How AI is Revolutionizing the Digital Payments Industry

Last Updated on March 2, 2022

Data and analytics are becoming crucial factors that enable every industry’s growth. For example, banks and financial institutions have used data to give their customers better, faster, more convenient, and more intelligent banking services.

The COVID-19 crisis has had a significant impact on the global payments industry, which has resulted in the further modernization of banks. This has also resulted in a surge in the volume of transactions ahead by two to three years. India had the highest number of digital online transactions of over 25.5 billion in 2020, followed by the US (25.5 billion online transactions) and China (15.7 billion online transactions). Moreover, with imposed lockdowns, businesses got a chance to scale themselves digitally, resulting in consumers increasing in digital space.

A study by McKinsey & Company shows that 82% of Americans use digital payments, and consumers have spent around $871.103 billion on transactions online on US merchant products and services. The success of the digital economy depends on how soon the BFSI industry embraces this change in consumer behavior and takes advantage of technologies like Machine Learning (ML), Artificial Intelligence (AI), IoT, and blockchain to transform and make innovations and change their business model.

This blog will discuss how AI is gaining momentum in the Digital Payments industry in a wide range of processes, including fraud detection and predicting customer behavior. Let’s look at some of the use cases on how AI can be used in the digital payments industry.

Use Case #1: Predicting Customer Credit Card Behaviour

Developing an effective credit card scoring model is necessary due to the rise in the number of consumers using credit cards. The insights derived from this model will help banks understand consumer payment preferences and spending habits, which can create a transaction-driven marketing solution.
Maintaining behavioral scoring based on the data gathered from the customers’ transaction history by the banks/fintech companies/credit card companies is important.
Companies such as Cardlytics, an AI vendor, have designed a card-linked marketing software that helps companies analyze the purchase behavior and help match with the deals on which they would likely spend their money.
Cardlytics uses customers purchasing insights for recognizing opportunities for marketing and targeting ads.

Use Case #2: Reducing False Debit and Credit Card Declines

Card transactions being declined during checkout can be frustrating for customers, leading to banks and financial institutions losing their brand reputation and trust. Cards are mostly declined when a transaction payment amount crosses the limit or when a transaction is flagged as fraud. Companies are estimated to lose around 3% of their revenue every year due to false card declines, i.e., when a legitimate transaction is flagged as fraud. AI-based algorithms are used to correctly identify transaction anomalies rather than a rule-based, algorithmic technique that tends to reject a non-fraudulent transaction.

Use Case #3: AI and Machine Learning in Fraud Detection

Fraudulent transactions are detected and prevented by fraud detection algorithms using large volumes of digital transaction data. This is done in digital payment transactions and the e-commerce sector to prevent hackers who can potentially hack other customers’ accounts. Both supervised and unsupervised algorithms are used to monitor and analyze these large transactions, look for suspicious activities in user accounts, and send alerts to individuals.

Supervised ML is trained in ‘labeled’ data, and based on the dataset, the algorithm predicts the output. In contrast, unsupervised ML is an algorithm that learns from untagged data. The unsupervised algorithm is used when transaction data is non-existent or improperly tagged and helps discover the outliers, which helps detect any unusual pattern. Thus, AI helps the payment industry to be able to process large numbers of transactions with low error rates.

Use Cases of Computer Vision in Banking and Financial Institutions

Document Extraction: Computer vision and NLP techniques are used for the Document Extraction process, which is integrated with existing processes. This is used for digitizing paper-based information and is automated to help reduce manual work and make the entire document extraction process more efficient. Google’s Vision API, Amazon Rekognition, and Azure Computer Vision are some of the tools used to read unstructured documents and automate document extraction.
Claims Processing: A Chinese Fintech company, Ant Financial, uses computer vision to recognize motor damage and enable claim processing. First, the users are asked to update their documents and information through an app. Next, the system tries to recognize the meaning of the information for subsequent verification and then makes decisions about online payment processing.
Processing KYC Verification: Thanks to computer vision, the time required to process KYC documents has significantly decreased. Customers are only required to take pictures of their faces and ID cards. If all the details submitted are verified, the customers will be asked to proceed or be required to provide additional information about themselves. This process has helped the financial institutions process error-free KYCs and a better customer experience.

Financial institutions and banking firms are actively trying to unlock ways to deploy AI in digital transactions as a part of their innovation strategy. AI in the digital payments sector will help improve efficiency, reduce the cost involved, enhance customer experience, and meet the growing demands of the digital fintech industry, which will help them stay ahead in the ever-growing competitive digital market.

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