Which fixed income investment in India offers the best post-tax returns for senior citizens?

Evaluate Senior Citizen Savings Scheme, PM Vaya Vandana Yojana, bank fixed deposits, post office schemes, and debt mutual funds based on interest rates, tax implications, and liquidity.

# Aplly.xyz Case Study Submission

## Title
Which fixed income investment in India offers the best post-tax returns for senior citizens?

## Type
Multi-Criteria Product Comparison

## Difficulty
Intermediate

## Estimated Time
60 minutes

## Overview
Evaluate Senior Citizen Savings Scheme, PM Vaya Vandana Yojana, bank fixed deposits, post office schemes, and debt mutual funds based on interest rates, tax implications, and liquidity.

## Case Details

Function Focus: Multi-Criteria Product Comparison — manual reasoning, decomposition, and critical judgment (no spreadsheet or AI tool permitted in Phase 1)

Scenario:
You are the analyst at a fictional consultancy ("Praxis Advisors") tasked with answering: Which fixed income investment in India offers the best post-tax returns for senior citizens?. You have a clean, synthetic dataset described below. You must produce a defensible answer using structured reasoning — no tool-assisted shortcut on the first pass.

Dataset Structure:
- product
- category
- expected_return_pct
- risk_level
- min_investment_rs
- lock_in_years
- tax_benefit
- expense_ratio_pct
- exit_load_pct
- liquidity_score
- suitable_for

Tasks:
1. Define the evaluation criteria for the choice and assign weights to each, justified explicitly.
2. Score each alternative by hand against the criteria using the dataset columns.
3. Compute a weighted total and rank the alternatives; show the arithmetic.
4. Run a sensitivity check: change the top two weights by +/10 points and note whether the winner changes.
5. After the manual scoring, redo it in a spreadsheet and reconcile any ranking differences.

Expected Output:
A one-page recommendation memo: criteria & weights, scorecard table, final ranking, sensitivity result.

Evaluation Criteria:
Explicit and justified weights, arithmetic correctness, meaningful sensitivity analysis, defensible final ranking.

## Data Sources

| product | category | expected_return_pct | risk_level | min_investment_rs | lock_in_years | tax_benefit | expense_ratio_pct | exit_load_pct | liquidity_score | suitable_for |
|---|---|---|---|---|---|---|---|---|---|---|
| SBI BlueChip Fund | Mutual Fund | 14.5 | Moderate | 500 | 0 | LTCG after 1yr | 1.12 | 1 | 8 | Growth |
| HDFC Top 100 | Mutual Fund | 13.8 | Moderate | 500 | 0 | LTCG after 1yr | 1.05 | 1 | 8 | Growth |
| Axis Midcap | Mutual Fund | 16.2 | High | 500 | 0 | LTCG after 1yr | 1.35 | 1 | 7 | Aggressive |
| PPF | Fixed Income | 7.1 | Low | 500 | 15 | 80C deduction | 0 | 0 | 3 | Conservative |
| EPF | Fixed Income | 8.25 | Low | 0 | 0 | 80C deduction | 0 | 0 | 2 | Salary-based |
| NPS Tier 1 | Pension | 10.2 | Moderate | 1000 | 60 | 80C+80CCD | 0.09 | 0 | 3 | Retirement |
| Sukanya Samriddhi | Fixed Income | 8 | Low | 250 | 21 | 80C deduction | 0 | 0 | 2 | Girl child |
| Gold ETF | Commodity | 9.8 | Moderate | 1000 | 0 | LTCG after 3yr | 0.5 | 0 | 9 | Diversification |
| SGB | Gold | 8.5 | Low | 1000 | 8 | LTCG benefit | 0 | 0 | 4 | Saver |
| FD (HDFC) | Fixed Income | 6.5 | Low | 10000 | 0 | Interest taxable | 0 | 0.5 | 10 | Conservative |


Full dataset: https://github.com/arora200/aplly_case_db/datasets/054-investments-financial-planning (synthetic, 13 records)

## Solution Frameworks
Multi-criteria weighted scoring, pairwise trade-off analysis, cost-benefit decomposition, sensitivity/robustness check

## Solver Guidance & Tutorials
_Solver guidance added by the pipeline (tutorial links) — see `solver_guidance` field._

## What You'll Learn
- Turn vague preferences into weighted criteria
- Build a transparent scorecard
- Test how fragile your winner is

## Tags
Fixed Income, Senior Citizens, Post-tax Returns, India

## Registration Links
Register as Solver / Register as Evaluator