Which Credit Card in India is best for getting maximum cashback benefits?

Compare cashback credit cards across different spending categories like groceries, fuel, utility bills, and online shopping. Analyze monthly caps, accelerated cashback periods, and redemption options available in the Indian market.

# Aplly.xyz Case Study Submission

## Title
Which Credit Card in India is best for getting maximum cashback benefits?

## Type
Multi-Criteria Product Comparison

## Difficulty
Intermediate

## Estimated Time
60 minutes

## Overview
Compare cashback credit cards across different spending categories like groceries, fuel, utility bills, and online shopping. Analyze monthly caps, accelerated cashback periods, and redemption options available in the Indian market.

## 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 Credit Card in India is best for getting maximum cashback benefits?. 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:
- card_name
- bank
- type
- annual_fee
- join_fee
- reward_rate_pct
- cashback_pct
- fuel_surcharge_waiver
- lounge_access
- insurance_cover_lakhs
- forex_markup_pct
- min_income_lakhs
- interest_rate_pct

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

| card_name | bank | type | annual_fee | join_fee | reward_rate_pct | cashback_pct | fuel_surcharge_waiver | lounge_access | insurance_cover_lakhs | forex_markup_pct | min_income_lakhs | interest_rate_pct |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Platinum Rewards | HDFC | Credit | 1500 | 500 | 2 | 1 | Yes | Domestic | 10 | 2.5 | 6 | 42 |
| Sapphiro | ICICI | Credit | 3500 | 1000 | 3 | 1.5 | Yes | International | 25 | 1.5 | 8 | 40 |
| SimplyCLICK | SBI | Credit | 500 | 0 | 1 | 5 | Yes | None | 5 | 3.5 | 3 | 43 |
| Regalia Gold | HDFC | Credit | 2500 | 500 | 4 | 1 | Yes | Domestic | 15 | 2 | 7 | 41 |
| Emeralde | ICICI | Credit | 12000 | 5000 | 6 | 2 | Yes | International | 50 | 1 | 15 | 38 |
| ACE | Axis | Credit | 500 | 0 | 1.5 | 2 | No | None | 0 | 3.5 | 3.5 | 42 |
| Magnus | Axis | Credit | 10000 | 2500 | 5 | 1.5 | Yes | International | 30 | 1.5 | 12 | 39 |
| Millennia | HDFC | Credit | 1000 | 0 | 1.5 | 5 | No | None | 2 | 3 | 4 | 42 |
| Club Vistara | SBI | Credit | 3000 | 1000 | 2 | 0.5 | Yes | Domestic | 10 | 3 | 6 | 41 |
| Infinite | SBI | Credit | 5000 | 1500 | 3 | 1 | Yes | International | 20 | 2 | 10 | 40 |


Full dataset: https://github.com/arora200/aplly_case_db/datasets/049-credit-cards-banking (synthetic, 21 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
Credit Cards, Cashback, Banking

## Registration Links
Register as Solver / Register as Evaluator