Machine Learning: The Need of the Hour for Banks

Machine Learning is a branch of Artificial Intelligence which is capable of self-learning. It has been widely accepted as the next big thing and is expected to transform banking operations in the coming years.

Machine learning is an emerging technology with the potential to transform both front and back-office operations. It will be able to automate tasks that are repetitive, time consuming and error prone. Machine learning can also be used for customer segmentation, fraud detection and credit scoring.

Let’s look at some of the ways banks are using machine learning:

Conversational banking

Customer service, which has a significant impact on both present and potential consumers, is undoubtedly the most crucial component of banking. Understandably, this is the area where financial institutions are experimenting with machine learning the most in order to improve customer service and communication.

The most popular application of machine learning in banks is through chatbots for conversational banking. Predictive analysis is a tool that virtual assistants can use to direct consumers, handle quick account updates, and even advertise new services and goods.

Customers can communicate with these chatbots at more convenient times and using simpler channels like smartphones and SMS. Virtual assistants eliminate the need for consumers to phone or physically visit a branch, saving both parties time and enabling banks to provide round-the-clock customer care.

Fraud detection and protection

Machine learning can have a significant impact on fraud identification and protection in a field that impacts practically all financial institutions. Machine learning can identify anomalies and notify clients by looking at their location, typical client behaviour, and spending trends. This helps lower account and credit card theft.

Machine learning can accurately analyse and warn in real-time with minimal to no errors, a task that is practically difficult for humans to perform because we can’t examine hundreds of transactions in a matter of seconds.

Additionally, machine learning algorithms can identify questionable account activity and either require the customer or the bank to take action or block the transaction. This makes it possible for banks to prevent fraud before it happens rather than waiting for a problem to arise and resolving it.

Credit decisions

Humans have traditionally evaluated credit histories, scores, and other financial actions when making credit choices. Banks frequently lose money as a result of incomplete or inaccurate data brought about by human mistake because this human-led process isn’t always precise or reliable.

Machine learning can be of assistance. It can be used by financial institutions to quickly and easily analyse data from customers looking to apply for a new credit card, borrow money, apply for a loan or mortgage, etc., and compare the information to typical consumer behaviours and market trends to assess the risk and/or benefit of offering the customer credit or a loan.

The analysis, processing, and application of credit decisions using machine learning provides banks with a more complete picture of risk and potential.

Banks may make safer and wiser business decisions by utilising machine learning to assess, process, and apply credit decisions. This gives them a more complete picture of risk and potential return on investment for their clients.
Process automation

Robotic Process Automation is a process of simplifying a task by integrating machines or other processes to reduce the need for human labor. In banking, automation can be used to reduce the manual data entry and make banking easier.

Financial organisations can reduce the need for human data entry and process intervention, free up workers’ time from repetitive duties so they can concentrate on client-focused work, and enhance customer interactions and experiences by integrating machine learning into banking operations. Robotic Process Automation can be used to automate back-office processes like account maintenance, reconciliation, and closing, credit card and mortgage processing, trial balancing, and more.