How is Artificial Intelligence Advancing Banking Domain?
In recent years, we can witness that artificial intelligence is becoming a need in every domain of the industry, and AI’s different domains, such as computer vision, natural language processing, and predictive modelling, are helping humans solve their use cases and problems more effectively and without the intervention of the humans. We can also enjoy the intervention of AI in our daily life, and humans are becoming more curious about this intervention. Banking sectors are also positively affected by the intervention of AI. In this article, we will cover some of the critical use cases of AI in the banking sector that is helping humans advance the banking sector.
This sector implies AI-enabled models to assist the customer during onboarding. These models are trained to perform step-by-step processes of customer onboarding. Natural conversation utilises digital channels. Bots are there to automate the services like processing the documents of customers and taking images from computer vision programs.
Some other vast implementations of OCR systems can be found to process the documents of the customer, and a variety of systems are there that help in performing document verifications of customers to make the customer onboarding faster and smooth. Since no human intervention is required, fewer human skills are necessary. It also helps the service provider to prevent their human workforce from performing repetitive and sometimes mundane tasks inside the premises.
WeChat messenger is an excellent example of engaging customers by utilising conversations. For instance, China Merchant Bank is one of the largest credit card companies using WeChat messenger to handle 1.5 to 2 million customers.
In a banking system, customer care, service and engagement programs play a crucial role in democratising the bank’s services among the customers and also help in enhancing sales and marketing of banking services. Nowadays, it is found that chatbots have replaced humans in customer care, and this also impacted the banking sector. While applying chatbots in such systems delivers a very high ROI in cost savings.
Embedding chatbots in the banking sector can be considered the most common application of AI in the banking sector. There are various tasks such as balance inquiry, statement production, and fund transfer that can be managed using these chatbots and applying such chatbots helps in reducing the overload of the channels like contact centres and internet banking channels.
HDFC bank’s chatbot named EVA is an excellent example of this use case where we can consider it as the banking assistance for the customers of HDFC and helping them with things like Branch addresses, IFSC codes, loan and interest rate information.
In traditional banking and finance systems, we find that banks employ humans to advise the customers and clients regarding investments, loaning, credit cards and debit card schemes. However, as the number of customers and their requirements increases, it becomes challenging for humans to manage such big data and provide beneficial advice to everyone.
Artificial intelligence and data science intervention in banking and finance systems have made this task very easy. AI-enabled models are being developed and prepared to perform automatic advice procedures with a touch of personalisation for everyone. These models are learned to give the best advice to customers according to the requirements, and that makes them a personalised advice system in banking. Also, they are beneficial in reducing the time of the banking procedures.
For example, ICICI banks in India blended an RPA( robotic process automation) with their systems and successfully cut loan processing time in half.
One of the most common use cases of artificial intelligence is to perform predictive analysis and modelling using the data. These modelling procedures find the correlation between the data points and variables and tell about the future possibilities.
