Companies use NLP-based sentiment analysis to source intelligence

For example, after the Reserve Bank of India (RBI) lifted the moratorium on loan repayment, banks and non-banking financial companies (NBFCs) had to deal with a backlog of pending loans. To expedite the process, many NBFCs and banks started adopting Natural Language Processing (NLP) based solutions to assess the sentiment of the borrower through their interactions.

One example is Credgenics’ sentiment analysis tool, which uses automated voice bots and WhatsApp bots to generate insights about borrowers, using speech recognition and analysis of chats. This has allowed NBFCs and banks to identify the problems that borrowers are facing in paying off loans. CredGenix claims that over 60 lending institutions including ICICI Bank, Axis Bank and IDFC First Bank are using its sentiment analysis-based software-as-a-service (SaaS) platform.

“This has allowed them to plan the communication strategy and channel for lenders for optimum results,” said Anand Agarwal, Co-Founder and Chief Technology Officer, CredGenix. He added that sentiment analysis is used to determine the sentiment of a borrower. Helps extract subjective meaning from text.It is an ideal tool for reviewing unstructured material about a particular borrower’s digital communications for insight.

According to Agarwal, sentiment analysis has enabled lending institutions to improve their debt recovery rates by 15-20% and recover 70-95% of their bad loans.

Sentiment analysis is also helping companies stop attrition. Firms are using these tools to identify employees who can leave, and retain them with perks, pay increases, and a better work environment. Tanmay Jain, founder and chief executive of Infido, a SaaS firm that provides sentiment analysis tools to over 200 companies including Samsung, Airtel, said, “We have been able to retain customers 85% of our top talent (using sentiment analysis). have seen.” Xiaomi and Lenovo in India.

Jain said one of the clients in India, a large unnamed enterprise with more than 3,000 employees, was struggling to retain employees after a major merger with another company. After deploying a sentiment analysis chatbot, the company was in a better position to estimate employee sentiment and managed to increase its retention rate by over 10%.

Infido’s AI chatbot provides insights based on interactions with employees. The bot uses NLP to understand the context and identify employees who seem to have missed out and are more likely to leave.

The use of sentiment analysis is not entirely new. Previously, the use of NLP for sentiment analysis was limited to tech giants such as Google and Amazon, which had more data and AI and ML engineers to conduct experiments.

Among Indian companies, e-commerce firms like Flipkart had adopted it to understand customer sentiment by analyzing user reviews using NLP.

NLP, a subset of AI, allows a piece of software to read, understand, and receive context in text and spoken words, just like humans. It can be used in any area where human interaction is involved. Before NLP, most AI-based chatbots operated and responded within a defined range of a fixed set of questions and answers.

Sentiment analysis has been around for years, but interest in it is now increasing among many firms as the underlying NLP technology becomes much more mature. “What has changed is that NLP and sentiment analysis are becoming much more mature in terms of accuracy, readiness, and more,” said Jayant Kolla, co-founder, market researcher Convergence Catalyst.

He said that the talent pool of people working on it has increased in recent times, which has led to more adoption.

According to Kolla, the demand for sentiment analysis has increased since the pandemic. He notes that a lot of HR tech firms are using sentiment analysis to read chatter on platforms like LinkedIn and Glassdoor to rank companies.

For Infido, the demand has increased 3 times since the pandemic. Jain said, “Earlier, with employees on campus, it was easy to understand employee sentiment, but with hybrid and remote working, and with the fatigue of video conferencing, it was difficult for leaders to understand their employee sentiment. Jain said.

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