The important of AI in merchant account payment gateways in High Risk Industries

Artificial Intelligence (AI) is no longer the future, but it’s already the present. For eg: AI is the technology that powers the verbal commands you give Siri. It operates driverless cars and can beat you in online games. It may sound intrusive, or even creepy, but companies are investing in AI to learn your behaviors – and monetize those behaviors.

Picture customers shopping at your business for products they need the following week — but they just don’t know it. Using artificial intelligence (AI), computers can recognize buying patterns and anticipate a customer’s needs purchasing goods and services based on those needs.

Every year, citizens of the European Union make 122 billion digital payments using payment cards, e-wallets, bank-transfer apps, mobile wallets, and other payment methods. Without AI, there is no chance the payments industry could process so many transactions so quickly and keep fraud and error rates down to an acceptable minimum.

Artificial Intelligence (AI) is here for quite some time now and is successfully being used in banking applications like Fraud Analysis and Customer Risk Scoring but with a limited scope. The prominence of AI in decision making has effected the advent of data explosion, big data analysis, and internet penetration.

How AI is Powering Payments

It is predicted that AI would move into payments. It has the potential to streamline the payment chain, reduce fraud, improve customer service and optimize risk assessment—all reasons venture financing is flowing into AI.

The massive volume of digital payments is both a boon and a hindrance to spotting fraud. On the one side, it strains existing fraud-detection systems to the limits. On the other hand, it supports developers of artificial intelligence with the data they need to train their algorithms.

There are many types of AI, but for payments – particularly the high-risk merchant processing industry – the most important types include Machine Learning, Natural Language Processing, Verbal and Vision Recognition, and AI bots.

Machine Learning: a process where a software model is constructed and vast volumes of data are compared against it. Learning algorithms allows the model to evolve in response to changes in the incoming data. Machine Learning is crucial to the high-risk processing industry’s ability to detect and respond to fraud.

Machine learning is a subset of AI. All machine learning counts as AI, but not all AI counts as machine learning sounds unfamiliar. For eg, symbolic logic – rules engines, expert systems and knowledge graphs – could all be described as AI, and none of them are machine learning.

Natural Language Processing (NLP): allows a machine to understand spoken commands or questions. Among the most well-known applications using NLP are Apple’s Siri and Google Translate. In the processing space, NLP is being used to reduce customer service costs by taking customer questions without human interaction. 

Natural Language Processing(NLP) is a branch of artificial intelligence that helps with the interaction between computers and humans using the natural language. The main objective of NLP is to read, decipher, understand, and make sense of the human languages in a manner that is valuable.

Visual Recognition (VR): allows the software to identify objects. In the payments industry, this is particularly applicable to fingerprint or facial recognition as second-level authentication methods. This technology enables customers to easily make mobile payments and provides security from fraud at the point of sale. In the world of high-risk processing, VR is important because it’s authentication capabilities reduce chargeback rates for companies targeted by fraudsters.

AI Bots: apps that represent the appearance of being human, while being completely automated. They combine NLP and Machine Learning code to present effective interfaces that can handle thousands of interactions at the same time, rather than the one at a time limitations of human customer service reps. AI bots are used in the chat software, messaging apps, and virtual assistants, and allow customers to make direct payments without leaving their preferred social media platform, or via text messaging.

In the high-risk space, AI bots can replace human service reps in handling customer questions and complaints. They completely speed up the service process and create significant savings. Just imagine if you could enable 24/7 availability, across channels, cost-effectively, and still provide consistent, compliant responses to all customer queries. 

Key factors to be noted:

Fraud Prevention:

With new payment methods like card-not-present (CNP) transactions come new opportunities for fraud. Artificial intelligence and machine learning are at the forefront of not just detecting fraud, but preventing it before it happens. These technologies can already uncover patterns and drive hidden insights, but the technology is moving towards refining these insights more further.
Currently,  AI and machine learning are arguably having their most significant impact in the fight against fraud, AI is increasingly being used to robotize instant fraud detection, with especially promising prospects for mobile payments and other higher-risk transactions, which are more vulnerable to fraud than, say, EMV-enabled POS devices.

Fraud detection is the most common use for AI in finance but it’s not the only one option. The same faculty for finding patterns and defining new variables can also be used to spot potentially useful or concerning connections and behavior as part of the know-your-customer (KYC) process. Using AI in this way, payment gateway can process larger quantities of data, from a range of sources including all the customer’s account information and other products with that company and soon, with the advent of open banking, with other companies as well.

Improving Efficiency:

Both machine learning and AI have the potential to revolutionize the way payments are processed, by improving operational efficiency and reducing cost. In fact, it’s already happening: AI is already being implemented with chatbots to lighten the load for customer service representatives, for example. Machine learning is already in the payment gateway world too, with learning algorithms playing important roles in helping speed along with authorization of transactions and monitoring.

AI can help reduce processing times for payments. It also can eliminate human error, saving precious time spent correcting those mistakes. Just think of a business that needs to process large amounts of data to generate financial reports and satisfy regulatory and compliance requirements; this process would typically involve a team of people performing repetitive data processing tasks. With AI, these tasks can be taught and left to machines that can accomplish these tasks faster and more accurately than human workers.

Artificial Intelligence (AI) is no longer the future, but it’s already the present. For eg: AI is the technology that powers the verbal commands you give Siri. It operates driverless cars and can beat you in online games. It may sound intrusive, or even creepy, but companies are investing in AI to learn your behaviors – and monetize those behaviors.

Picture customers shopping at your business for products they need the following week — but they just don’t know it. Using artificial intelligence (AI), computers can recognize buying patterns and anticipate a customer’s needs purchasing goods and services based on those needs.

Every year, citizens of the European Union make 122 billion digital payments using payment cards, e-wallets, bank-transfer apps, mobile wallets, and other payment methods. Without AI, there is no chance the payments industry could process so many transactions so quickly and keep fraud and error rates down to an acceptable minimum.

Artificial Intelligence (AI) is here for quite some time now and is successfully being used in banking applications like Fraud Analysis and Customer Risk Scoring but with a limited scope. The prominence of AI in decision making has effected the advent of data explosion, big data analysis, and internet penetration.

How AI is Powering Payments

It is predicted that AI would move into payments. It has the potential to streamline the payment chain, reduce fraud, improve customer service and optimize risk assessment—all reasons venture financing is flowing into AI.

The massive volume of digital payments is both a boon and a hindrance to spotting fraud. On the one side, it strains existing fraud-detection systems to the limits. On the other hand, it supports developers of artificial intelligence with the data they need to train their algorithms.

There are many types of AI, but for payments – particularly the high-risk merchant processing industry – the most important types include Machine Learning, Natural Language Processing, Verbal and Vision Recognition, and AI bots.

Machine Learning: a process where a software model is constructed and vast volumes of data are compared against it. Learning algorithms allow the model to evolve in response to changes in the incoming data. Machine Learning is crucial to the high-risk processing industry’s ability to detect and respond to fraud.

Machine learning is a subset of AI. All machine learning counts as AI, but not all AI counts as machine learning sounds unfamiliar. For eg, symbolic logic – rules engines, expert systems, and knowledge graphs – could all be described as AI, and none of them are machine learning.

Natural Language Processing (NLP): allows a machine to understand spoken commands or questions. Among the most well-known applications using NLP are Apple’s Siri and Google Translate. In the processing space, NLP is being used to reduce customer service costs by taking customer questions without human interaction. 

Natural Language Processing(NLP) is a branch of artificial intelligence that helps with the interaction between computers and humans using the natural language. The main objective of NLP is to read, decipher, understand, and make sense of the human languages in a manner that is valuable.

Visual Recognition (VR): allows the software to identify objects. In the payments industry, this is particularly applicable to fingerprint or facial recognition as second-level authentication methods. This technology enables customers to easily make mobile payments and provides security from fraud at the point of sale. In the world of high-risk processing, VR is important because it’s authentication capabilities reduce chargeback rates for companies targeted by fraudsters.

AI Bots: apps that represent the appearance of being human, while being completely automated. They combine NLP and Machine Learning code to present effective interfaces that can handle thousands of interactions at the same time, rather than the one at a time limitations of human customer service reps. AI bots are used in the chat software, messaging apps, and virtual assistants, and allow customers to make direct payments without leaving their preferred social media platform, or via text messaging.

In the high-risk space, AI bots can replace human service reps in handling customer questions and complaints. They completely speed up the service process and create significant savings. Just imagine if you could enable 24/7 availability, across channels, cost-effectively, and still provide consistent, compliant responses to all customer queries. 

Key factors to be noted:

Fraud Prevention:

With new payment methods like card-not-present (CNP) transactions come new opportunities for fraud. Artificial intelligence and machine learning are at the forefront of not just detecting fraud, but preventing it before it happens. These technologies can already uncover patterns and drive hidden insights, but the technology is moving towards refining these insights more further.
Currently,  AI and machine learning are arguably having their most significant impact in the fight against fraud, AI is increasingly being used to robotize instant fraud detection, with especially promising prospects for mobile payments and other higher-risk transactions, which are more vulnerable to fraud than, say, EMV-enabled POS devices.

Fraud detection is the most common use for AI in finance but it’s not the only one option. The same faculty for finding patterns and defining new variables can also be used to spot potentially useful or concerning connections and behavior as part of the know-your-customer (KYC) process. Using AI in this way, payment gateway can process larger quantities of data, from a range of sources including all the customer’s account information and other products with that company and soon, with the advent of open banking, with other companies as well.

Improving Efficiency:

Both machine learning and AI have the potential to revolutionize the way payments are processed, by improving operational efficiency and reducing cost. In fact, it’s already happening: AI is already being implemented with chatbots to lighten the load for customer service representatives, for example. Machine learning is already in the payment gateway world too, with learning algorithms playing important roles in helping speed along with authorization of transactions and monitoring.

AI can help reduce processing times for payments. It also can eliminate human error, saving precious time spent correcting those mistakes. Just think of a business that needs to process large amounts of data to generate financial reports and satisfy regulatory and compliance requirements; this process would typically involve a team of people performing repetitive data processing tasks. With AI, these tasks can be taught and left to machines that can accomplish these tasks faster and more accurately than human workers.

Can AI outwit humans? Share your thoughts and call iPayTotal now to get more details..



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