Pune based consumer lending startup Fibe is exploring generative AI applications in customer service and risk assessment. It recently released a chatbot supported by LLMs via Amazon Bedrock, which has improved its customer support efficiency by 30%.
At AWS Fintech Forum held in Bengaluru earlier this month, AIM caught up with Anil Sinha, chief technology officer of Fibe. He said that the team finds Anthropic’s Claude 3, hosted on Bedrock, most useful for their work. The company also employs Amazon Comprehend for natural language processing, which is used to analyse sentiment and quality in customer calls.
Amazon Bedrock provides the choice of top-notch models from both Amazon’s own first-party offerings (like Amazon Titan)and various third-party models. This includes families of foundational models from AI-focused companies like Meta, AI21 Labs, Anthropic, Cohere, AI21, Mistral, Stability AI, and more. Last year, Amazon invested $4 billion in Anthropic.
“The investments made with Anthropic and others is helping us to bring this choice to customers, but then customers can also use open source models with Hugging Face set up on SageMaker”, Pandurang Nayak, head of startup solutions architects AWS India, told AIM at the event.
Fibe’s Growth Story
“We have been using AWS for the past seven to eight years now which went from a single-product offering to a diverse portfolio that now includes personal loans, embedded finance, and more,” Sinha told AIM. He said that this expansion was facilitated by AWS’ robust infrastructure, which provided the scalability and security necessary to manage increased demand and complexity.
Fibe primarily caters to young salaried professionals and has been able to disburse more than six million loans worth ₹20,000 crore in near real-time since its inception. The startup leverages AWS ML services to streamline KYC processing, employing features such as optical character recognition, face match, and selfie deduplication.
Along similar lines, it has developed FibeShield, a proprietary algorithm-based product, crafted with AWS tools such as Amazon Neptune, AWS Lambda, and Amazon S3. FibeShield uses graph ML, device fingerprinting, and geo-fencing to effectively identify fraud by revealing hidden connections and duplications among users.
“AWS credits and the Activate program have been helpful in providing us with resources to experiment freely from the time we started out. The support from AWS, including technical advice and regulatory guidance, has enabled us to focus more on innovation rather than infrastructure management,” added Sinha.
Since its inception in 2013, AWS Activate has provided $6 billion in credits to startups around the globe to help them build solutions in the cloud.
Why Enterprises Choose AWS
Besides Fibe, Nayak highlighted several success stories demonstrating the scalability AWS offers. “One such success story is with EaseBuzz, a Pune-based company that grew fourfold in two years using AWS Spot Instances, optimising their infrastructure costs while scaling their operations massively,” he added.
Another example is fintech startup Ring, whose data science team has been using its tools like Amazon Rekognition and Amazon Textract to process customer data rapidly, reducing its NPA by almost 20-25% and improving overall collection efficiency by 30%. Its loan application processing times have also halved.
“We’ve always worked backwards from what our customer needs are. In fact, 90% of the features in our products are developed from customer feedback, while the remaining 10% come from developments like AWS Lambda. We maintain this approach as part of our culture, continuously adapting to what customers need and offering them the flexibility to use technology in various ways,” added Nayak.
Other startups include Yubi, inVOID, Decentro, Setu, PayU, Fibe, and so on.
AWS has been instrumental in shaping early-stage startups through programs like the AWS Activate Program, Startup Architecture Challenge, AccelerateHer Program, Public Sector Startup Ramp, and more.
Looking ahead, AWS is committed to continuing its support for startups by enhancing its service offerings and reducing the “undifferentiated heavy lifting” that often slows down innovation. The goal is to allow startups to focus more on their core products and customer experiences rather than on managing infrastructure.
