Bankruptcy Prediction System / Mohamad Aizuddin Mohamad Rafee … [et al.]

Mohamad Rafee, Mohamad Aizuddin and Abd Halim, Khairul Nizam and Mohd Shukri, Mohd Shamil and Samsudin, Muhamad Nur Firdaus (2021) Bankruptcy Prediction System / Mohamad Aizuddin Mohamad Rafee … [et al.]. In: International Jasin Multimedia and Computer Science Invention and Innovation Exhibition (i-JaMCSIIX 2021). International Jasin Multimedia and Computer Science Invention and Innovation Exhibition, 4 . Faculty of Computer and Mathematical Sciences, Jasin, p. 24.

Abstract

A company often takes bankruptcy prevention measures. This action is critical to ensure the viability of their business. Preventive activity refers to the activity of predicting and analysing the probability of bankruptcy. This process is challenging because it requires expert, specialized techniques, takes a long time, and high cost. Here is introduced a novel product called Bankruptcy Prediction System (BPS). It is built using Artificial Intelligence technology with Random Forest techniques. It is more practical than the existing way to predict and measure the probability of bankruptcy for a company quickly, without using experts, and producing reports automatically without engaging in tedious and complicated statistical calculation work. BPS has been registered under MyIPO under the copyright domain. BPS has a high commercial value to be marketed to all companies that want to prevent bankruptcy and all finance companies that want to predict a company’s ability to repay its loans. BPS has been tested using a set of data collected from the Emerging Markets Information Service (EMIS), a database containing information on emerging markets worldwide to build a classification model. According to the tests’ findings, BPS ensures the accuracy of the bankruptcy predictions up to 90% and beyond. BPS is flexible and able to customizable with different data and users. BPS is beyond the prototype as it provides a better technique to predict the company’s status toward bankruptcy and ready to penetrate the market.

Metadata

Item Type: Book Section
Creators:
Creators
Email / ID Num.
Mohamad Rafee, Mohamad Aizuddin
UNSPECIFIED
Abd Halim, Khairul Nizam
UNSPECIFIED
Mohd Shukri, Mohd Shamil
UNSPECIFIED
Samsudin, Muhamad Nur Firdaus
muhdnurfirdaus.samsudin@gmail.com
Subjects: Q Science > Q Science (General) > Back propagation (Artificial intelligence)
Q Science > QA Mathematics > Instruments and machines > Electronic Computers. Computer Science
Q Science > QA Mathematics > Instruments and machines > Electronic Computers. Computer Science > Communication of computer science information
Divisions: Universiti Teknologi MARA, Melaka > Jasin Campus > Faculty of Computer and Mathematical Sciences
Series Name: International Jasin Multimedia and Computer Science Invention and Innovation Exhibition
Volume: 4
Page Range: p. 24
Keywords: Prediction; Bankruptcy; Random forest; Artificial Intelligence; Classification
Date: 2021
URI: https://ir.uitm.edu.my/id/eprint/49423
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