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

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.