Bankruptcy prediction of construction businesses: Towards a big data analytics approach

Hafiz Alaka, Lukumon O. Oyedele, Muhammad Bilal, Olugbenga O. Akinade, Hakeen A. Owolabi, Saheed O. Ajayi

Research output: Chapter in Book/Report/Conference proceedingConference proceeding

7 Citations (Scopus)
62 Downloads (Pure)

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.
Original languageEnglish
Title of host publication2015 IEEE First International Conference on Big Data Computing Service and Applications (BigDataService)
PublisherIEEE
Pages347-352
Number of pages6
ISBN (Electronic)978-1-4799-8128-1
DOIs
Publication statusPublished - 13 Aug 2015
Event2015 IEEE First International Conference on Big Data Computing Service and Applications (BigDataService) - Redwood City, United States
Duration: 30 Mar 20152 Apr 2015
Conference number: 1

Conference

Conference2015 IEEE First International Conference on Big Data Computing Service and Applications (BigDataService)
CountryUnited States
CityRedwood City
Period30/03/152/04/15

Fingerprint

Industry
Learning systems
Big data

Bibliographical note

© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Keywords

  • Big data
  • Bankruptcy
  • Data models
  • Predictive models
  • Companies
  • Robustness
  • Support vector machines
  • learning (artificial intelligence)
  • Bid Data
  • construction industry
  • data analysis
  • financial data processing
  • lifelong machine learning
  • bankruptcy prediction model
  • construction business
  • BPM
  • Big Data analytics approach
  • credit worthiness
  • financial data
  • Hadoop database
  • Apache Mahout
  • Construction business failure
  • Big data analytics
  • Bankruptcy prediction models
  • Machine learning
  • Financial models

Cite this

Alaka, H., Oyedele, L. O., Bilal, M., Akinade, O. O., Owolabi, H. A., & Ajayi, S. O. (2015). Bankruptcy prediction of construction businesses: Towards a big data analytics approach. In 2015 IEEE First International Conference on Big Data Computing Service and Applications (BigDataService) (pp. 347-352). IEEE. https://doi.org/10.1109/BigDataService.2015.30

Bankruptcy prediction of construction businesses : Towards a big data analytics approach. / Alaka, Hafiz; Oyedele, Lukumon O.; Bilal, Muhammad; Akinade, Olugbenga O.; Owolabi, Hakeen A.; Ajayi, Saheed O.

2015 IEEE First International Conference on Big Data Computing Service and Applications (BigDataService). IEEE, 2015. p. 347-352.

Research output: Chapter in Book/Report/Conference proceedingConference proceeding

Alaka, H, Oyedele, LO, Bilal, M, Akinade, OO, Owolabi, HA & Ajayi, SO 2015, Bankruptcy prediction of construction businesses: Towards a big data analytics approach. in 2015 IEEE First International Conference on Big Data Computing Service and Applications (BigDataService). IEEE, pp. 347-352, 2015 IEEE First International Conference on Big Data Computing Service and Applications (BigDataService), Redwood City, United States, 30/03/15. https://doi.org/10.1109/BigDataService.2015.30
Alaka H, Oyedele LO, Bilal M, Akinade OO, Owolabi HA, Ajayi SO. Bankruptcy prediction of construction businesses: Towards a big data analytics approach. In 2015 IEEE First International Conference on Big Data Computing Service and Applications (BigDataService). IEEE. 2015. p. 347-352 https://doi.org/10.1109/BigDataService.2015.30
Alaka, Hafiz ; Oyedele, Lukumon O. ; Bilal, Muhammad ; Akinade, Olugbenga O. ; Owolabi, Hakeen A. ; Ajayi, Saheed O. / Bankruptcy prediction of construction businesses : Towards a big data analytics approach. 2015 IEEE First International Conference on Big Data Computing Service and Applications (BigDataService). IEEE, 2015. pp. 347-352
@inproceedings{2b4aee5c9f8a44c2b99310761807bb55,
title = "Bankruptcy prediction of construction businesses: Towards a big data analytics approach",
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.",
keywords = "Big data, Bankruptcy, Data models, Predictive models, Companies, Robustness, Support vector machines, learning (artificial intelligence), Bid Data, construction industry, data analysis, financial data processing, lifelong machine learning, bankruptcy prediction model, construction business, BPM, Big Data analytics approach, credit worthiness, financial data, Hadoop database, Apache Mahout, Construction business failure, Big data analytics, Bankruptcy prediction models, Machine learning, Financial models",
author = "Hafiz Alaka and Oyedele, {Lukumon O.} and Muhammad Bilal and Akinade, {Olugbenga O.} and Owolabi, {Hakeen A.} and Ajayi, {Saheed O.}",
note = "{\circledC} 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.",
year = "2015",
month = "8",
day = "13",
doi = "10.1109/BigDataService.2015.30",
language = "English",
pages = "347--352",
booktitle = "2015 IEEE First International Conference on Big Data Computing Service and Applications (BigDataService)",
publisher = "IEEE",
address = "United States",

}

TY - GEN

T1 - Bankruptcy prediction of construction businesses

T2 - Towards a big data analytics approach

AU - Alaka, Hafiz

AU - Oyedele, Lukumon O.

AU - Bilal, Muhammad

AU - Akinade, Olugbenga O.

AU - Owolabi, Hakeen A.

AU - Ajayi, Saheed O.

N1 - © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

PY - 2015/8/13

Y1 - 2015/8/13

N2 - 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.

AB - 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.

KW - Big data

KW - Bankruptcy

KW - Data models

KW - Predictive models

KW - Companies

KW - Robustness

KW - Support vector machines

KW - learning (artificial intelligence)

KW - Bid Data

KW - construction industry

KW - data analysis

KW - financial data processing

KW - lifelong machine learning

KW - bankruptcy prediction model

KW - construction business

KW - BPM

KW - Big Data analytics approach

KW - credit worthiness

KW - financial data

KW - Hadoop database

KW - Apache Mahout

KW - Construction business failure

KW - Big data analytics

KW - Bankruptcy prediction models

KW - Machine learning

KW - Financial models

U2 - 10.1109/BigDataService.2015.30

DO - 10.1109/BigDataService.2015.30

M3 - Conference proceeding

SP - 347

EP - 352

BT - 2015 IEEE First International Conference on Big Data Computing Service and Applications (BigDataService)

PB - IEEE

ER -