Digital consumer financing fintech Fibe is planning to raise ₹750 crore primarily to strengthen its lending subsidiary and for general corporate purposes. According to reports from The Hindu BusinessLine, the company aims to enhance its AI-driven digital lending platform and strengthen operations across India. The fundraise will focus on further strengthening technology and artificial intelligence capabilities to build a scalable digital lending platform.
Fibe's technology-led operating model has delivered impressive growth metrics. As reported by The Hindu BusinessLine, the company achieved a 45% compounded annual growth rate in AUM to ₹8,603 crore as of March 2026, compared to ₹4,064 crore as of March 31, 2024. The company's profit for FY26 increased significantly to ₹257 crore from ₹114 crore in FY25. During FY26, Fibe's technology ecosystem processed approximately 1.31 million loan applications every month, resulting in fresh disbursals of ₹7,614 crore.
The company plans to further integrate AI across multiple operational areas including customer engagement, risk assessment, credit decisioning, portfolio management and employee productivity. According to The Hindu BusinessLine, Fibe's technology-led operating model supports multiple lending products and distribution channels across India through its mobile application and web interface. The company's technology ecosystem includes Fibe Mind, which enables management to access business information and monitor trends, and an AI engineering agent that assists technology teams in software development activities.
During FY26, Fibe's platform managed 15.74 million unique applicants who applied for their first loan on the platform. As reported by The Hindu BusinessLine, of these unique applicants, 2.02 million were approved through Fibe's credit assessment process. The company's proprietary scorecard evaluates parameters such as internal policy checks, credit bureau data, income estimation models and behavioural indicators. Customers provide additional information including income, demographic, residential and employment details as part of the AI and ML-enabled credit assessment process.
