AI: Use Cases for Machine Learning in Banking in 2021

By Veritran - Feb. 26, 2021 - Category : Without category

Banks are turning to artificial intelligence to increase their efficiency and improve their services. Here are the trending use cases this year.

Artificial Intelligence (AI) is becoming a disruptive force in banking, bringing automation to operations management, 24/7 customer service and pattern processing to ensure more secure transactions.

Thanks to the growth in digital data and computing power, the possibilities for using AI have increased significantly. Today, there are numerous practical applications.

The potential is so big, that analytics and AI could add an additional $1 trillion in value each year to the financial sector, according to a report by Mckinsey.

The potential contribution of AI to banking profitability should not be underestimated. By increasing labor productivity, this group of technologies, which also encompasses machine learning and big data, could help to structurally reduce costs.

With this in mind, as well as the wave of further innovation to come, organizations must prepare themselves to face not only the new demands of users who want better solutions, but also the level of competition brought by new players in the sector.

To help, here is an overview of how the financial institutions can use AI in 2021.


AI can review massive amounts of data to uncover subtle patterns, allowing you to improve cybersecurity efforts and help create organizations with more resilient protocols.

Technology has proven especially useful in threat and malware detection because it can help identify deviations from expected patterns and also provide countermeasures to address and neutralize them, according to the Association of Certified Financial Crime Specialists, a professional educational institution that operates across Latin America.

This can strengthen Know Your Client (KYC) and Anti-Money Laundering (AML) processes, and reduce the time your bank spends on these mandatory regulatory frameworks.

Machine learning also enables behavioral analysis to generate predictions that, combined with the context provided by the expertise of cybersecurity professionals, can modernize and strengthen assessments, and intercept incidents before they even occur.


One of the most advanced and established solutions provided by AI is biometrics for user identification, to prevent access by impostors.

In its broadest form, this solution is part of physical biometrics, which studies and identifies distinctive and measurable patterns of parts of the body, such as the face, iris, and fingerprints, among others.

With the help of AI, these patterns are transformed into a unique code, which is processed to provide access to a platform or application that houses sensitive user information. The technology has evolved to incorporate deep learning, to the point of being able to distinguish a human face from an image or photograph in seconds, quickly enabling access to a platform or application.

On the other hand, with the growth of remote work, the financial industry has reinforced its security systems, turning to remote authentication solutions to manage who accesses the institution's information, and when.

In an environment with factors that cannot be controlled by the organization - unprotected Wifi, for example - AI for remote authentication examines each login attempt and produces a risk assessment for that scenario.

Based on this evaluation, the algorithm identifies which information the employee will need to provide to access the system - or not - such as answering specific questions, requesting a token code, or responding to a push notification from their smartphone.

The advantage of adding AI to a remote authentication process is that it learns the user's behavior, making it possible to assess how risky the login request is. Some of the data that the algorithms study is the user's location, the browser model, and even the recurrent login time.

That transforms the security software from being a simple processing unit into a technology that’s capable of learning behavioral patterns to evaluate risks and respond appropriately to approve or deny a log-in request.


Using techniques such as natural language processing or image recognition, organizations can automate manual tasks that are more repetitive or provide less added value, such as FAQs.

Initially, virtual financial services used generic queries to develop virtual assistants or chatbots that answered basic questions. Now, these communication tools have evolved, and should respond to new demands, such help with transactions.

Chatbots that use only FAQs to configure their interactions are unlikely to be able to interpret questions and respond adequately. In contrast, other assistants that use databases with intelligent analytics engines enrich the user experience by being more accurate and providing transactional solutions.

By using a more intelligent chatbot, clients will get faster and more accurate answers - at a lower cost for the institution. And, in contrast to a human team dedicated to answering questions, a robot can respond to 10,000 people at the same time.

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