Predict Corporate Financial Distress Using Machine Learning Models

Use case in BFSI Intelligence

Financial distress is a term used in the corporate finance world that refers to a situation of a company when the financial promises to its creditors are either broken or are honored with much difficulty. There could be many factors responsible for a company to be financially distressed – poor budgeting, constant losses, inability to break even, etc. The ability of the company to identify such reasons in advance will enable it to draft preventive measures and strategies to save itself from the distress.

To safeguard the company as well as the vested interests of various stakeholders involved, predicting the early warning indicators of financial distress is a crucial element. By leveraging Data Science, AI, and Machine Learning, we analyzed the factors and predicted the major factors that affect the financial strength of the company.

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Data Analytics and Artificial Intelligence is growing to be an ubiquitous need in the modern enterprise ecosystem. The need for Analytics is ranging from basic Descriptive and Diagnostic Analytics to advanced Predictive, Prescriptive and Cognitive Analytics.  However, the barrier of entry is high due to expensive infrastructure and highly skilled resource requirements.

Qualetics Data Machines Inc. aims to eliminate this barrier by introducing a product that makes it easy for businesses to embrace data science and gain data intelligence.

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