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We live in an exciting time when artificial intelligence is slowly taking over our daily lives. Alexa and Siri are gradually replacing personal assistants.
We have AI-powered cameras at work, AI-powered robots that do our work, AI-powered cars, and more! So, it shouldn't be a surprise that AI and digital transformation have spread to every industry, including the digital payment industry. You can also read our article about the application of AI in different sectors by clicking here.
The growing number of digital ways to pay is pushing us toward cash alternatives. There are a lot of online transactions, so there is a greater chance of data leaks, problems with payment processing security, and fraud. This is where AI in the payment gateway sector comes into play!
Without AI, the payment industry would be unable to handle many transactions while keeping fraud and mistakes to a minimum. It is clear that the industry will depend on this cutting-edge technology in the coming years.
Why Is AI Necessary In the Payment Industry?
Statista predicted that by 2021, the value of all digital payments would reach $6,752,388 million. By 2025, this financial transaction is projected to expand by 12.24%.
The vast quantity of online payments demands an advanced fraud detection system. Furthermore, the enormous number of digital transactions has advantages and disadvantages for detecting fraud.
It's crucial for training AI algorithms and is thus made available to those who build them. The typical fraud detection system would take into account factors such as the total amount spent, the geographical location of the transaction, the identity of the business conducting the transaction, and so on. Spending outside a user's norm, in an unusual area, or with an unknown vendor will raise red flags and prompt further investigation.
However, this strategy had a flaw: it could not handle the influx of new business. In addition, it would provide false information while trying to establish whether or not an unusual occurrence is fake. The payment gateway industry cannot depend on a human inspection procedure if the expected increase in the number of digital transactions in the future years is to be believed.
Use Cases Of Ai In The Digital Payments Industry
Let's look at some use cases for how AI can be used in the digital payments industry.
Predicting Customer Credit Card Behavior
People are increasingly turning to credit cards for online purchases and because of this, having a reliable credit card scoring model to assist banks in learning about their customers' payment habits is crucial. They can then use this information to inform the development of innovative products and services in the market. Service providers can also use AI to learn about their customers' spending patterns, which can lead to the development of new offerings and the introduction of more personalized pricing structures.
Leveraging a customer's transaction history to provide personalized discounts and specials is one approach to building a robust scoring system.
Since AI-based solutions already have all the data they need, they may make way for more customized advertisements and other marketing communications.
Reducing False Debit and Credit Card Declines
When customers' credit card purchases are denied, their frustration levels inevitably rise. When credit limits are reduced, it reflects poorly on the issuing institution. A card will be refused in most cases because the payment amount is beyond the limit or the transaction has been identified as suspicious.
False card declines, which frequently can be avoided, are projected to cost businesses roughly 3% of annual revenue. The most common cause is a bank wrongly flagging a valid transaction as fraudulent.
Algorithms powered by artificial intelligence may be used to prevent this from happening by accurately identifying and flagging any genuine irregularities. This would replace the current rule-based approach, which is often too sensitive and falsely triggers alarms.
AI and Machine Learning in Fraud Detection
Using massive amounts of digital transaction data, fraud detection algorithms can identify and stop potentially fraudulent transactions. This is done in the e-commerce and digital payment industries to protect user accounts from hackers. Both supervised and unsupervised algorithms are utilized to track and analyze these massive transactions, identify any questionable behavior in user accounts, and notify the appropriate parties.
Supervised ML has its starts with "labeled" data, from which the algorithm learns to make predictions. On the other hand, unsupervised machine learning is an approach that does not rely on labeled data. When transaction data is missing or mislabeled, an unsupervised method may be used to find the outliers, which can then be utilized to spot any irregular pattern. That's why AI is useful in the financial sector; it allows for processing more transactions with fewer mistakes.
Improved Customer Service
One of the important ways AI can help payments companies and financial institutions is by improving the customer experience.
Then came chatbots, AI programs that can hold a conversation using natural language processing. When used in customer-facing settings, these programs could change many service industries by offering customized and personalized service in a way that is highly automated and easily scalable, as the best paid online survey apps to do.
An analysis by Juniper Research says that chatbots will save banks billions of dollars in operating costs and hundreds of millions of hours of work when used in customer-facing settings like customer service and dispute resolution. According to the study, the number will rise from $209 million this year to $7.3 billion in 2023, an incredible 862 million hours.
Payments technology companies and financial institutions could quickly become handy tools for helping solve chargebacks, helping their merchant customers, and speeding up the onboarding of merchants in a very cost-effective way.
To read more about natural language processing, we recommend you read this: Beginners' Guide to NLP in 2022
Use Cases of Computer Vision in Banking and Financial Institutions
Let's look at some use cases for how Computer Vision can be used in the digital payments industry.
Large-Scale Document Data Extraction
It is common practice for financial and transactional data processing to necessitate scanning related documents. Document extraction employs computer vision and natural language processing and is layered on top of other procedures. Scanning mountains of documents or converting archival paper files to a digital format may be accomplished with the use of natural language processing (NLP) methods.
You can save time and effort by using an AI-powered invoice extractor to process these documents and insert the relevant data into the right digital system. These solutions can also self-correct as they gain experience from their own errors.
For long-standing, massive organizations with data need only a computer can meet; this is a lifesaver.
Processing Claims
Ant Financial, a Chinese FinTech company, uses computer vision to find damage to cars and process claims. First, users are asked to use an app to make changes to their documents and information. Next, the system tries to figure out what the data means so that it can be checked later. After that, it decides how to process online payments.
Performing KYC Verification
Because of computer vision, the time it takes to process Know Your Customer (KYC) documents has decreased significantly. Customers only have to take photos of themselves and their ID cards. The customer would be asked to move forward or provide more details if all the information submitted checks out. This process has helped financial institutions handle KYC without making mistakes and give customers a better experience.
Bottom Line
There is a clear need for artificial intelligence and computer vision in the payment gateway sector, which handles billions of dollars worth of digital transactions daily. The digital payment industry is just one of several that is benefiting from the advancements in efficiency and effectiveness brought about by artificial intelligence.
AI has reduced the time it takes to process payments, can manage massive amounts of data, and guarantees that all necessary compliance regulations are met. Expected future increases in payment volumes can be handled with little disruption, according to the system's efficiency guarantees.
There is also less space for mistakes made by humans. As a result, digital payment technology has begun to use such tools creatively. Constant testing of algorithms helps protect businesses against fraud. The use of artificial intelligence is likely to increase in the financial sector and digital payment industry.
by Cameralyze
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