Abstract: This paper proposes a solution of the lost item and finder known as FindBack. It is an integrated of AI matching system with lost-and-found digital platform aimed to improve the efficiency of recovering lost items through a centralized and intelligent system. By using traditional methods like social media postings and manual reporting are usually scattered in many places, time consuming and inefficient. FindBack addresses the issues by implementing machine learning-based image matching, real-time notifications and an important security measure in communication features to connect item owners and item finders. The study adopts Design Thinking methodology supported by Business Model Canvas (BMC) and Value Proposition Canvas (VPC) to develop a platform that work for multi-sided customer segments. This platform delivers value by reducing recovery time of lost item, minimizing user stress, and increasing the chance of retrieving the item to its rightful owner while offering rewards for finders. The platform aligns with global and national initiatives, including Sustainable Development Goals which are SDG 9 and SDG 11, the Malaysia Digital Economy Blueprint (MyDigital) and the National 4IR Policy in which utilizing and focusing on digital innovation and smart services in national industry. In addition, the proposed model demonstrates strong potential for sustainable profitability through scalable revenue streams such as subscription fees, premium services, and commission-based rewards. The findings shows that FindBack platform has potential to enhance urban digital ecosystems and contribute to Malaysia digital economy growth.
Keywords: Lost-and-Found System, Artificial Intelligence, Multi-Sided Platform, Digital Economy, National Policies.
Title: FindBack Conceptual Business Model: AI-Powered Lost-and-Found Digital Platform
Author: Muhammad Haikal Faiq bin Hairulhuda, Muhammad Harith Syafi bin Jasmi, Muhammad Hannan bin Rozlan, Abdul Rahman bin Ahmad Dahlan
International Journal of Computer Science and Information Technology Research
ISSN 2348-1196 (print), ISSN 2348-120X (online)
Vol. 14, Issue 2, April 2026 - June 2026
Page No: 188-202
Research Publish Journals
Website: www.researchpublish.com
Published Date: 01-June-2026
