Risk Management Strategies for Bankruptcy and Litigation Prediction Using ML Multivariate Model

Abstract

The incorporation of ML-based multivariate frameworks in bankruptcy and litigation
anticipation improves financial risk assessment by highlighting distress signals initially. Thus, to
highlight the role of the ML Multivariate Model this research has used both qualitative and
quantitative methods. “Altman’s Z-score” as a menial manual model has wide usage however, the
machine learning frameworks create improved accuracy by interpreting non-linear and more
difficult coordination between the financial parameters. The WCF–profitability relationship is an
inverted U shape, the researchers found that small companies and high-leverage firms follow the
full sample. Automated machine learning or AutoML platforms are becoming more common,
allowing the novices to benefit from the machine learning capabilities, and accelerate model
building. Risk mitigation models can also anticipate litigation risks, thus enhancing better
compliance strategies. This navigates companies to decrease economic downturns, regulate risk
assessment models, and reinforce corporate government or CV to create financial dependence for
the long term.