How Artificial Intelligence Is Changing the Digital Payments Industry

In this blog post, we will examine how artificial intelligence (AI) is gaining traction in the Digital Payments business in a wide range of operations, such as forecasting customer behavior and detecting fraudulent activity.

How Artificial Intelligence Is Changing the Digital Payments Industry

Data and analytics are rapidly becoming important aspects that are necessary for the expansion of every industry. For instance, banks and other financial institutions have begun to utilize data in order to provide their consumers with banking services that are superior, quicker, more convenient, and more intelligent.

The COVID-19 problem has had a huge impact on the worldwide payments sector, which has led to greater modernization of banking institutions as a direct result of these developments. This has also led to an increase in the number of transactions that are taking place two to three years in the future. After the United States (25.5 billion online transactions) and China (also 25.5 billion online transactions), India had the highest amount of digital and online transactions totaling over 25.5 billion in the year 2020. (15.7 billion online transactions). In addition, businesses were given the opportunity to scale themselves online as a result of lockdowns that were imposed, which led to an increase in the number of customers in the digital arena.

According to a survey conducted by McKinsey & Company, 82 percent of American consumers make use of digital payment methods, and consumers have spent approximately $871.103 billion on transactions completed online for items and services offered by US merchants. The success of the digital economy is contingent on how quickly the BFSI industry adapts to the shift in consumer behavior and makes use of technologies such as machine learning (ML), artificial intelligence (AI), internet of things (IoT), and blockchain in order to transform, make innovations, and change their business model.

Let’s have a look at some examples of how artificial intelligence can be put to use in the digital payments business.

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Use Case Number One: Anticipating How Customers Will Use Their Credit Cards

  • Because more and more people are using credit cards, it is imperative that a reliable scoring system for credit cards be developed. The insights that may be gained from this model will assist financial institutions in better comprehending the payment preferences and spending patterns of customers, which can lead to the development of transaction-driven marketing solutions.
  • It is essential for banks, fintech companies, and credit card companies to continue to maintain behavioral scoring based on the information obtained from the transaction history of their consumers.
  • Businesses such as Cardlytics, which is a vendor of AI, have developed a card-linked marketing software that assists businesses in analyzing the purchasing behavior of customers and helping them match with the deals on which they are most likely to spend their money.
  • Cardlytics makes use of the purchase insights provided by its customers in order to identify chances for marketing and ad targeting.

Use Case Number Two: Eliminating Incorrect Declines on Credit and Debit Cards

Customers may get dissatisfied and angry if their card transactions are refused while they are checking out, which can lead banks and other financial institutions to lose their brand reputation and customers’ trust. The most common reasons for a card to be denied are when the transaction payment amount is beyond the limit or when the transaction is suspected of being fraudulent. It is estimated that businesses lose somewhere in the neighborhood of 3 percent of their annual revenue owing to erroneous card rejects. This occurs when a valid transaction is labeled as fraud when it should not be. Instead of a rule-based, algorithmic technique that has a tendency to reject a transaction that is not fraudulent, AI-based algorithms are utilized. These algorithms are used to appropriately identify transaction abnormalities.

Use Case No. 3: Artificial Intelligence and Machine Learning in the Detection of Fraud

Using enormous volumes of digital transaction data, fraud detection algorithms can identify potentially fraudulent transactions and block them before they occur. This is done in the digital payment transactions sector and the e-commerce sector to protect other customers’ accounts from being hacked by hackers who are attempting to access their accounts. In order to monitor and analyze these huge transactions, check for suspicious activities in user accounts, and alert individuals, both supervised and unsupervised algorithms are used.

Supervised machine learning requires training with data that has been ‘labeled,’ and the algorithm makes predictions about the output based on the dataset. On the other hand, unsupervised machine learning refers to the use of algorithms that learn from data that is not labelled. The unsupervised approach is utilized in situations in which transaction data is either absent or incorrectly categorized. This algorithm assists in the discovery of outliers, which assists in the detection of any uncommon pattern. Therefore, AI makes it possible for the payment sector to process enormous numbers of transactions with a significantly reduced mistake rate.

Examples of Where Computer Vision Is Being Put to Use in Banking and Other Financial Institutions

  • Document Extraction: The Document Extraction process makes use of computer vision and natural language processing (NLP) tools. This process is connected with other processes already in place. This is automated to help reduce the amount of manual effort required and to make the overall process of document extraction more efficient. It is used for digitizing information that was previously stored on paper. Read unstructured documents and automate document extraction with the help of tools like Google’s Vision API, Amazon Rekognition, and Azure Computer Vision. These are just some of the tools available.
  • Claims Processing: Ant Financial, a Chinese fintech business, uses machine vision to identify motor vehicle damage and facilitate claims processing. To begin, it is requested of the users that they use an application to bring all of their documents and information up to date. After that, the system attempts to decipher the significance of the information for the purpose of performing additional verification, and then it makes decisions regarding the processing of online payments.
  • The amount of time needed to process Know Your Customer (KYC) documents has greatly decreased as a direct result of the implementation of computer vision technology. Only the customer’s face and an image of their identification card are necessary to be photographed. If all of the information that was provided can be confirmed, the consumers will either be given the option to proceed with the transaction or they will be forced to supply extra information about themselves. Because of this approach, the financial institutions have been able to process KYCs with fewer errors and provide a higher quality experience for their customers.

As a part of their overall innovation strategy, financial institutions and banking companies are making concerted efforts to discover new applications for artificial intelligence (AI) in digital transactions. When applied to the digital payments industry, Artificial Intelligence will help improve efficiency, reduce costs, enhance customer experience, and meet the growing demands of the digital fintech industry. This will allow digital payment companies to maintain a competitive advantage in an online market that is continually becoming more cutthroat.

Artificial Intelligence Driving Digital Payments

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