Investify - No Sharks Required
Authors
Anagha Burki S
Dayananda Sagar Academy of Technology and Management Bengaluru, India (IN)
Divya HN
Dayananda Sagar Academy of Technology and Management Bengaluru, India (IN)
Bhavatharani S
Dayananda Sagar Academy of Technology and Management Bengaluru, India (IN)
Devyash Jangid
Dayananda Sagar Academy of Technology and Management Bengaluru, India (IN)
Himanshu KM
Dayananda Sagar Academy of Technology and Management Bengaluru, India (IN)
Article Information
DOI: 10.51583/IJLTEMAS.2025.140600009
Subject Category: Artificial Intelligence and Machine Learning
Volume/Issue: 14/6 | Page No: 70-74
Publication Timeline
Submitted: 2025-07-04
Published: 2025-07-04
Abstract
Abstract - The integration of machine learning in investment matchmaking has the potential to democratize access to funding by efficiently connecting small-scale businesses with investors. Traditional investment processes rely heavily on manual networking and subjective decision-making, often excluding promising startups due to limited outreach or investor bias. Investify, a machine learning powered business investor matchmaking platform, seeks to bridge this gap. By leveraging AI-driven recommendation algorithms, the platform enables businesses to submit detailed proposals, while investors define preferences such as budget, industry, and risk appetite. The system then ranks and matches businesses with investors based on compatibility metrics, streamlining the funding process. Additionally, built-in communication and negotiation tools facilitate investor-business interactions, further enhancing decision-making. This literature survey explores existing research on AI-driven matchmaking systems, recommendation algorithms, and investment decision-making frameworks to provide insights into the effectiveness and potential of such a platform.
Keywords
Business-Investor Matchmaking, Machine Learning (ML), Recommendation System, , Investment Decision-Making, Risk Appetite, Proposal Evaluation, Negotiation Platform
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References
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