Hybrid Bankruptcy Prediction Model (HBPM) for NBFCs in India

Abstract

The Hybrid Bankruptcy Prediction Model (HBPM) provides a comprehensive and multi-faceted approach for predicting bankruptcy in Non-Banking Financial Companies (NBFCs). This model integrates multiple domains—financial ratios, macroeconomic indicators, regulatory compliance metrics, and real-time alerts through an Early Warning System (EWS)—to deliver a robust, data-driven solution aimed at assessing and preventing financial distress. The HBPM combines quantitative financial analysis with insights into the external economic environment and compliance requirements to create a holistic risk assessment framework. Leveraging advanced machine learning algorithm like Random Forest, the HBPM processes a variety of data inputs to generate a Probability Score (P-score) that indicates the likelihood of bankruptcy. This model allows for timely interventions by identifying patterns and anomalies in the data, thereby supporting decision-makers in proactively mitigating potential financial risks. The integration of machine learning ensures that the model adapts to evolving market conditions, providing accurate, reliable, and scalable predictions. The HBPM's ability to incorporate dynamic, real-time monitoring through the EWS further enhances its predictive accuracy, making it a valuable tool for financial stability and risk management in the NBFC sector.

Specification

Description:Background:
Traditional models for bankruptcy predictions are mostly based on the Altman Z-Score and Merton Model, based mostly on financial ratios. As such, these models are not applicable to NBFCs as it involves special operational, sectoral, and regulatory risks. HBPM addresses the limitation with a multi-dimensional approach to targeted parameters from the domains of financial, macro-economic, and regulatory in rendering a general overview of the health of the financials of NBFCs. Real-time data acclimation enables it, backed by machine learning algorithms that this model uses for application, therefore predicting correctly under different economic scenarios.

Overview of the Hybrid Bankruptcy Prediction Model (HBPM):
The HBPM predicts bankruptcy by computing a composite risk score based on the following components:
1) Financial Health Score (30%): It assesses Internal financial stability through asset quality, liquidity, profitability, and capital structure.
2) Macroeconomic Impact Score (25%): It evaluates external factors like GDP growth, interest rate sensitivity, and inflation, highlighting NBFC vulnerability to economic shifts.
3) Regulatory Compliance Score (25%): Measurement of compliance to regulatory requirements, such as capital adequacy, exposure limits, and statutory ratios, with an emphasis on regulatory penalties and compliance history.
4) Early Warning System (20%): It indicates the level of immediate financial risk based on real-time monitoring of financial, macroeconomic, and regulatory indicators.
HBPM assumes these scores and thus evaluates the total financial stress probability of an NBFC to produce a probability score, P-score, determining how likely an entity may have the potential for bankruptcy.
Detailed Model Structure and Component Calculations:
1) Financial Health Score: Assesses internal financial stability using metrics such as:
• Asset quality: Mainly comprises the gross NPA, net NPA, and provision coverage.
• Liquidity position: Current ratio, quick ratio, and working capital ratio.
• Profitability: includes Return on Assets, Return on Equity, and Net Interest Margin.
• Capital Structure: Examines Debt/Equity Ratio, Interest Coverage Ratio, and Capital Adequacy Ratio.
The Financial Health Score accounts for 30% of the overall bankruptcy prediction model.

2) Macroeconomic Impact Score: Incorporates external economic factors such as:
• Economic Indicators: Economic indicators measured are GDP growth, RBI repo rate changes, and CPI inflation rate added, weighted, and aggregated to find economic impact on financial stability.
• Market Conditions: Sectoral performance, credit market conditions, and liquidity environment are assessed to determine the exposure of NBFC to market shocks. Regulatory Compliance Score.
These indicators are aggregated to assess the impact of macroeconomic factors on the NBFC's financial stability. The Macroeconomic Impact Score contributes 25% to the overall model.


3) Regulatory Compliance Score: Measures adherence to regulatory requirements, including:
• Complying Metrics: monitors the capital adequacy compliance, limit of exposure, and maintaining the statutory ratio.
• Regulatory Standing: Analyzes compliance history, regulatory ratings, and penalties for past non-compliance.
The Regulatory Compliance Score contributes 25% to the overall risk assessment.

4) Early Warning System (EWS): A real-time monitoring mechanism that tracks various indicators to detect early signs of financial distress. They are:
• Financial Triggers: indicate internal financial problems such as declining profitability, asset quality deterioration, or liquidity issues.
• Macroeconomic Triggers: highlights broader economic conditions like changes in GDP, inflation, or interest rates that may impact the NBFC's stability.
• Regulatory Triggers: signals various compliance issues, such as failure to meet capital requirements or breaches of statutory limits, that can lead to regulatory actions.
The EWS uses pre-defined thresholds to trigger alerts, allowing stakeholders to take preventive action against potential financial risks. The Early Warning System Score contributes 20% to the overall model.
Machine Learning Algorithm:
The model employs the Random Forest algorithm, chosen for its interpretability, robustness, and capacity to prevent overfitting. Random Forest processes a variety of inputs and generates a P-score by combining the weighted contributions of each model component.
Validation and Testing:
The model's reliability is ensured through rigorous backtesting, stress testing, and sensitivity analysis under various economic scenarios. These validation processes ensure that the HBPM remains effective across different conditions, providing consistent and reliable predictions.
The Probability Score (P-Score) is computed by combining the weighted scores from each component: Financial Health Score (30%), Macroeconomic Impact Score (25%), Regulatory Compliance Score (25%), and Early Warning System Score (20%). This score is used to classify NBFCs into different risk categories, prompting the required level of monitoring and intervention. , Claims:1. Claim 1: A method for predicting the likelihood of bankruptcy in NBFCs, comprising the steps of:
• Collecting financial ratios, macroeconomic indicators, regulatory compliance data, and operational metrics.
• Calculating Financial Health, Macroeconomic Impact, Regulatory Compliance, and Early Warning System Scores.
• Using the Random Forest algorithm to aggregate these scores and generate a Probability Score (P-score) indicating bankruptcy risk.
2. Claim 2: The method of Claim 1, wherein the Financial Health Score is calculated by analyzing asset quality, liquidity position, profitability, and capital structure of the NBFC, incorporating metrics such as Gross NPA Ratio, Current Ratio, Return on Assets (ROA), and Debt-to-Equity Ratio.
3. Claim 3: The method of Claim 1, wherein the Macroeconomic Impact Score includes factors such as GDP growth, interest rate sensitivity, inflation, sectoral performance, credit market conditions, and liquidity environment, measured through economic indicators and market trends.
4. Claim 4: The method of Claim 1, wherein the Regulatory Compliance Score includes evaluating capital adequacy compliance, exposure limit adherence, statutory ratio maintenance, compliance history, regulatory ratings, and penalties imposed by regulators.
5. Claim 5: The method of Claim 1, wherein the Early Warning System Score aggregates risk levels across financial, economic, and regulatory triggers, quantifying immediate risk levels for preventive action.
6. Claim 6: The method of Claim 1, further comprising a validation process including backtesting, stress testing, and sensitivity analysis to ensure model robustness and reliability across different economic scenarios.
7. Claim 7: A system that integrates financial, macroeconomic, regulatory, and operational metrics into a comprehensive bankruptcy prediction model for NBFCs, using machine learning algorithms to process data inputs and produce a real-time predictive output, thereby enabling early detection and intervention.

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