# Collections Predictor

## Overview

Browser-based collections prioritization demo where students test receivables scenarios, compare collection strategies, and examine how AI recommendations change outreach urgency, expected recovery, and working-capital impact.

## Learning Objectives

- Explain the main treasury decision that Collections Predictor is designed to support.
- Use Collection strategy, Enable AI recommendation to test how different assumptions change the scenario.
- Interpret Output Summary in plain language and connect them to an action or conclusion.
- State one limitation, risk, or governance consideration before using the result in a real decision.

## Run Modes

- Browser

## Expected Setup / Startup Time

- Starts immediately in browser with no installs, no API keys, and classroom-safe defaults.

## Demo Type

- Interactive browser demo

## Files in This Folder

- `app.js`
- `index.html`
- `README.md`
- `style.css`

## How To Run

- Browser: open `index.html`.

## How To Use The Demo

1. Choose the run mode that fits the class: Browser.
2. Review the default assumptions before changing anything.
3. Change one or two inputs, then use `Reset Scenario`.
4. Read Output Summary first, then compare any supporting metrics, charts, or AI text.
5. Capture one insight, one limitation, and one action recommendation.

## Inputs

- `Collection strategy` changes one part of the scenario; increase or decrease it deliberately and watch how the output shifts.
- `Enable AI recommendation` changes one part of the scenario; increase or decrease it deliberately and watch how the output shifts.

## Buttons / Actions

- `Reset Scenario` restores the default collections case so learners can compare strategies from a known baseline.
- `Export Result` saves the current collections recommendation and projected outcome for class discussion or follow-up analysis.

## Outputs

- Read the predicted collection outcome and recommended treatment path together, because the suggested action matters as much as the score.
- Look for recovery potential, urgency, and customer treatment differences across strategies or segments.

## What To Notice

- Look for customer risk score, payment history, amount due, and collection priority
- Observe how segmenting customers changes collection strategy
- Note that collections analytics should improve cash recovery while preserving customer relationships
- Compare the headline output with supporting views such as Output Summary before drawing a conclusion

## Related Demos or Course Context

- Course path: [Treasury Management](../../courses/treasury-management.html)
- Related demo: [Monte Carlo Company Valuation](../MonteCarloCompanyValuation/about.html)
- Related demo: [AI Hedge Orchestrator](../AIHedgeOrchestrator/about.html)
- Related demo: [CCC Analyzer](../CCCAnalyzer/about.html)

## Attribution

Created by **Professor Vinaya Sathyanarayana** as part of [KateelLearningDemosToStudents](https://github.com/VinayaSharada/KateelLearningDemosToStudents).
Attribution email: `vinallcontact@gmail.com`