Tackling GenAI-Powered Data Analytics and Unlocking AI Strategy
The author shares their experience of completing Tata's GenAI Powered Data Analytics simulation on Forage, which involved predicting delinquency risk, designing ethical AI systems, and building an end-to-end GenAI-powered analytics solution.
Why it matters
This simulation provides a valuable learning experience for AI/ML professionals to tackle real-world business problems using GenAI tools and ethical AI principles.
Key Points
- 1Leveraged GenAI tools like Claude and ChatGPT for exploratory data analysis
- 2Designed a no-code predictive modeling framework focused on business feasibility, scalability, and explainability
- 3Architected an AI-driven collections strategy that incorporated ethical AI principles and regulatory compliance
- 4Appreciated the simulation's real-world messiness, GenAI integration, ethical complexity, and progressive scaffolding
Details
The author, a master's-degree hustler, tackled Tata's GenAI Powered Data Analytics simulation on Forage, which placed them in the role of an AI transformation consultant working with Geldium Finance's collections team. The simulation involved three interconnected tasks: 1) Exploratory Data Analysis (EDA) using GenAI tools to assess data quality, identify risk indicators, and structure insights for predictive modeling; 2) Designing a no-code predictive modeling framework that focused on business feasibility, scalability, and explainability; and 3) Architecting an AI-driven collections strategy that leveraged agentic AI, incorporated ethical AI principles, and met regulatory compliance requirements. The author appreciated the simulation's real-world messiness, GenAI integration (rather than AI replacement), ethical complexity, progressive scaffolding, and Forage's professional presentation, which made it feel like a genuine consulting engagement.
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