while at the same time the number of re -efforts for transactions Reduce 2 %.
Radar’s 2 % decrease in cheating
In addition, the Stripe cheat preveion tool, called RadarIt has been updated with new capabilities, including automatic autheication. This tool can now activate the two -step autheication flow for transactions and preve cheating attacks. Early users of this utility tool reduced 5 % Cheating has been eligible for transactions.
Artificial Ielligence, Future Paymes Processing
The success of STRPE in the use of artificial ielligence shows the increasing importance of this technology in the payme industry. Other companies, such as Razorpay, also use artificial ielligence to solve problems such as delaying customer payme, simplifying payme settings, and reducing problems related to return on goods.
Overall, using advanced models of artificial ielligence has not only increased paymes’ security, but it has been able to significaly improve its user experience. This approach can be a model for other companies active in this field.
