A Comprehensive Workflow for Enhancing Business Bankruptcy Prediction

2015 ◽  
pp. 216-238
Author(s):  
Rui Sarmento ◽  
Luís Trigo ◽  
Liliana Fonseca
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Empirical Study ◽  
Sustainability Assessment ◽  
Business Environment ◽  
Bankruptcy Prediction ◽  
Prediction Performance ◽  
Critical Area ◽  
Classifier Selection ◽  
Comprehensive Knowledge

Forecasting enterprise bankruptcy is a critical area for Business Intelligence. It is a major concern for investors and credit institutions on risk analysis. It may also enable the sustainability assessment of critical suppliers and clients, as well as competitors and the business environment. Data Mining may deliver a faster and more precise insight about this issue. Widespread software tools offer a broad spectrum of Artificial Intelligence algorithms and the most difficult task may be the decision of selecting that algorithm. Trying to find an answer for this decision in the relatively large amount of available literature in this area with so many options, advantages, and pitfalls may be as informative as distracting. In this chapter, the authors present an empirical study with a comprehensive Knowledge Discovery and Data Mining (KDD) workflow. The proposed classifier selection automation selects an algorithm that has better prediction performance than the most widely documented in the literature.

A Comprehensive Workflow for Enhancing Business Bankruptcy Prediction

2018 ◽  
pp. 2135-2160
Author(s):  
Rui Sarmento ◽  
Luís Trigo ◽  
Liliana Fonseca
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Empirical Study ◽  
Sustainability Assessment ◽  
Business Environment ◽  
Bankruptcy Prediction ◽  
Prediction Performance ◽  
Critical Area ◽  
Classifier Selection ◽  
Comprehensive Knowledge

Forecasting enterprise bankruptcy is a critical area for Business Intelligence. It is a major concern for investors and credit institutions on risk analysis. It may also enable the sustainability assessment of critical suppliers and clients, as well as competitors and the business environment. Data Mining may deliver a faster and more precise insight about this issue. Widespread software tools offer a broad spectrum of Artificial Intelligence algorithms and the most difficult task may be the decision of selecting that algorithm. Trying to find an answer for this decision in the relatively large amount of available literature in this area with so many options, advantages, and pitfalls may be as informative as distracting. In this chapter, the authors present an empirical study with a comprehensive Knowledge Discovery and Data Mining (KDD) workflow. The proposed classifier selection automation selects an algorithm that has better prediction performance than the most widely documented in the literature.

Predicting Business Bankruptcy

2016 ◽  
Vol 5 (2) ◽  
pp. 48-65
Author(s):  
Rui Sarmento ◽  
Luís Trigo ◽  
Liliana Fonseca
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Financial Institutions ◽  
Broad Spectrum ◽  
Financial Distress ◽  
Sustainability Assessment ◽  
Business Environment ◽  
Decision Makers ◽  
Government Agencies

Managers, investors, financial institutions and government agencies have a major concern on forecasting enterprise bankruptcy. It enables the sustainability assessment of critical suppliers and clients, as well as competitors and the business environment. Throughout the 20th and the 21st century, advances in statistics and computer science fields enabled the development of different trends in financial distress assessment that co-exist today. However, recent Data Mining (DM) techniques are regarded as being the most precise. IT expertise requirements in the constantly evolving DM field may have been a major obstacle to the adoption of these techniques by decision makers. Furthermore, DM software tools that are now widespread offer a broad spectrum of Artificial Intelligence algorithms and the most difficult task may be the decision of selecting the appropriate algorithm. Hence, the adoption of a good workflow method for data processing and analysis is critical for having fast and reliable results. This work presents an overview of the available bankruptcy techniques and provides a comprehensive case study exploring the latest Data Mining techniques.

Innovative Marketing Strategies for Select E-Commerce Companies in India - A Review.

2018 ◽  
Vol 5 (1) ◽  
Author(s):  
Amit Kumar Bhanja ◽  
P.C Tripathy
Keyword(s):  
Artificial Intelligence ◽  
Internet Of Things ◽  
Business Environment ◽  
Marketing Strategies ◽  
Point Of View ◽  
Digital Marketing ◽  
Customer Base ◽  
Innovative Methods ◽  
Online Shoppers ◽  
Drastic Increase

Innovation is the key to opportunities and growth in today’s competitive and dynamic business environment. It not only nurtures but also provides companies with unique dimensions for constant reinvention of the existing way of performance which enables and facilitates them to reach out to their prospective customers more effectively. It has been estimated by Morgan Stanley that India would have 480 million shoppers buying products online by the year 2026, a drastic increase from 60 million online shoppers in the year 2016. E-commerce companies are aggressively implementing innovative methods of marketing their product offerings using tools like digital marketing, internet of things (IoT)and artificial intelligence to name a few. This paper focuses on outlining the innovative ways of marketing that the E-Commerce sector implements in orders to increase their customer base and aims at determining the future scope of this area. A conceptual comparative study of Amazon and Flipkart helps to determine which marketing strategies are more appealing and beneficial for both the customers and companies point of view.

A novel multi-stage ensemble model with fuzzy clustering and optimized classifier composition for corporate bankruptcy prediction

2020 ◽  
pp. 1-17
Author(s):  
Dongqi Yang ◽  
Wenyu Zhang ◽  
Xin Wu ◽  
Jose H. Ablanedo-Rosas ◽  
Lingxiao Yang ◽  
...  
Keyword(s):  
Fuzzy Clustering ◽  
Rapid Development ◽  
Optimization Method ◽  
Selection Method ◽  
Bankruptcy Prediction ◽  
Ensemble Model ◽  
Composition Method ◽  
Classifier Selection ◽  
Multi Stage ◽  
Proposed Model

With the rapid development of commercial credit mechanisms, credit funds have become fundamental in promoting the development of manufacturing corporations. However, large-scale, imbalanced credit application information poses a challenge to accurate bankruptcy predictions. A novel multi-stage ensemble model with fuzzy clustering and optimized classifier composition is proposed herein by combining the fuzzy clustering-based classifier selection method, the random subspace (RS)-based classifier composition method, and the genetic algorithm (GA)-based classifier compositional optimization method to achieve accuracy in predicting bankruptcy among corporates. To overcome the inherent inflexibility of traditional hard clustering methods, a new fuzzy clustering-based classifier selection method is proposed based on the mini-batch k-means algorithm to obtain the best performing base classifiers for generating classifier compositions. The RS-based classifier composition method was applied to enhance the robustness of candidate classifier compositions by randomly selecting several subspaces in the original feature space. The GA-based classifier compositional optimization method was applied to optimize the parameters of the promising classifier composition through the iterative mechanism of the GA. Finally, six datasets collected from the real world were tested with four evaluation indicators to assess the performance of the proposed model. The experimental results showed that the proposed model outperformed the benchmark models with higher predictive accuracy and efficiency.

Sentiment analysis of students in ideological and political teaching based on artificial intelligence and data mining

2021 ◽  
pp. 1-10
Author(s):  
Wan Hongmei ◽  
Tang Songlin
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Sentiment Analysis ◽  
Machine Learning Algorithms ◽  
Classroom Teaching ◽  
Functional Modules ◽  
Semantic Ambiguity ◽  
Calculation Methods ◽  
Multiple Factors ◽  
Quantitative Calculation

In order to improve the efficiency of sentiment analysis of students in ideological and political classrooms, under the guidance of artificial intelligence ideas, this paper combines data mining and machine learning algorithms to improve and propose a method for quantifying the semantic ambiguity of sentiment words. Moreover, this paper designs different quantitative calculation methods of sentiment polarity intensity, and constructs video image sentiment recognition, text sentiment recognition, and speech sentiment recognition functional modules to obtain a combined sentiment recognition model. In addition, this article studies student emotions in ideological and political classrooms from the perspective of multimodal transfer learning, and optimizes the deep representation of images and texts and their corresponding deep networks through single-depth discriminative correlation analysis. Finally, this paper designs experiments to verify the model effect from two perspectives of single factor sentiment analysis and multi-factor sentiment analysis. The research results show that comprehensive analysis of multiple factors can effectively improve the effect of sentiment analysis of students in ideological and political classrooms, and enhance the effect of ideological and political classroom teaching.

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