Applications of AI in Banking

Artificial Intelligence in Banking is stated by the American Banker to be the game-changer for tomorrow. It is hoped that Artificial Intelligence for Banking and Financial Markets will help banks be more proactive, reduce overall costs and provide their customers better services.
AI Applications in Banking

Artificial Intelligence in the banking industry is a branch of AI and Machine learning that uses computational algorithms to solve banking problems. AI can be provided as standalone software or embedded within banking applications. All banks apply AI to loan evaluation, customer service, fraud detection, cybersecurity, and other areas. As a result, AI processes can help banks do their jobs more efficiently while improving services. This blog explains the applications of Artificial Intelligence in Banking.

Allied Market Research says, The global AI in the banking market size was valued at $3.88 billion in 2020 and is projected to reach $64.03 billion by 2030. 

1. Machine Learning Applications for Banking

1.1. Fraud Detection using AI in Banking

Fraud Detection using AI in Banking

Statistical analysis and other data-mining tools to uncover fraudulent attempts made through online channels are known as fraud detection. Chargebacks, transactions with the same IP but different details, and attempting to access funds after losing bank details are all examples of this. Using machine learning analysis for fraud detection has resulted in significant increases in detecting fraudulent transactions with minimal false positives. This is because it effectively seeks to identify every transaction that was most likely valid as a fraud.

For example, Teradata is an artificial intelligence company that sells fraud detection software to banks. They believe that by assisting their data analytics software in predicting possible fraud situations. While avoiding acceptable deviations from the norm, their machine learning platform improves banks’ fraud detection.

2. AI Applications in Banking

2.1. AI for Analyzing Sentiments in Banking

AI for Analyzing Sentiments in Banking

Artificial Intelligence for Analyzing Sentiments in banking uses computer systems to do tasks that usually require human intelligence. For example, sentiment analysis aims to analyze. Text or speech and determine whether the statement expresses a positive, negative or neutral opinion on some subject. 

Here're the Trends in Artificial Intelligence and Machine Learning Right Now

In banking, AI models are used to analyze the attitude of a range of payment exchanges. In addition, these models can predict market trends and provide expertise in industry trends using machine learning techniques.

2.2. Risk Management

Risk Management

With the development of AI, a new industry – AI for Risk Management will emerge. Many banks are now using this technology to quickly screen transactions and detect fraud.

AI and machine learning can execute this function with extreme resolution and transparency in banking. AI-powered mobile banking apps simplify financial transactions and evaluate borrowers’ banking data. It might improve bankers in detecting the risk related to borrowing for clients. Furthermore, bankers may examine the borrower’s activity applying the AI-driven risk assessment process, minimizing the likelihood of financial fraud.

2.3. AI Data Collection and Analysis

AI Data Collection and Analysis

The Banking industry uses artificial intelligence. For example, to extract data from emails, voice or video chats, and instant messages sent through any channel. Banks are trying to take advantage of the changes in technology. 

It’s the reason they are exploring artificial intelligence for different use cases that can help them provide better customer service. For Example, banks have deployed artificial intelligence-powered bots to make banking more personalized.

2.4. AI Cybersecurity

AI Cybersecurity

Artificial Intelligence cybersecurity in banking keep track of everyone on the network and with whom bank customers communicate with AI cybersecurity in the banking or private sector. All contacts can be monitored and sorted by different parameters. For Example, by person, profession, department, activity type.

3. AI Chatbots

AI Chatbots

Chatbots are computer programs designed to simulate textual human conversations through chat interfaces. They represent a digital version of the customer service representative used by many banks. Chatbots are efficient, reliable, and effective in answering customer queries like their real-life counterparts. If you need a bank to keep track of your money, you may also have an account with a chatbot. It helps you manage various features of your personal financial needs.

For Example, instead of calling or emailing the customer service department, you can directly talk to the bot and ask how much money you have in your bank account, your credit balance, your last check done, and any unclaimed assets. 

In addition, you can do all this and transfer funds to other bank accounts by writing messages in response to the bot’s prompts.

For Example, the Virtual Assistant Chatbot from HSBC Amy is fluent in English, Simpler in Chinese, and she assists customers in getting quick answers to frequently asked questions about the bank’s products and services.


Artificial Intelligence is a developing and growing sector in the banking industry. It improves efficiency and speed, accuracy, and costs. 

Data scientists play a critical part in this emerging trend by discovering valid data, automating decision-making systems, and detecting fraud. So, in addition to positively impacting the last check financial sector, Artificial Intelligence can also help with various other problems.