The buzzword ‘Artificial Intelligence’ was a privilege that accompanied the fourth industrial revolution. Since its inception, it has created a roar in technology. The caliber of artificial intelligence is seen in the advancement of many industries. From increasing sustainability to automating human tasks, the application of artificial intelligence is considerably large, with no exception in banking. Let’s take a peek into how artificial intelligence can refashion the banking industry.
1. Improving customer service with chatbots
A massive chunk of banking involves customer service. While hiring personnel can be a huge investment for the task, chatbots can be a great alternative to human resources. They can resolve queries like answering FAQs(Frequently Asked Questions), providing timely notifications and reminders, assisting with money transfers, providing account details, and a lot more.
2. Detecting fraud
In 2022, RBI reported a banking fraud of 604 billion rupees. While the magnitude of fraud was less than the previous year, it is still a substantial amount. The involvement of Artificial intelligence is the right call when it comes to automating fraud detection. Many Banks and NBFCs have already invested in R&D for cybersecurity. For instance, developers at JPMorgan Chase created a warning system using Artificial intelligence and Deep learning systems to detect malware and trojans. The system would provide a warning before the actual attack takes place and mitigates the risk of fraud in the organisation.
3. Enhancing customer experience
Artificial Intelligence-based BFSI is a surefire way to enhance the customer experience by evaluating consumers’ needs and customising a suitable strategy. The quality experience will drive customer engagement and influence them to explore more banking services. Automating onboarding and evaluation procedures by software like DMS(Decision Management System) and KYC(Know Your Customer) will provide a seamless experience for the customer and an area for the banks to cut operational costs.
4. Automating credit scoring
Credit scoring requires a lot of complex data in order to evaluate the creditworthiness of the person seeking a loan. Metrics like credit history, income, bank statements, the purpose of the loan, work experience, and many more are to be considered. Using an AI-based credit scoring model helps analyse data that can be complex for an individual. Hence, implementing lending systems backed by solid technology can significantly contribute to minimising the lender’s risk.
To conclude, using more technology to lessen the standard procedures ensures higher productivity. It can help redirect the workforce to function more in areas that cannot be replaced by automation and artificial intelligence.