The Role of AI and ML in Banking

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AI-Bank of the Future

With the advent of technology and the global pandemic, the banking sector has evolved rapidly, and clients’ expectations have grown. A massive shift toward online platforms has raised contentions for a more satisfactory user interface. Given these modern technological transformations, Artificial Intelligence and Machine Learning in banking and financial services are gaining higher traction as banking institutions now understand the significance of automation for crucial procedures. 

Approximately 85% of top-level industry decision-makers think AI in banking will add some meaning and advantages to their enterprise processes in the coming days. Keep reading below to learn more about the need for AI in banking, along with its existing obstacles and solutions. 

Understanding the Role of AI in Banking

Even though AI in financial services and banking is a newly emerged concept, it is already on its way to making financial institutions more accessible, efficient and secure. 


Furthermore, to leverage AI and deep learning in investment banking, financial institutions must continuously innovate to address the increased demand from modern tech-savvy clients and financial regulators. Hence it might not be wrong to say that adopting AI in the banking domain will transform money management in the coming years. 


Top Advantages of Artificial Intelligence and Machine Learning in Banking?


  • Better investment evaluation

Interest earnings are only one aspect of revenue generation for banks and financial institutions. Therefore, they continually look for lucrative possibilities to invest and earn profitable returns. This is where AI comes into the picture. With the help of advanced AI-based investment software, banks can gain valuable investment recommendations that match their risk-taking capacity. 


Furthermore, it can assist in accurately assessing customer funding proposals, given that industry-relevant data is often challenging to understand. However, you must note that the decision to invest funds remains in the hands of human beings. This investment analysis software makes the process more seamless and allows the decision-maker to accommodate more variables. 


  • Reduced risks and operational costs 

While people enjoy the physical feel and human interaction in banking, it has a considerable drawback. Manual errors are inevitable, and they can sometimes have severe repercussions.  


In addition, even though trained employees usually make fewer manual mistakes, even one could expose the bank or financial institution to penalties and cause irreparable reputational harm. On the other hand, AI-based decision management systems lower this risk by making logic streams in the data capture and incorporating predictive and prescribed methods to solve enterprise issues.


  • Enhanced client experience

Modern clients are continually looking for ease and convenience. For instance, the ATM (Automated Teller Machine) facility was a hit because clients could access integral assistance even when banks and financial institutions were closed. That level of comfort has only encouraged more innovation. 


Furthermore, with the help of AI in financial services, customers can now open a bank account with their web or mobile devices right from the comfort of their homes. Additionally, with appropriate business management software, financial decisions can be executed and rolled out without extensive procedures.


What Are Some Potential Use Cases of AI in Banking? 

Artificial Intelligence and Machine Learning have become integral aspects of banks and financial institutions. They have started integrating AI technologies into their business operations to offer better convenience to their customers. Some of the possible AI banking use cases are as follows: 

  • Fraud detection and cybersecurity 

In the banking and financial sector, many online transactions take place, such as depositing and withdrawing funds, paying bills and performing transactions via online accounts or mobile apps. Therefore, there is a rampant demand for the banking and finance sector to improve fraud detection efforts and cybersecurity to save valuable user data from potential cyber threats. 


It is where Machine Learning and Artificial Intelligence can help. AI technologies in the banking and financial sectors enhance online security and can better detect fraudulent activities by alerting clients and financial institutions. 


  • Biometric authentication 

Biometric authentication in the banking and financial sector can be an ideal instance of safety and comfort. The biometric verification process is unique and individual-specific, making it challenging for cyber attackers to deceive clients and extract money from them. 


In addition, with its strong identity validation procedure, this form of authentication offers a competitive advantage to banking and financial institutions by providing them with better security and clarity. 


  • Credit and loan decisions

Recently, banks and financial institutions have begun integrating AI-based techniques to make more educated, secure and favourable credit and loan decisions. Also, in present times, numerous banks and financial institutions are still limited to using credit scores and records and client considerations to decide the creditworthiness of a person or company seeking a loan facility. 


Nevertheless, it might not be wrong to say that existing credit reporting systems are usually filled with mistakes that misclassify creditors in the eyes of banks and financial institutions. 


On the contrary, artificially intelligent loan and credit systems can better track and determine the customers’ patterns and behaviours to decide their loan repayment capacity and creditworthiness.


AI Adoption: Obstacles and Solutions 

Here are some prominent obstacles in AI adoption and its possible solutions: 


  • Explainability, Clarity and Trust

Developing AI models that deliver precise projections can sometimes be challenging for banks and financial institutions. Furthermore, it can also be time-consuming for senior management to explain advanced AI-based applications to their employees. Nevertheless, this issue can get resolved with adequate training and the development of easy data models. 


  • Black Box Impact

The intricacy of neural devices makes Machine Learning and artificially intelligent software challenging to interpret. Hence to overcome this challenge, financial institutions and banks must increase governance and embrace visual understanding and model administration frameworks.


The Bottom Line 

In a nutshell, Artificial Intelligence and banking go hand in hand, given the countless advantages this modern technology offers. In addition, according to a report, 65% of senior banking and financial management anticipate favourable transformations from adopting AI and Machine Learning in the finance and banking domain. Hence, it might not be wrong to say that every financial institution must invest in the latest AI technologies to offer exceptional client services and make operations more seamless. 



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