How leading fintech player RING reaches the economically underserved with AWS

The credit loan industry has witnessed a strong rise in recent years. According to CRIF High Mark’s report titled “How India Lends”, India’s lending market grew to Rs 174.3 lakh crore by March 2022, up 11.1 percent year-on-year.

Several players have emerged in this area to cater to the new age digital consumer. Leading the pack is RINGa transaction credit app launched by Kissht.

Ranveer Singhfounder and CEO, Kissht | RING; Krishnan Vishwanathan, Founder and CEO, Kissht | RING; Karan Mehta, Founder & CTO, Kissht | CALL and Sonali Jindal, Founder & COO, Kissht | RING is at the forefront of construction solutions that offer immediate online credit with one click.

Karan and Sonali talked about the vision and mission of their startup, the challenges they’ve faced along the way, and how their collaboration with AWS has helped them scale and innovate.

The mission of RING

On RINGis the mission to reach out to the section that has not yet received the benefits of digital financial services.

“When we look at the penetration in this segment, whether financial services are offered or not – it is as low as 5 percent. Therefore, we want to cover these 150 million millennials through a simplified product offering, which is a combination of payments and credit,” shared Sonali, adding that their idea is to provide a simple, analytically-led digital experience.

All it takes is less than a minute for a customer to download the app and get the loan.

The journey from then to now

Sonali and Karan started using AI/ML solutions for their lending platform Kissht and brought these solutions to RING.

In their early days, Kissht focused on a traditional warranty model, where they looked at a selected set of data. In the past five years, they have served around 5 million customers and paid out loans to 3 million customers.

“Given the customer data we have, all of our underwriting models at RING are now geared towards using artificial intelligence and machine learning in our day-to-day decision-making. For us, technology and analytics cut across both our core and non-core offerings , and that’s where we bring a big difference to the table,” explained Sonali.

For RING, the biggest challenge was to make use of a large set of customers who did not have a previous CIBIL background. If so, how did they guarantee and rate customers who had no credit history?

Karan replies, “The good thing is that today we have a rich digital footprint of customers. Most of our transactions are online, which is enough to consider a customer even if they haven’t taken credit before applying to RING.”

Today, their warranty and fraud models are driven by machine learning. At first it was all a bit daunting, as they assumed that a large team of experts would be needed. Over time, their exposure to a variety of services through Amazon made the journey far more seamless.

Leverage the power of AWS

RING relies heavily on Amazon Sagemaker. When they adopted the service, they realized that much of the heavy lifting had already been done. Most of the time, exercise machines start much later – the first few months are spent setting up an infrastructure, but none of that had to be done here.

“Our credit and fraud engines make use of Amazon Sagemaker in a big way. We have teams building, testing and deploying models, all using Sagemaker and Sagemaker Studio, which fulfills our primary purpose. RINGits credit engine has been trained on over 14 million loans, and the fraud engine has verified 20 million customers. We can differentiate a risk accurately and can predict in advance how a particular month’s performance is going to be, at the time of payout,” explained Karan.

To be able to extract printed text from important documents, the team uses RING Amazon Text Extract. This helps to automatically extract printed text, handwriting and data from any document. While there were clear models available for Aadhar and PAN Card, the other documents were processed with this service.

AWS recognition is another ground-breaking solution that helps RING to identify customers and their photos.

“We have use cases that allow us to do 1:N face deduping, blur detection, or just make sure the image is accurate, not a photocopy. We also make sure it’s not an image of a device screen,” added Karan.

They are currently testing several use cases involving voice detection to understand the customer’s tone when calling the support team.

Business impact

There are five areas where AWS has impacted the business, Sonali revealed. Firstly, it has reduced the NPAs by 20 per cent to 25 per cent. The second is from a retention point of view, where RING has scored upwards of 90 percent cycle-on-cycle for 16-18 months.

“When I look at customer engagement, or customer service, our ability to say this is the set of customers I should serve and these are the customers who can wait makes a difference. Also, customer service wait times have gone down by 50 percent.” , she shared, adding that debt collection crime has also reduced by 20-25 per cent.

The way forward…

RING’s vision is to offer an app that is fast and well designed, and which at the same time shows the right options at the right time.

“If a customer is outside a store, show them a QR code scanner. If they’re at home shopping online, show them the right online payment options. To be able to achieve these use cases, you need ML,” concluded Karan.