How is Artificial Intelligence Changing Mobile Payments?

Slowly and continuously, disruptive technologies such as artificial intelligence, machine learning and AR / VR are spreading their wings worldwide. With all the curtains open, Siri and Alexa successfully removed our assistants.


With so much craving for AI-powered cameras in our phones, developers have a lot of pressure to give them nothing but the best. The following post explores the many ways AI-powered digital payments can have a big impact on the online domain.

If someone were to ask me to define the term artificial intelligence briefly, I would say, “Gadgets that mimic human actions.” There are many benefits to using technology, but what comes out is understanding the environment and adapting accordingly. According to sources, the wave of AI is already making significant progress in electronics, and mobile money payment solution are no exception.

Know how?

There is no denying that a financial revolution is taking place – all thanks to transformative technologies. We live in an age where people are well aware of potential risks and breaches and demand a secure, fast and convenient payment structure.

In the current situation, more and more data are given to the machines for more accurate results. So, the stakes are too high! One wrong move can ruin everything.

More specifically, customers connect anywhere, anytime to make their lives easier. Then whether it is money transfer or bill payment, online transactions are gaining momentum like never before. By integrating machine learning and artificial intelligence, organizations can feel relaxed for mobile payment solution in many ways, such as:

  • Complete KYC (Know Your Customer) Online
  • Improved customer service
  • Changing the way people invest
  • Predicting the borrower’s guilt

Get real-time authentication of transactions

Turning the pages, the bank less population eventually became entangled in cumbersome challenges and search of infrastructure. By taking advantage of AI, we can use various potential technology applications due to its large scale and wide utility.

Unlike previous processes, KYC processes are no longer slow, complex and naturally ineffective. So far, the range of third-party data sources, including credit reports, CIBIL scores, watchlists, social media, transaction history, can be easily analysed, and the list goes on, except for the critical information provided by the government and biometrics in particular!

Use of AI in payments

Banking Chatbots – Smartphone users no longer use chatbots and text messaging services. Financial institutions looking for a better customer experience and connectivity must embrace this technology or lose a lot! One of the best examples to cite here is Bank of America Chatbot, Erica.

Here, customers can easily communicate via voice or text messages to keep an eye on their finances. Another example is PayPal, which integrates its chatbots with Facebook Messenger. In turn, this allows users to make payments to the app. Everything happens without much trouble.

Predictive Analytics and Machine Learning

AI can help companies identify patterns in data to prepare e-commerce for individuals in mobile payment apps. If we take a closer look at the inferential analysis process, it can quickly and efficiently detect large amounts of data. More and more companies are using big data to understand their consumers’ cost patterns, and what they find is critical information relevant to their end-users promptly.

This can lead to more engagement and better business planning. By incorporating both artificial intelligence and inferential analysis, a person can gain more functional insights than ever before. For example, Capital One can create new products and deals for customers based on their spending behaviour.

Fraud detection

One of the best ways AI technologies affect custom software development and improve end-user interaction is through transaction filtering to recall high-risk transactions with only a security chargeback level.

As a result, it avoids good customers taking advantage of real-time features such as geolocation, behavioural analysis, and physical biometric keys to return to an abandoned cart or make frequent, less risky transactions.

Bot technology

Customers are accustomed to the convenience of buying and paying wherever and whenever they want. Smart merchants are taking advantage of this choice by offering streamlined omnichannel payments. Thanks to machine learning and AI, this has paid off in channels not required for commerce.

For example, merchants like Starbucks allow customers to send gifts with iMessage by combining remote payment processing and gift card activation technology. The mega coffee retailer has also implemented a voice-activated chatbot – My Starbucks Barista, allowing customers to make payments using voice-activated orders.

P2P money transfer

Financial institutions have also pulled out a page from a conversation book and used AI-assisted bot technology to facilitate money transfer requests through Digital payments mobile app. In 2017, Western Union introduced a money transfer boat in the Philippines, which allowed users to request money from people working in the US or living abroad.

The technology uses Facebook’s Messenger app, where requests can be sent via chatbot and handled by the sender via a mobile application by clicking on a message.

Fraud prevention and streamlined payments

Machine learning is integral to the highly effective and efficient way to prevent fraud, essential for the Mobile payment systems. While rule-based systems have long been the norm for traders seeking to prevent fraud, machine learning carries the torch in a more sophisticated way.

Machine learning greatly reduces the likelihood of errors when detecting fraud, processing large amounts of transaction data, and subtly and repeatedly modifying transaction filtering rules to detect actual fraud. By simply identifying high-risk transactions and applying an extra level of security, machine learning can improve the end-user experience with mobile payments without sacrificing security.

Machine learning also offers a way to reduce false positives. This problem has been a thorn in the side of traders for years – and is costly. In 2019, false losses cost traders 20.3 billion. Falsely denied, fraudulent transactions are often more costly to the seller than fraudulent losses.

AI is poised to revolutionize the mobile payment landscape

While AI and machine learning technology have come a long way, their applications for the mobile payment industry are still in their infancy. We will continue to see improvements in both the short- and long-term user experience and the prevention of mobile payment fraud and generate revenue with a mobile money solution.

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