Transforming banking with Gen AI: More precise loan and investment offers for customers on the way

Are you tired of seeing repetitive or irrelevant personal loan or credit card recommendations every time you open your banking app? New Delhi-based Newgen Software has a solution to help banks accelerate hyper-personalisation!
Newgen provides banks and institutions with Gen AI solutions that create hyper-personalised recommendations. This innovative tool is for bank marketing teams, retail banking departments, and data scientists.

LumYn, a newly launched product by Newgen, boasts clients like Union Bank of India, IndusInd Bank, and ING Vysya Bank among others. It is a hyper-personalised generative AI model for banks to provide tailored services to consumers. Newgen claims that its model promises personalised service and customised banking solutions fine-tuned to individual preferences. Unlike previous models based on age or account balance, the AI will consider an individual’s preferences, spending habits, and behaviours to recommend options the consumer can choose from. “It could be more about your behavioural elements like what you spend on, what kind of products you relate to, and things like where you are travelling because that’s transactional data. There are another 200 behavioural elements you can extract from this data,” says Virender Jeet, CEO of Newgen Software.

“The experience between two end-users will change depending on what is being recommended to them, based on their profile. No two customers or even similar customers will be offered the same things. They may have very different offers, highly hyper-personalised to them,” says Jeet. “We are providing what consumers get offered as products, either to improve the quality of their service. So, we are working with the enterprise users of the bank to service them,” he adds.
The advanced conversational AI capabilities of LumYn will enable banks to build a hyper-personalized understanding of each customer’s needs, behaviour, preferences, and intent to drive deeper engagements while ensuring data privacy and security. The AI model will work on banking data, especially transactional information, transaction history, and account history of a customer.

The company claims that there is no bias in its recommendations. In fact, the problems of bias are more significant in credit underwriting because models rely on historical trends of what has happened. “Our generative AI is about engaging and converting natural language into a theory internally which can be understood by the system. The bias question is a bigger issue in credit scores and underwriting, not for recommendation engines,” says Jeet.

“What we are seeing today is the result of 70% development over multiple years,” says Jeet. The company’s AI ML platform, the Asian Data Sciences platform, has been built over the last five years. “Our other technology components, just servicing a product platform, have been developed over the last 20 years. The recent addition, combining and stitching this product together with generative AI capabilities, has been developed over the last 9 to 12 months,” says Jeet.