Abstract
The Alertify App is an innovative fraud detection system designed to tackle the growing problem of financial fraud, particularly in digital transactions and receipt management. With 5-7% of organizational expenses often lost to fraudulent activities, the need for effective solutions is more urgent than ever. The app helps identify and prevent common types of fraud, such as duplicate claims, altered receipts, and inflated expenses, which can lead to significant financial losses for both businesses and individuals. The development of Alertify followed a clear, step-by-step process, starting with in-depth research to understand the challenges users face when detecting fraud. The app integrates advanced technologies like Optical Character Recognition (OCR) for extracting text, machine learning for recognizing patterns, and fuzzy matching to spot discrepancies in receipt details. These tools allow for real-time fraud detection, making the process faster and more accurate. User feedback was crucial in shaping the app’s features, with many highlighting the importance of real-time fraud alerts, secure data protection, and ease of use. In fact, 57.1% of users considered fraud alerts to be the most important feature, while 28.6% focused on the security of their profile data. Designed to be intuitive, Alertify can be used by a wide range of users, from frequent shoppers to small businesses and finance professionals. However, the app is not without its challenges. Issues such as OCR inaccuracies, the need for large datasets to train machine learning models, and potential scalability concerns as transaction volumes grow need to be addressed. To ensure its success, the team plans to test the app with real users and refine it based on their feedback. Ultimately, Alertify App offers a promising solution to help users protect themselves from the growing threat of digital financial fraud.
Metadata
| Item Type: | Book Section |
|---|---|
| Creators: | Creators Email / ID Num. Azharie, Alif Zikri UNSPECIFIED Abdul Hamid, Husna Firzanah UNSPECIFIED Sofian, Nor Fatehah UNSPECIFIED Abu Bakar, Rubiah UNSPECIFIED |
| Contributors: | Contribution Name Email / ID Num. Advisor Zainodin @ Zainuddin, Aznilinda 314217 |
| Subjects: | H Social Sciences > HF Commerce > Electronic commerce T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Scanning systems |
| Divisions: | Universiti Teknologi MARA, Johor > Pasir Gudang Campus > College of Engineering |
| Series Name: | International Tinker Innovation & Entrepreneurship Challenge |
| Number: | 2nd |
| Page Range: | pp. 64-68 |
| Keywords: | Alertify application, Fraud detection, OCR, Receipt, Duplicate |
| Date: | 2025 |
| URI: | https://ir.uitm.edu.my/id/eprint/118573 |
