Aditya Kondawar@aditya_kondawar
Rajeev Jain, CEO, Bajaj Finance, on Q3 Concall on the Impact of AI –
“AI listened to 2 Cr calls, converted voice to text, and gave us data. Text-to-data conversion happened for 5.2 lakh customers. As a result, we generated 100,000 new offers for which we did not have information earlier.
“This capability did not exist in Q1 and Q2. It just got deployed. We’ll be able to listen to 100 million calls next year,” said Jain. He added that loan disbursements through AI-powered call centres stood at about Rs 1,600 crore. That’s ~ 10% of the Rs 16,545 Cr of disbursals in Q3FY26
Data converting -- data from those calls led to another INR 325 crores of volumes. So, this is just our first attempt.
Over the next six months, Bajaj Finance plans to invest heavily in its agents. The company expects to have more than 800 autonomous agents across sales, operations, HR, IT, risk, and DMS in the next fiscal.
Similarly, in terms of 100% of videos are now generated by us using AI, 100% of banners are generated using AI, 2.7 lakh videos were generated, and 1.2 lakh banners were generated. At the customer engagement level, we have 11 AI text BOTs that are live that engage with the customer. So rather than sending dumb SMSs for 11 products now, we have an AI text BOT, which allows you to engage, interact, and respond to your queries.
The company has 26 products. All 26 will be live between April and May'26. So, there will be no communication that we'll be sending, which will not have a -- whether service or sales, which will not have a conversational BOT embedded in it.
At the branch and point of sale, existing customers face match that we're doing, we did 46 million face matches to ensure this is the same customer, if it's an ETB customer who had actually principally come in, giving us much better control over identity.
Customer onboarding in terms of document -- ensuring that auto-fill of the document happens, whether it's a PAN card or an Aadhaar. There are 43 such documents that the company has now mapped, which an image extracts with a 95% - 96% accuracy and populate data in our platforms, delivering significant productivity for our employees.
Auto quality check of documents is now 41%. As we sharpen the model, it will take us to between 85% and 90% over a period of the next 15-odd months
On technology development, we are clearly seeing between 25% - 45% efficiencies emerging in terms of the development process, depending on whether it's a legacy platform, then the benefit is much lower, or rather, I would say, none. But if it's a digital infrastructure, then the efficiencies can be as high as 45% - 47%. So significant work is being done.
Src – Q3 Concall, no reco
