Developed Debt Tracking and Forecasting Systems

• Day: 2024-12-31
• Time: 00:55 to 23:55
• Project: Business
• Workspace: WP 1: Strategic / Growth & Development
• Status: In Progress
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Debt Tracking, Financial Management, OCR, Predictive Modeling, Data Analysis

Description

Session Goal

The session aimed to develop a comprehensive debt tracking and forecasting system, leveraging various data processing and analysis techniques.

Key Activities

• Debt Tracking System Design: Outlined principles for a debt tracking system using Google Sheets and Pandas, focusing on data manipulation and live ledger setup.
• Debugging Techniques: Discussed debugging shortcuts in scripting for efficient development.
• Reverse Engineering Financial Models: Implemented methods to reverse engineer penalty rules from observed data, using Python for rate estimation and modeling.
• OCR Data Processing: Executed OCR processes to extract financial data from municipal debt statements, summarizing key details for Tigre Municipality and TASA reports.
• Debt Management Models: Developed a plan for a debt master calculator, including predictive modeling and anomaly detection.

Achievements

• Successfully outlined and initiated the design of a debt tracking system.
• Implemented OCR processing workflows to extract and summarize municipal financial data.
• Developed a structured plan for a debt master calculator, integrating predictive modeling and optimization strategies.

Pending Tasks

• Further refinement of the debt tracking system using Pandas and Google Sheets.
• Continued development of the debt master calculator, focusing on parameter optimization and anomaly detection.
• Additional data cleaning and model refinement sessions to enhance debt prediction accuracy.

Evidence

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