We examined generative artificial intelligence (AI) in January 2024 and how it has entered the fintech space after its 2023 surge. Artificial intelligence has a wide range of applications, and while there are beneficial uses for it, bad actors can also take advantage of it. Therefore, we decided to look into how the widely used technology will affect the cross-border payments industry, with a focus on security.

AI does not prevent international payments.

The card issuer and payment processor i2c Inc.'s chief client officer, Serena Smith, says that in 2024, AI won't be the primary obstacle to the rise of cross-border payments. It will be crucial to guaranteeing the security of the new payment rails.

The field of cross-border payments will see a significant increase in security thanks in large part to artificial intelligence (AI). It will be crucial for real-time transaction monitoring, risk prediction, and fraud detection.

Nevertheless, a number of obstacles stand in the way of the expansion of cross-border payments, such as complex legislative frameworks, expensive transaction costs, hold-ups in the transfer procedures, and regional differences in technology infrastructures. In order to maintain the growth and modernization of cross-border payment systems, these problems must be resolved.

A proactive approach to combating fraud

The consultancy firm Guidehouse's Jonathan Shiery, a partner in payments modernization and digital assets, highlights how AI can be used to prevent criminality in real time. As a result, businesses can adopt a proactive rather than a reactive strategy.

Through behaviour prediction, AI will be able to enhance security protocols by spotting unusual activity that might be fraudulent and alerting companies when necessary. Furthermore, AI and machine learning will enable firms to be better prepared and more proactive in combating fraud by being able to recognise potentially fraudulent behaviours in real-time and continuously learn from historical transaction data.

Similar opinions were expressed by Eyal Moldovan, co-founder and CEO of 40seas, a platform for cross-border trade financing. He said: "From a security perspective, AI can be used to detect fraudulent transactions more effectively." Artificial intelligence (AI)-driven algorithms, for example, are capable of real-time analysis of large datasets, spotting odd trends and abnormalities that help stop fraud and improve the security of global financial transactions.

Made feasible by finding a needle in a haystack

According to Clark Frogley, head of financial crime solutions at Quantexa, a big data and enterprise intelligence supplier, artificial intelligence (AI) has the capacity to investigate complicated data points that people are unable to examine adequately within a given time confinement.

Finding the proverbial needle among numerous haystacks dispersed over the world is akin to attempting to apprehend criminals who use cross-border payments to launder or conceal money. Data trails and financial imprints do persist, despite the fact that criminals aim to conceal their identities and their networks. But it's practically hard to do without AI and machine learning.

Made feasible by finding a needle in a haystack
According to Clark Frogley, head of financial crime solutions at Quantexa, a big data and enterprise intelligence supplier, artificial intelligence (AI) has the capacity to investigate complicated data points that people are unable to examine adequately within a given time confinement.

Finding the proverbial needle among numerous haystacks dispersed over the world is akin to attempting to apprehend criminals who use cross-border payments to launder or conceal money. Data trails and financial imprints do persist, despite the fact that criminals aim to conceal their identities and their networks. But it's practically hard to do without AI and machine learning.
With the use of artificial intelligence (AI), investigators can make connections inside a complex network of disparate data points spanning several accounts and institutions. Big data, network analytics, and AI combined can sort through the transactions to uncover the obscure, hard-to-find connections between people, businesses, and their counterparties, as well as between transactions and addresses throughout the supply chain.

The victim and the numerous professional facilitators who support and/or profit from this activity are connected, as well as the criminal's network, are all intricately depicted by context-driven AI technology and analytics through the resolution of seemingly unrelated things.

The payments provider considers the wider picture and how AI might be abused in the wrong hands, as AI is a two-sided coin: The finance business benefits from AI's advancements, but there are also chances for fraud. Artificial intelligence (AI) tools, like ChatGPT, are being used against fintech companies and their clients by fabricating customer profiles and using dishonest methods to obtain private information from clients and banking staff.

Traditional reactive techniques to fraud prevention are no longer enough as real-time connection increases transaction volumes and gives fraudsters more opportunities. Rather, it is necessary for banks and fintechs to take preventative and proactive steps. The likelihood of breaking compliance requirements increases as more transactions move across international borders. International transactions, in contrast to domestic ones, have higher work to do on counterparty and foreign exchange risk, as well as Know Your Customer (KYC), Anti-Money Laundering (AML), and sanctions screening.

"The use of AI and machine learning tools to automate and expedite labor-intensive and slow fraud prevention and AML/KYC verification processes is not surprising. These techniques also help to more rapidly discover infractions to lower the risk of noncompliance.

"The amazing thing about AI is that it's always interested. The efficiency of these processes is increased by its capacity to quickly discover infractions by conducting quick customer ID verification and speed-reading large volumes of data in milliseconds. Its ability to quickly absorb vast amounts of data, standardise it, classify it, and index it makes data analysis for organisations simpler.

"Predictive AI algorithms enable fintechs to transcend inflexible rules-based systems by rapidly identifying suspicious fraud tendencies. This helps them to find and stop problematic actors before serious harm is done.