With technology improving itself each day, the banking and financial sector is constantly evolving to learn and improve their level of services. The companies in this sector are increasingly taking advantage of machine learning in order to provide improved services and security for their customers.
What is machine learning and how is it being used in customer support?
Machine learning in customer service is used to provide a higher level of convenience for customers and efficiency for support agents. Over the last few years, customer-centric enterprises have been using chat systsems powered by machine learning to understand and serve their customers better. Due to these advanced conversational support tools, banking organizations are able to:
- Interpret pre-existing customer data
- Improve the current user experience
- Reduce any chances of risk and fraud
According to predictive research done by Autonomous Next, it is said that nearly 22% of cost-saving measures will be adopted by banks all over the world by the year 2030. The study also claimed that around 60% of professionals hired by banks would specialize in the creation of AI systems.
Although banks and financial institutions like HDFC had begun the usage of digital services as early as 2004, it wasn’t until the financial crisis in 2008 that led to the full-scale adoption of machine learning. This was majorly done in order to deal with the large volume of customer data analysis to understand their usage behavior. It was also seen that customer data analysis was helpful for the banking industry to be able to handle multiple channels of customer requirements as well as prediction of customer behavior, fraud and risk detection, marketing and customer support among many other related fields.
Usually, machine learning in banks is employed in three main areas:
- Personalized service
- Increased human to machine conversation
- Secure and automatic actions as asked by customers
Since there is an increased need for banks to be aware of the different ways their customers spend their money, it is important to have a system that can understand the different patterns and be able to suggest solutions irrespective of the media they are accessing through.
Increases human to machine conversation
Due to the increased use of machine learning in the BFSI sector, there has been a change in the way a bank or a financial institution is able to go through customer requests faster than before. This has also led to some exceptional impressions:
- Through machine learning, banks are able to understand a customer’s profile and take decisions after proper appraisal of the borrower’s history and then grant loans.
- Using machine learning helps in reducing the hurdles involved in areas like keeping a check on transactions, expense handling and future projects related to the data collected in an on-going financial year.
Secure and automatic actions as asked by customers
With today’s highly digitized world, any financial institution including banks in the BFSI sector needs to possess the power of machine learning in order to provide potential customers with the constantly progressive technology.
This is why KaptureChat’s conversational AI is a great choice for all your business needs. The seamless integration, easy to use implementation combined with round the clock support and the ease of creating contextual conversations should be enough to give your business and clients a clear choice for better results.
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