Banking
AI in Banking: Applications, Implications, and Considerations
Published by Jessica Weisman-Pitts
Posted on February 4, 2022
11 min readLast updated: January 28, 2026
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Global Banking & Finance Review®
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Published by Jessica Weisman-Pitts
Posted on February 4, 2022
11 min readLast updated: January 28, 2026
Add as preferred source on GoogleAI revolutionizes banking by enhancing fraud detection, improving customer service, and optimizing credit decisions, while raising ethical concerns.
By Therese Schachner, Lead Cybersecurity Consultant and Writer at VPNBrains
Through artificial intelligence (AI), the power of machines is leveraged to streamline banking operations. With AI, tasks such as data processing can be automated, which allows these tasks to be completed more efficiently and lightens the workload for bank employees. AI can also be used to draw conclusions from data, which has applications such as detecting fraud and making credit decisions. As a result, banks using AI technologies are able to handle high loads of data at a fast pace, while keeping bank employees’ workloads at a more manageable level and reducing operating costs.
Nevertheless, there are some drawbacks associated with the use of AI in banking, including job elimination, bias, and data privacy risks. In this article, we’ll delve into three areas of banking that AI has helped transform: customer experience, fraud detection, and credit. We’ll examine how AI has benefited both banks and customers in these three areas, as well as consider some of the resulting ethical consequences and other concerns.
Machine learning models have been used in banking for real-time fraud detection, which can identify anomalous activity in online banking transactions and send timely alerts when this suspicious behavior is detected. Since AI can perform quick and relatively accurate analyses of data for malicious activity, AI has proved useful for responding to concerns of fraud in a timely manner, before they escalate into larger issues. Although AI-powered fraud detection can’t identify all fraudulent transactions and often results in false positives, it helps protect banks and their customers from the deployment of malware and other cyberattacks, bolstering the overall security of banking activity.
The use of AI-driven biometric authentication for users to verify their identity prior to conducting banking activities can also help prevent fraudulent transactions. Biometric authentication, such as fingerprint and facial recognition, involves comparing aspects of a customer’s physical characteristics to stored information about these characteristics to determine whether they are the person they claim to be. Since biometric data is different for each person, it is difficult for attackers to falsify.
However, there are risks associated with biometric authentication methods. It’s possible for attackers to gain access to databases storing biometric data, as they did during the Biostar 2 data breach in August 2019 that exposed millions of users’ face and fingerprint data. In the event of this type of cyberattack, users can’t “change” their facial or fingerprint data to protect against unauthorized use of it in the same manner that they can reset their passwords following a data breach.
Chatbots have been used to provide timely, customized, and accurate responses to customers’ questions and assist customers with some payments and other transactions. Chatbots can assist banking customers 24/7 and help lighten the load of requests with which human customer service agents are faced. Instead of accommodating every customer and handling all their issues and questions, customer service representatives can focus their time on customers requesting more specialized forms of assistance that chatbots can’t provide.
There are disadvantages associated with the usage of chatbots, though. The increased use of chatbots comes with the elimination of customer service jobs since chatbots conduct many of the tasks that customer service representatives would otherwise handle. Also, chatbots can’t always properly interpret every spoken or written communication from customers, and customers often prefer human assistance over chatbots since they tend to find that human customer service agents better meet their needs.
Another way in which AI has transformed the customer experience has been through the use of information about customers’ banking activity to present personalized recommendations to customers. Techniques such as predictive analytics have been used to detect trends in data such as customers’ past transactions and interactions with banking websites. Analytical findings from this data are then used to present services and products, such as credit cards and mortgage offers, to customers that are tailored to their needs and interests. As a result of these personalized recommendations, customers benefit from an improved customer experience, and banks benefit from increased customer engagement and utilization of their financial products and services. However, data privacy concerns are often raised, since not all customers approve of the collection, storage, and analysis of their data for financial marketing purposes.
AI is used in banking to make informed and data-driven financial decisions, such as how much credit an individual will be offered. AI evaluates customer data, such as credit history and past purchases, to evaluate customers’ credit and determine their loan eligibility. The use of AI helps speed up the loan approval process, which can be completed much more quickly when it is automated.
However, AI lending models are known to incorporate racial and gender bias. These models are often trained on credit data in a manner that results in biased credit decision making due to factors such as proxy discrimination and historical bias, even though legal restrictions in the United States prevent these models from directly considering demographic attributes such as gender and race.
For example, a variable such as a person’s job may serve as a proxy for race or gender if the number of employees holding that job is dominated by people of a particular race or gender. If a woman holds a job that has traditionally been female-dominated, the model could decide to offer her unfairly low credit since it was trained on historical data in which people holding that job, the majority of whom were female, were discriminated against and granted low amounts of credit. As a result, credit scoring and access to loans have very different outcomes across different demographic groups, even for individuals who have the same financial ability to pay back loans.
Although AI has helped make bank operations more time efficient and cost effective, AI adoption comes with drawbacks, such as ethical concerns and negative impacts on some aspects of the customer experience.
If solutions to these issues are created and adopted, banks will continue to be able to streamline operations and handle high loads, but in a fairer manner that better accommodates customers. The fast-paced innovation in the fintech industry has been promising and will likely continue to yield solutions that help address these issues associated with AI in banking.
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By Therese Schachner, Lead Cybersecurity Consultant and Writer at VPNBrains
Through artificial intelligence (AI), the power of machines is leveraged to streamline banking operations. With AI, tasks such as data processing can be automated, which allows these tasks to be completed more efficiently and lightens the workload for bank employees. AI can also be used to draw conclusions from data, which has applications such as detecting fraud and making credit decisions. As a result, banks using AI technologies are able to handle high loads of data at a fast pace, while keeping bank employees’ workloads at a more manageable level and reducing operating costs.
Nevertheless, there are some drawbacks associated with the use of AI in banking, including job elimination, bias, and data privacy risks. In this article, we’ll delve into three areas of banking that AI has helped transform: customer experience, fraud detection, and credit. We’ll examine how AI has benefited both banks and customers in these three areas, as well as consider some of the resulting ethical consequences and other concerns.
Machine learning models have been used in banking for real-time fraud detection, which can identify anomalous activity in online banking transactions and send timely alerts when this suspicious behavior is detected. Since AI can perform quick and relatively accurate analyses of data for malicious activity, AI has proved useful for responding to concerns of fraud in a timely manner, before they escalate into larger issues. Although AI-powered fraud detection can’t identify all fraudulent transactions and often results in false positives, it helps protect banks and their customers from the deployment of malware and other cyberattacks, bolstering the overall security of banking activity.
The use of AI-driven biometric authentication for users to verify their identity prior to conducting banking activities can also help prevent fraudulent transactions. Biometric authentication, such as fingerprint and facial recognition, involves comparing aspects of a customer’s physical characteristics to stored information about these characteristics to determine whether they are the person they claim to be. Since biometric data is different for each person, it is difficult for attackers to falsify.
However, there are risks associated with biometric authentication methods. It’s possible for attackers to gain access to databases storing biometric data, as they did during the Biostar 2 data breach in August 2019 that exposed millions of users’ face and fingerprint data. In the event of this type of cyberattack, users can’t “change” their facial or fingerprint data to protect against unauthorized use of it in the same manner that they can reset their passwords following a data breach.
Chatbots have been used to provide timely, customized, and accurate responses to customers’ questions and assist customers with some payments and other transactions. Chatbots can assist banking customers 24/7 and help lighten the load of requests with which human customer service agents are faced. Instead of accommodating every customer and handling all their issues and questions, customer service representatives can focus their time on customers requesting more specialized forms of assistance that chatbots can’t provide.
There are disadvantages associated with the usage of chatbots, though. The increased use of chatbots comes with the elimination of customer service jobs since chatbots conduct many of the tasks that customer service representatives would otherwise handle. Also, chatbots can’t always properly interpret every spoken or written communication from customers, and customers often prefer human assistance over chatbots since they tend to find that human customer service agents better meet their needs.
Another way in which AI has transformed the customer experience has been through the use of information about customers’ banking activity to present personalized recommendations to customers. Techniques such as predictive analytics have been used to detect trends in data such as customers’ past transactions and interactions with banking websites. Analytical findings from this data are then used to present services and products, such as credit cards and mortgage offers, to customers that are tailored to their needs and interests. As a result of these personalized recommendations, customers benefit from an improved customer experience, and banks benefit from increased customer engagement and utilization of their financial products and services. However, data privacy concerns are often raised, since not all customers approve of the collection, storage, and analysis of their data for financial marketing purposes.
AI is used in banking to make informed and data-driven financial decisions, such as how much credit an individual will be offered. AI evaluates customer data, such as credit history and past purchases, to evaluate customers’ credit and determine their loan eligibility. The use of AI helps speed up the loan approval process, which can be completed much more quickly when it is automated.
However, AI lending models are known to incorporate racial and gender bias. These models are often trained on credit data in a manner that results in biased credit decision making due to factors such as proxy discrimination and historical bias, even though legal restrictions in the United States prevent these models from directly considering demographic attributes such as gender and race.
For example, a variable such as a person’s job may serve as a proxy for race or gender if the number of employees holding that job is dominated by people of a particular race or gender. If a woman holds a job that has traditionally been female-dominated, the model could decide to offer her unfairly low credit since it was trained on historical data in which people holding that job, the majority of whom were female, were discriminated against and granted low amounts of credit. As a result, credit scoring and access to loans have very different outcomes across different demographic groups, even for individuals who have the same financial ability to pay back loans.
Although AI has helped make bank operations more time efficient and cost effective, AI adoption comes with drawbacks, such as ethical concerns and negative impacts on some aspects of the customer experience.
If solutions to these issues are created and adopted, banks will continue to be able to streamline operations and handle high loads, but in a fairer manner that better accommodates customers. The fast-paced innovation in the fintech industry has been promising and will likely continue to yield solutions that help address these issues associated with AI in banking.
This is a Sponsored Feature
The article discusses the applications and implications of AI in the banking sector.
AI uses machine learning models for real-time fraud detection, identifying anomalies in transactions.
Biometric data breaches pose privacy risks as users cannot change their biometric data like passwords.
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