Use of AI in payments industry is set to explode
Payment processors are making wider use of AI technologies as part of an effort to make better use of their vast troves of data and connect more directly with customers.
Artificial intelligence has long been used in banking and credit card systems to detect and spot fraudulent activity. Now, AI is making its way into other areas of the payments and financial services industries. AI in payments industry is helping enhance customer service, provide hyperpersonalized credit scores and offers, and drive new forms of transactions like stores with no cashiers.
AI enhancing fraud detection
About two decades ago, e-commerce revolutionized the way goods are bought and sold online. No longer are people bound by borders or time zones. Goods can now be purchased from anywhere around the world at any time of day. Because of this, traditional rules-based fraud detection systems quickly became outdated and no longer work. We've moved to a world where real-time payments require real-time fraud detection.
With so many transactions being done electronically, it's nearly impossible to have humans alone monitor these transactions and keep fraud and error rates down to acceptable levels. According to a report by Capgemini, global digital payment transactions are projected to reach approximately 726 billion by 2020. Machine learning pattern matching systems can intelligently monitor and analyze these very large amounts of data in real-time to look for unexpected commonalities between fraudulent and non-fraudulent transactions.
Additionally, AI systems can send alerts to individuals if they spot activity that doesn't appear normal for that user. For example, if a recurring monthly bill is higher than usual, or a tip left at a restaurant exceeds the norm, the system can immediately alert the customer. The system is able to learn a user's normal behavior over time and spot outliers to this behavior.
Companies are seeing value in bringing AI into other areas of their business as well. AI-based chatbots are increasingly being adopted by payment and finance firms to interact with customers, answer questions, help navigate users through the company website or app and help make online purchases. Capital One, for example, has developed chatbot Eno to allow customers to conduct basic account inquiries, check account balances and transfer funds between accounts.
Ingenico Group, a smart payment technology provider, partnered with IBM Watson to create an AI-enabled messaging chatbot to help merchants in the retail, hotel and hospitality industries conduct payment transactions with customers. Using natural language processing, the chatbot is able to interact with customers in a variety of languages to find out what the customer is interested in, such as a coffee or meal from the hotel restaurant, and allows customers to make payments through the app's secure payment API. The AI-powered bot is able to more personally interact with each user and provide more relevant recommendations and is showing five times better conversion rates than traditional mobile apps.
AI is also offering lenders the ability to get a hyperpersonalized look into someone's creditworthiness and score individuals more accurately. In the United States, the FICO score has long been used by banks and lenders as the traditional standard to determine an individual's credit worthiness, but this score has inherent flaws and can inaccurately group people into buckets or rank someone poorly who might actually be worthy of a loan, such as young people and new credit seekers. Machine learning algorithms are allowing lenders to look at a variety of data sources to create personalized profiles that allow for more accurate predictions of credit risk to approve loans to people who were previously deemed uncreditworthy and also offer better pricing to people who deserve it.
To add to this level of individualized service, companies are taking things one step further using AI to analyze a consumer's spending record and find less costly options or recommend the elimination of certain products. AI can intelligently monitor your spending and make suggestions on how to help increase savings, cut back spending and find alternative products.
Computer vision-powered retail and payment transactions
A recent concept to the world of retail and payments is the idea of an AI-powered store where recognition sensors throughout the store can detect what people are picking up and charge them for the items without having to go through the traditional checkout process. Companies such as Amazon are pioneering this concept. No longer do customers need to wait in long lines to check out.
Similarly, we're starting to see increased use of computer vision in payment process itself. Companies are experimenting with the use of facial recognition systems as a means to authorize payments, and even using them as a replacement for credit cards and other payment systems. One advantage of facial recognition, gesture detection and voice recognition systems is that they are very hard to fool. Perhaps in the future, we'll simply use our face and voice as a way to authorize payment transactions and transfers and skip the use of cash or credit.
Many companies are headed toward bringing a hyperpersonalized approach to payments and lending. It allows for a better overall view of their customers, provides tailored offerings and learns normal spending behavior for each person. However, there are also ethical concerns to having AI systems automatically make loan decisions and extend credit, or not extend credit, when it should have. These systems will need to be able to explain how they came to certain decision.
For companies in highly regulated industries that require explanation, keeping a human in the loop is recommended. AI systems can be powerful at helping companies spot fraudulent transactions in real time, allow customers to purchase items on demand and increase customer service. Just make sure your system is properly trained and can explain itself when needed because the benefits to AI in payments can be vast.
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