Loan applications have become popular for managing loans through mobile devices, offering convenience and personalized loan offers. However, there are concerns about bad actors who borrow without paying back. Scam baiting, a technique for overcoming these difficulties, entails luring users into participating in activities that reveal their illegal activities and then, turning them over to the authority. In this paper, the goal was to create a secure Progressive Web Application (PWA) mobile application that uses scam baiting methods to discover bad actors. This study focuses on creating a secure loan application using a framework that identifies and exposes loan defaulters. Agile extended programming, which allowed for flexibility and adaptability during the development process, was the methodology used in this study. The researcher used the magnetic honeypot technique, which entails constructing a target that is alluring to them. Zero loan credit rate and lack of a credit history was used in this regard. The Loan Bureau API was used to confirm the user's status, while the Youverify API was used to ensure the authenticity of the user. Ten people were chosen at random to take part in the loan application Test. 7 of the participants were discovered by the system to have previously borrowed from other loan applications, while 3 individuals were confirmed as trustworthy borrowers with no prior history of fraudulent loan activity. This distinction confirmed the framework's accuracy in separating legitimate borrowers from prospective con artists. This outcome illustrated the scam-baiting framework's effectiveness in locating persistent loan defaulters.

Keywords: Scam baiting, Framework, Loan Application, Bad Actors

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