The Fifth Elephant 2023 Monsoon

Problem:
The Consumer Packaged Goods (CPG) industry faces unique credit risk challenges such as fluctuating consumer demands, the risk of bad debt, optimal working capital management, and market volatility.
These challenges necessitate a robust and dynamic credit risk model to accurately assess and manage credit risks. While similar problems have been addressed in the banking sector, the CPG industry presents unique challenges due to its specific market dynamics and consumer behavior patterns. The integration of AI and machine learning in this context, while ensuring data privacy and governance, presents a unique problem to solve.

Solution:
The proposed solution is a comprehensive AI-driven framework for credit risk modeling specifically designed for the CPG industry. This framework leverages Decision Intelligence Systems and Responsible AI principles to provide a step-by-step guide for building a robust credit risk model.
It emphasizes the integration of cutting-edge technologies such as machine learning and big data analytics to enhance predictive accuracy and decision-making agility. The framework also highlights the importance of model governance, monitoring, and maintenance to ensure the model’s effectiveness over time. Furthermore, it underscores the need for regulatory compliance, data security, data privacy, and ethical considerations in credit risk assessments, focusing on transparency, fairness, and model explainability.

Implication:
Without a comprehensive AI-driven credit risk model, CPG companies may face a multitude of risks. The most significant of these is financial risk, as companies may struggle to maintain financial stability and sustainability without accurate credit risk assessments.
This could lead to issues such as bad debt and challenges in managing optimal working capital. In addition, companies may also face operational risks if they lack a robust system for credit risk management. This could result in inefficiencies and errors in credit decisions, potentially leading to financial losses.
Strategy risk is another implication, as companies without a dynamic credit risk model may fail to adapt their strategies to changing market conditions, potentially leading to missed opportunities or financial losses. Legal risk is also a concern, as companies that fail to comply with regulatory standards in credit risk assessments could face legal repercussions.
This could also lead to reputation risk, as non-compliance with regulations or unethical practices in credit risk assessments could damage a company’s reputation, potentially leading to loss of customers or business partners.

Session Outline:
Introduction to Credit Risk: Understanding the unique credit risk challenges in the Consumer Packaged Goods (CPG) industry and the need for AI and machine learning solutions.
Credit Risk Playbook: A step-by-step guide to building a credit risk and credit limit model, starting with data collection, preprocessing, feature selection, model development, validation, and backtesting.
Model Explainability and Stability: Exploring the importance of model explainability, ensuring that stakeholders understand how the model works and makes decisions, and emphasizing transparency in credit risk assessments along with the stability of credit risk scores.
Experiment Design: Discussion on how to design experiments to test the model’s performance under a variety of scenarios.
Model Deployment (MLOps): Discussing the final step of the process, deploying the model into the real world, and using it to make credit risk assessments.
Model Governance, Regulatory Compliance, and Ethics: Highlighting the importance of model governance, monitoring, and maintenance, and emphasizing the need for adherence to regulatory standards and ethical considerations in credit risk assessments, focusing on fairness in credit decisions.

Speaker Linkedin Profile: https://www.linkedin.com/in/mraduljain1/