The Role of AI and ML in Banking

Artificial intelligence (AI) and Machine Learning (ML) both have the potential to bring evolution into the banking sector. Some banks have already begun integrating them into their day-to-day operations. It not only benefits the banking professionals but also the customers. 

If you are curious to know what role AI and ML can play in the banking industry, this blog post is for you. It will outline the practical benefits that both technologies can bring to the industry. However, before we delve into the applications of AI and ML in banking, let’s first introduce you to these two technologies. 

So, what is AI and ML in the Banking Context?

Artificial intelligence (AI) in banking refers to the use of intelligent systems trained on pre-existing rules derived from human data. These systems enable professionals to automate repetitive tasks and enhance efficiency. 

AI-based systems don’t need to be fully autonomous. They are being designed in a way that requires human intelligence to function correctly. 

In short, these systems are helpful assistants that make it easier to complete complex tasks. They streamline the banking processes and eliminate human errors. This helps banks improve their working efficiency and provide their customers with a better banking experience. 

Regarding machine learning (ML), you can think of it as a subset of AI. It helps AI systems learn and improve by utilizing existing customer data. ML eliminates the need to repeatedly update AI systems with complex programs. 

ML can analyze all the data passed through the systems it is integrated into. Using this analysis, they understand working patterns and behaviors and use them for future predictions and decision-making. 

Practical Applications of AI and ML in Banking

Follow are some of the key applications of AI and ML in banking. They are not the least one, there are many more as well. But these are the ones that are highlighted by many of today’s banking professionals. 

AML & CFT

Banks worldwide are legally required to monitor their customers’ transactions. The key focus of the monitoring is to detect money laundering or terrorist financing.

This is what AI systems help banks with. Some systems have been specifically designed to assist with AML & CFT monitoring. These systems analyze transaction patterns to detect suspicious activities such as structured transactions. 

Regarding ML models, they help AI systems improve over time. They allow them to learn new fraud patterns automatically, eliminating the need for reprogramming. 

As an example:

A customer who usually makes small, local transactions suddenly receives several large international wire transfers. An ML system will flag this as unusual and immediately report it to a compliance officer for review. This eliminates the need for human intervention in reviewing every transaction in depth.

KYC and On-boarding Processes

Whenever a new customer comes to open an account in a bank, the most crucial thing a banker has to perform is the KYC process. In KYC, they must verify the identity of customers and, most importantly, perform a risk assessment. This process involves reviewing numerous documents. It takes a significant amount of time, and sometimes a single account can occupy the entire day of a banker. 

That is where AI systems jump in to help. They automatically:

• Check documents
• Perform a cross-reference with databases
• Flag inconsistencies
• Speed up the onboarding process

As an example:

During digital account opening or online registration, customers upload their ID card and a selfie. AI verifies the ID, matches it with the face in the selfie using facial recognition, and completes KYC within minutes. 

This way, banking professionals will not need to perform manual verification, and the customer will also get a seamless experience.

Document Processing

Banks deal with a large number of physical or scanned documents (IDs, bills, tax forms, contracts). Their staff has to manually enter all the information into the systems. And you know what, this is a very time-consuming process and is also prone to human errors. And in the banking sector, a single human error can result in penalties from regulatory authorities. 

This is where AI is helping them save both time and improve efficiency. We have personally seen some banks now utilize AI-powered OCR (optical character recognition) systems, such as image-to-text converters. 

Using an image to text converter, banking staff can convert physical or scanned documents into editable digital files. That digital files can be easily uploaded and integrated into their systems, such as Temenos24. 

When provided with digital files, AI-powered banking systems automatically extract information (e.g., customer name, address, contact details) into the customer information file, also referred to as a CIF. 

This eliminates the need for manual data entry, increasing the productivity of banking staff and allowing them to deal with more customers in their day-to-day operations.

Cybersecurity

Providing the best cybersecurity to customers is one of the most crucial tasks for banks today. All the banks are responsible for protecting customer accounts and sensitive financial data from cyberattacks. And this is what AI and ML systems are helping them with. 

AI continuously monitors systems to detect unusual access patterns or threats. ML models adapt to new attack types, offering stronger protection.

As an example:

If you try to log in to your bank account digitally using a brand-new device or at a foreign location, the ML data AI systems will find it suspicious and will not let you log in to the device. 

To protect your account from unauthorized access, these systems will make an automated call to verify your identity. Or they might send an SMS to your registered contact number or email address. 

That call or email will include a verification code that you can use to verify that it is you trying to access the account. This way helps banks keep their customers protected. Additionally, it allows them to shield customers from the frustration of undergoing lengthy physical verification processes. 

Artificial Intelligence (AI) and Machine Learning (ML) are reshaping the banking industry, improving operations, customer service, and security. AI is streamlining processes like customer support through chatbots, while ML analyses vast amounts of data to detect fraud and assess credit risks. These technologies also enhance personalized banking services, offering tailored financial advice and product recommendations. As AI and ML continue to evolve, banks are adopting them for better decision-making and operational efficiency. To stay ahead in this field, enrolling in an Artificial Intelligence Course provides valuable insights into how these technologies are transforming banking.

Enhancing Fraud Detection and Security

AI and Machine Learning are fundamentally changing how banks identify and respond to fraud. Machine Learning algorithms can analyze huge amounts of transactional data in real-time and raise alerts for suspicious activity immediately. This helps provide control of illegitimate behaviour while not disturbing genuine customers.

Personalized Banking Experience

AI plays a major role in hyper-personalization and from chatbots to personalized product recommendations, AI is assisting banks in providing hyper-personalized experiences.  Machine learning models are developed based on customer behaviors and preferences to deliver relevant financial options to their customers- ultimately generating positive emotional responses and increased customer satisfaction.

Smarter Credit Scoring and Loan Processing

Credit scoring systems rely heavily on historical data to make decisions. Machine learning provides an avenue for banks to evaluate credit risk based on alternative data sources, helping banks make better and more informed decisions on lending and in turn providing context around transparency and inclusion. AI also speeds up loan decisions by increasing banks transparency when determining the loan purpose and the speed of checking documents and assessing risk.

Why Should You Consider a Machine Learning Course?

As Artificial Intelligence is beginning to be adopted across the banking industry, the demand for AI professionals is increasing. In a machine learning course, you will learn to build fraud detection systems, prediction models, and intelligent automated systems. It doesn’t matter if you are getting into fintech or it is an upskilling exercise for your existing role, it’s a great investment in a future with AI and data-driven banking.

Final Talk

Artificial intelligence and machine learning systems are enabling banks to perform complex tasks more efficiently. They are also assisting them in automating repetitive tasks. 

The above-discussed applications of AI and ML in banking are only a few major ones. They are not the least. There are many more tasks where these technologies play a role, such as customer service (automated chats), fraud mitigation, credit scoring, and loan approval, among others. 

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