Prime Highlights-
- Consint.AI raises Rs 22 crore Series A funding to expand its AI-driven fraud detection platform globally.
- Healthcare AI startup builds a foundational model to tackle fraud, waste and abuse across sectors.
Key Facts-
- Consint.AI has assessed over 100 million transactions and flagged more than Rs 1,000 crore in fraud using 500-plus AI models.
- Startup plans expansion across India, Middle East, Africa, United States and Southeast Asia with new AI-based solutions.
Background-
Healthcare and insurance risk management startup Consint.AI has raised Rs 22 crore ($2.3 million) in a Series A funding round led by BIG Global Investment JSC, Equanimity Ventures Trust II and Seafund Venture India Scheme I.
The deeptech company plans to use the proceeds to expand its global footprint, strengthen its artificial intelligence research capabilities and enhance enterprise delivery operations.
Founded by Ashish Chaturvedi, Consint.AI builds AI-driven platforms that help insurers, healthcare providers and financial institutions detect fraud, waste and abuse.
The company has evaluated more than 100 million transactions and uncovered over Rs 1,000 crore in fraudulent activity, drawing on a suite of 500-plus AI and machine learning models, fine-tuned large language models and more than 500 digitised clinical protocols.
Consint.AI operates across claims processing, document forensics and clinical intelligence, offering solutions that support risk assessment in healthcare, insurance, banking and financial services. With the fresh capital, the firm plans to build a foundational model that improves fraud detection across these industries.
Consint.AI plans to widen its reach across India, the Middle East, Africa, the United States and Southeast Asia. It also aims to add new AI-based healthcare and insurance solutions to its existing platform, reinforcing its position in the risk management space.
Consint.AI expects the foundational model to help insurers, healthcare providers and government health programmes detect fraud more accurately and at scale.
