AI & ML: The Next Big Thing in Banking?

Discover how AI & ML are revolutionizing banking. Explore the future of AI ML in banking and stay ahead of the curve.
AI ML Banking Future

The banking industry has always been slow to adapt to digitization, but technological advancements have forced it to come out of its comfort zone and embrace it.

First, it was the rise of Fintech companies, which exposed the limitations of traditional banking by offering mobile payments, peer-to-peer lending, and robo-advising.

Secondly, customers wanted more convenience in accessing banking services, as their busy schedules proved to be a hindrance when visiting the nearest banking branch.

Lastly, COVID-19 struck the final blow as social distancing and lockdowns forced the banking industry to rely on digital channels to provide their services, as it was risky for the customers to come to the nearest branch due to the fear of catching the flu.

Now, post-pandemic, we are witnessing the rise of Artificial Intelligence and Machine Learning, which heralds the next disruption in the banking industry. In this blog, we will explore how AI and Machine Learning can help banks improve their services.

Banks Will Be Able To Reduce Their Operational Costs With the Help of Artificial Intelligence

Banks generally incur huge operational costs to provide their services to customers. These include maintaining technology and infrastructure, providing employee salaries and benefits, marketing and advertising, compliance and regulatory costs, office supplies and equipment, etc.

Artificial Intelligence can play a meaningful role in helping banks reduce their operational costs.

For example, adding customer data from contracts and forms can take up a lot of time and is often riddled with human errors. Banks can automate this task by implementing robotics process automation software that can perform these tasks that were previously done by humans.

That’s not all! Banks can even automate other repetitive tasks like loan processing and account maintenance with the help of AI-powered tools.

It is said that by 2030, AI will save more than $1 trillion for banks.

AI Chatbots Will Help the Banks in Provide Better Customer Satisfaction

With the help of AI Chatbots, banks will be able to provide more convenience to their customers. For example, if someone wants to transfer money at midnight, they can do this with the help of the banking chatbot, freeing them from having to wait for the banking branch to open in the morning. That’s not all! Banking chatbots have started providing a wide array of services:

Use Cases of Banking Chatbots

Often, when having a query, we have to wait for the customer service representative to come online (while listening to a ringtone in the background), which can feel annoying at times. This problem, too can be resolved by the AI chatbot.

For example, if a customer has some questions regarding the eligibility of the new scheme launched by the bank, then instead of having to wait 20-25 minutes for a customer service representative to come online, the AI chatbot can answer this question immediately, thus saving time for the customer.

AI chatbots will be beneficial not just to customers but also to banks.

  • More convenience in services = better customer satisfaction
  • Unlike humans, AI chatbots will not get exhausted if there are a large number of queries from customers. Also, it will free up the customer service representative from mundane calls that can be resolved by the chatbot.
  • AI chatbots can help banks in reducing their customer service costs.

However, I do feel that the option of talking with a human representative should always be available. Chatbots should not replace human interaction completely.

AI chatbots can also help banks provide personalized services to their customers. For example, it can learn the banking habits and preferences of the customers and suggest services that align with their needs.

Machine Learning Will Be the Game Changer- For Better Fraud Detection

The COVID-19 pandemic forced banks across the world to undergo a rapid digital transformation. Thanks to this disruption, banking services have become extremely convenient. For example, if I want to transfer funds to my mother’s bank account, I can do it immediately through a mobile application.

That’s not all! Banking apps have made it possible to pay all the bills, change the ATM pin, or get bank account statements with just one click.

These are just a few of the services I have given as an example, but banking apps nowadays provide a wide range of services through their applications, which previously required one to visit the nearest branch.

But behind this convenience lies an inherent risk that cannot be ignored.

As more and more people are relying on mobile banking, cybercriminals are increasingly focusing on committing fraud through mobile applications.

For example, a report by Zimperium (a mobile security company) revealed that it has analyzed 24,000 samples of malware infections that are specifically designed for mobiles and found that 29 malware families (a group of applications with similar attack techniques) are targeting 1,800 mobile banking apps.

These malware have the capability to steal bank credentials by evading security and avoiding detection.

Digital Fraud by numbers

The success of mobile banking has begun to attract all sorts of bad actors, just like moths to a flame, and this is where machine learning comes into the picture.

For example, a machine learning algorithm can analyze transactional data and determine if there is an unusual pattern in it. This is done by training the algorithm from the dataset containing past transactions, enabling it to analyze newer transactions based on what it learned from the previous data, and allowing the algorithm to flag transactions that deviate from the norms as suspicious. For example:

  • When there is a small transaction followed by a larger one in a short period of time
  • Transaction taking place at odd times
  • Transaction occurring at different locations, for example, a debit card being used in two different cities within minutes

The digital landscape will keep on evolving, and so will the different ways through which cyber criminals will commit fraud. That’s why we need a system that can continuously evolve to stay one step ahead of these hackers, which is why machine learning algorithms will play an essential role in combating fraud in the future.

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AI & ML Can Assist Banks in Maintaining All Regulatory Compliances

There is no denying the fact that Regulatory Compliances can be complicated to follow. They never remain the same and always keep on changing, which poses quite a challenge to the banks, as understanding the changes, how they will affect the banking operations, and what adjustments will be required to the policies and procedures can be a very time-consuming process.

I feel AI & ML can play a meaningful role in streamlining all the compliances that are mandatory for the banks to follow. Here are some examples of it:

  • Banks are required to follow KYC and AML compliances, which is a time-consuming process as one has to screen vast amounts of data against the sanctions list and check for any suspicious activity. Artificial Intelligence can completely automate these checks, freeing up banking officials to focus on more complex cases that cannot be afforded to get overlooked.
  • Machine learning algorithms can create a customer risk profile after analyzing a wide range of data points. This will enable the banks to improve their compliance efforts and detect any problems before they occur.
  • Traditional methods often overlook suspicious activity, resulting in substantial damage to the customers and banks. This can be prevented with artificial intelligence as it can analyze transactions in real-time and alert the authorities if money laundering or fraud is going on in the transaction.
  • Artificial Intelligence can sift through vast amounts of legal information and keep the banks well-informed regarding any changes in regulatory compliance.

A 2019 survey by the American Bankers Association revealed that 70% of community banks admitted that regulatory compliance feels quite burdensome to them. This means artificial Intelligence and machine learning can be quite a boon for banks when it comes to following regulatory compliances.


Clients can use AI to simplify their financial planning. By asking the chatbot, they can learn more about the latest investing trends, economic opportunities in their sector, etc. In this regard, AI in banking will serve as a free financial advisor, giving the bank more competitive power.

Artificial Intelligence and Machine learning will assist banks in identifying fraudulent activities, tracking loopholes in their systems, minimizing risks, and improving the overall security of online finance.

A significant benefit of Artificial Intelligence in banking is automating decision-making in underwriting and credit analysis.

Banks can use ML to respond to cyberattacks efficiently. Automated fraud detection is now becoming a norm in the banking industry.

AI Chatbot in banking can send timely notifications for transactional updates, payment reminders, bank offers, policy offers, etc.

To Conclude

The integration of AI and ML is still in its early stages within the banking industry, but it holds immense potential to change the way we use different banking services. As artificial intelligence and machine learning continue to evolve, we can expect even more innovation that will make banking more secure, and personalized.

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