Artificial Intelligence (AI) has the potential to transform the way we approach lending. One particular area where AI is poised to make a significant impact is the area of embedded lending. When combined with generative AI, this type of lending can create more personalised lending experiences for borrowers while providing lenders with more accurate risk assessments.
The age of hyper-personalisation
One significant advantage of generative AI in embedded lending is its ability to create highly personalised lending experiences. By analysing vast amounts of user data and contextual information, generative AI can develop credit solutions tailored to each borrower’s unique financial situation, preferences, and needs.
Imagine an experience that is the same as talking to ChatGPT through which you’ll submit the necessary information, and you’ll get exactly the credit product you need — one that you might’ve not known about even before! This benefits not only borrowers who get more personalised credit options but also lenders who can better manage risk and improve their lending portfolio’s quality.
With the current technology available, it’s probably already possible. Why it’s not is because the truly talented AI specialists aim to work in leading AI tech companies like OpenAI. And ones that also understand the intricacies of lending and risk are even rarer.
Also Read: How embedded lending will drive healthy growth in credit in Indonesia
But to effectively integrate AI technologies into lending services, we need professionals who can navigate the nuances of risk management. So it might be a case that the resources that can execute still for a long time will be occupied with solving problems in other fields.
Another significant benefit of generative AI in embedded lending is its ability to integrate unique platform data into credit models. With the vast amount of platform data available from social media, e-commerce, and ride-sharing platforms, generative AI can analyse and develop credit models tailored to specific platform data.
This leads to more accurate risk assessments, improved risk management, and higher customer satisfaction. This has been already happening for the past few years and is not getting advanced latest developments in generative AI, rather just good old statistics (which is cooler to call AI lately).
Generative AI in embedded lending can also help address issues of bias and discrimination in lending. Traditional lending models have been criticised for being biased towards certain demographics, such as race or gender, resulting in unfair lending practices. By using generative AI to analyse a broader range of data points and factors, lenders can create more accurate and fairer credit models.
Furthermore, generative AI can help identify any patterns of bias in lending data, allowing lenders to adjust their models accordingly and ensure fairness in their lending practices. As AI technology continues to advance, we can expect to see even more innovative ways of using it to create a more equitable lending landscape for everyone.
Final thoughts
The latest developments in generative AI will bring some significant developments to the lending industry. It holds the potential to make embedded lending especially even more personalised than it already is. Some improvements have already been happening, some will come later, for us all it’ll be a great show of what’s possible.
–
Editor’s note: e27 aims to foster thought leadership by publishing views from the community. Share your opinion by submitting an article, video, podcast, or infographic
Join our e27 Telegram group, FB community, or like the e27 Facebook page
Image credit: Canva Pro
The post How will generative AI advance embedded lending appeared first on e27.