One of the most discussed technologies in the recent years has been the rapid development of Artificial Intelligence (AI). It is already making a huge impact in finance and banking sector- this includes the payments industry as well. It can safely be said that it is capturing the interest of companies worldwide.
For e.g., Tesla, Facebook, Amazon, Apple and Google, all have already started investing actively in AI technology. AI covers a wide spectrum of technologies that work in payment services.
“Artificial intelligence is likely to transform many industries in the next decade, including payments,” predicts Michelle Evans, Global Head of Digital Consumer Research at Euromonitor International. So, let’s see how if AI is making his predictions come true.
Machine learning (ML) is especially important now than ever, in fraud detection as there is a significant shift to e-commerce and other remote “cards, not present” transactions where fast approval is needed- yet the customer’s physical card is not available for additional verification.
Studies point out that such remote channels are primarily responsible for increased levels of fraud at US merchants – especially amongst larger companies.
Plus, due to the significant change in immediate payment systems (which correspond to requiring faster identification of potentially fraudulent transactions), the importance of fraud detection is driven up. Using the Machine learning’s algorithm to derive insights into which correlation of variable lead to a fraudulent payment.
The systems use rule-based logic to block the transactions that have the highest probability of being a fraud. Albeit, professional con-men are fast at detecting when their payments are being blocked and switch to a new merchant to continue their scheme. Combining the forces of ML and AI, historical data can be used to identify patterns associated with fraud and make quick adjustments to their algorithm’s and rule-based logic without human interference.
Canadian travel agency Redtag.ca uses Distil Networks which weeds out the bad bots that are fraudulently reserving a block of seats on a flight to cause the price of the remaining seats to rise dramatically.
Another example is that of the 3-D Secure (newest version) which is playing a key role in new e-commerce security product powered by AI. It is an additional security layer within its e-commerce fraud prevention platform which uses ML and AI to flag down the riskiest transactions that should be challenged in order to protect merchants from chargebacks and fraud.
Chatbots are the perhaps one of the, if not the most famous application in financial services. Rather than just a method of maximizing customer engagement- many think that payment are the only thing needed for chatbots to deliver on their promise.
One of them is the Kik CEO Ted Livingston, who told TechCrunch in an interview that- “Right now, it is in a holding pattern until we get payments [but] we’ve seen so many killer examples of unlocking the world around you that we know there is magic there.”
Companies like American Express have deployed chatbots on Facebook Messenger that take care of customer service as well as payments. Another example is that of Amex which introduced chatbot featuring transaction notifications and benefit reminders. The second version of the bot allows users to add a card when they link their Amex cards to their Facebook Messenger account.
Optimization of payment routes and fielding higher success rates
ML and AI can be used to optimize payments by dealing with one of the biggest issues regarding processing cards- i.e. authorization rates.
Notwithstanding the rules and frequent compliance releases from schemes like Mastercard and Visa- getting a payment authorized – is more difficult than it seems. The merchants send their transactions to PSPs, who send it to the Acquirers, who create the complete message to send it to the Schemes and then they pass it the Issuer.
There is 1 in 5 chances that the payment gets refused. It can show anything from ‘expired’ to “not honored’ as a reason for the decline.
The situation remains that by using ML and AI, Merchants/ PSPs are able to learn from the submitted payments and estimate the success rate depending upon the category of card type, transaction, issuers, merchant’s country and other parameters. Therefore, by optimizing the route, (i.e. the transaction to the acquirer with the highest probability of success)- merchants/PSP can increase their authorization rate.
3-D Secure 2.0 improves the authentication possibilities that enable merchants to move closer to frictionless checkout experiences for their customers- with improved security and new intelligent risk- based possibilities.
Another area of payment services where AI is utilized, is for marketing- which gets quite an attention but is not as developed as one might think. Many payment service providers have used AI to make predictions grounded on customer’s behavior- using them to push certain products/ services that will help customers manage their finances.
CrossCues uses machine learning and AI to enable their clients to understand, anticipate and engage their customers. Equipped with data, they use their neural network (which is a computer system modeled off the human brain) which “makes a psychographic profile of a customer, advising the financial institution about what a specific customer or demographic likes and dislikes”.
Conclusion: Our View
While there’s still a long way to go before we let AI-based tools and algorithms control our financial systems, the fintech sector is starting to use the technologies in diverse and creative ways. The crossover between AI and fintech comes at a stage where AI is creating disruptive technology for the consumer. Payments and digital transactions are key areas for successful integration of AI services to improve customer experience. Fortunately for customers, major industry players in the payment industry like PayPal, Amazon, MasterCard and Google have their eye on AI- their involvement in the revolutionary change may have a positive butterfly effect throughout the industry.