Workgreen2015

Bankruptcy Prediction of Construction Businesses: Towards a Big Data Analytics Approach

Hafiz Alaka · Lukumon O. Oyedele · Muhammad Bilal · Olúgbénga O. Akinadé · Hakeem A. Owolabi · Saheed O Ajayi

33 citations0/0 files storedIEEE International Conference on Big Data Computing Service and Applications (BigDataService)

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Financial Distress and Bankruptcy Prediction Corporate Insolvency and Governance Artificial Intelligence in Healthcare

Abstract

Bankruptcy prediction models (BPMs) are needed by financiers like banks in order to check the credit worthiness of companies. A very robust model needs a very large amount of data with periodic updates (i.e. appending new data). Such size of data cannot be processed directly by the tools used in building BPMs, however Big Data Analytics offers the opportunity to analyse such data. With data sources like DataStream, FAME, Company House, etc. that hold large financial data of existing and failed firms, it is possible to extract huge financial data into Hadoop database (e.g. HBase), whilst allowing periodic appending of data from the data sources, and carry out a Big Data analysis using a machine learning tool on Apache Mahout. Lifelong machine learning can also be employed in order to avoid repeated intensive training of the model using all the data in the Hadoop database. A framework is thus proposed for developing a Big Data Analytics based BPM.

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References

1968FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY 1980Financial Ratios and the Probabilistic Prediction of Bankruptcy 2011Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data 1992Managerial Applications of Neural Networks: The Case of Bank Failure Predictions 2006Bankruptcy prediction in banks and firms via statistical and intelligent techniques – A review 1994Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience)2005Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters 2004An application of support vector machines in bankruptcy prediction model

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2016Methodological approach of construction business failure prediction studies: a review 2021Bankruptcy prediction for private firms in developing economies: a scoping review and guidance for future research 2018A framework for big data analytics approach to failure prediction of construction firms 2019Application of Predictive Analytics at Financial Institutions: A Systematic Literature Review

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