Digital payments involve a sizable amount of unstructured data, which can yield valuable insights. AI helps convert this information into knowledge which can be used to predict payment behavior while also allowing to manage risks better. Learn more about the potential of AI in the payments industry.
The digital payments industry is lucrative and expanding quickly. Mordor Intelligence estimates a 35% CAGR in AI’s share of the global retail business from 2018-26. AI provides actionable information for users to make real-time decisions in E-commerce and other commercial domains. However, the risk of fraud and other cybercrime is increasing. As AI-powered technologies are getting better at detecting payment data irregularities, they are being used by banks and payment service providers to safeguard their systems from attacks.
Predicting Payment Behavior
While AI can help financial institutions analyze data and predict future behavior, it may also aid in detecting fraud and customer behavior anomalies, providing insights into customer sentiments, and extracting information from big data.
AI technology often manages real-time risk through machine learning algorithms that provide early warning signals on potential threats and vulnerabilities. For example, an AI algorithm could be trained to monitor social media channels for mentions of a credit union’s brand name. If a large number of negative comments are posted about the credit union over time, this would indicate that there may be issues with its products or services. These can be in the form of a bug or maybe poor customer support service that needs to be addressed immediately.
Financial institutions have been exploring ways to use artificial intelligence for payment processing automation purposes. One such application involves using voice biometrics or voiceprint authentication for customers to make payments. Instead of using a physical interface, they could now enter their code word by merely reciting it. This provides greater ease of use while reducing human errors. These human errors could result from typos or misdialing phone numbers when making payments through traditional methods such as ATMs or online banking applications.
Convenience is the most demanded feature today, so it organically sells. Like home assistants, IoT will soon enable all smart devices on the network to order their repairs and maintain themselves, thanks to the omnichannel APIs. PYMNTS’ recent survey found that two-thirds of respondents demand super apps. These apps can give great insights into customer preferences and behavioral patterns that will teach AI to plug loopholes in the system to enhance CX.
AI has improved the ability to detect fraud and customer behavior anomalies, reducing the volume of alert investigations, increasing detection rates based on non-obvious patterns, and saving time and money. It does this by detecting transactions, not in line with a given pattern or “template” for an account holder or business. This may be caused by variations in transaction volumes and locations and other modifications to typical activity levels.
For example, suppose you consistently withdraw $50 from an ATM at 2 p.m., but one day, you withdraw $300 instead. Even though you used the same card, the bank may flag it as unusual for you. There is no magic formula for detection except for a well-built AI engine’s elite pattern recognition capabilities. However, the more data points (transactions) available to train AI models, the more they’ll become immaculate at recognizing false positives and negatives vs. true positives and negatives. It has been observed that 3% of companies’ annual revenues are lost due to erroneous credit card declines, primarily caused by the wrong flagging of transactions by the action of standard filters. This may lead to a potential loss of $386 billion by 2023.
Large-scale Data Extraction
AI creates the opportunity to extract big data from unstructured data and use it across various applications. This includes itemized transaction information that can be used for data analytics, payment category classification, spending behavior analysis, benchmarking, and more. By drawing conclusions based on the analysis, AI can extract knowledge from unstructured data sources.
Computer systems used to find it challenging to process high-level languages as they only understand combinations of bits. But the genius of NLP allows AI to now process invoices that are complex data structures for humans. This helps reduce processing time and manual errors and would instead help identify mismatches and inconsistencies in batch processing. Digitization of data is a move closer to sustainable development while reducing creation, preservation, and circulation costs. The security prospects in the virtual environment are way higher with controls such as authorization. Tokenization is another way of quickly processing a large volume of data without the risk of exposing it. Financial information is being masked in this way to reduce vulnerability when payment processors deal with it.
Using Historical Precedents for Managing Risk
The AI engine can recognize patterns with the ability for self-learning, improvement, and adaptation. The best way for an automated program to learn about the world around us is by observing what happened previously. To do this effectively, AI must be trained on historical data (i.e., past events), which can help make sense of how things work today—we also refer to this as, ‘learning from experience’ or ‘expert learning.’ AI uses historical precedents recorded to manage risk in real-time through anomaly detection and determine appropriate actions in response to those anomalies.
This is a brilliant feature to have in payment applications. The need for vigilance is more significant in the payments industry, as the impact of a breach is high. Recently, the rate of cyber fraud has increased due to catapulted digital adoption, but the power to identify such incidents in terms of AI has also strengthened.
Technology is a Toolkit
Technology may be used to detect and prevent fraud, but it also has a wide range of additional uses for businesses of all kinds. For example, AI can make the customer experience seamless through efficiency. Imagine you have a content creation agency that entertains requests for third-party online content. You may have customers who live in different time zones and speak other languages, all of whom want their content in English. AI-powered chatbot engines are becoming so intuitive that they can even respond in a language different from the language of inquiry. Contract negotiations and payments also become simplified with real-time AI inputs for conversions.
Apart from various innovative AI projects in the world of science, the rise of AI-based security solutions in the payments sector will help protect consumers from fraud and merchants from losses. With these solutions in place, companies can better build customer trust. Customers are additionally reassured by the knowledge that their payments are secure. Businesses can focus on serving customers without worrying about threats from cybercriminals. An investment in an AI-replete pipeline is the best investment for any digital business—payments being the frontrunner.
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