🚨 Stripe HackerRank OA Deep Dive: Merchant Fraud Scoring System — Why Do So Many Fail This "Simple" Business Logic Problem?

In North American tech company Online Assessments, Stripe has always been uniquely different.

Unlike typical algorithm-focused companies, Stripe's OA leans heavily toward real business scenarios with lengthy problem statements, complex rules, and extensive implementation details. Often giving just one hour for a single problem, it thoroughly examines candidates' ability to decompose business logic and implement engineering solutions.

This article will systematically break down the typical model of Stripe HackerRank Online Assessment Questions through a complete real-world case, summarizing hidden patterns and stable solution approaches to help you quickly establish a clear problem-solving structure within limited time.

📋 Stripe HackerRank OA Real Question: Merchant Fraud Scoring System

The problem simulates Stripe's merchant fraud scoring system with a clear objective:

Calculate the final fraud score for each merchant based on a day's transaction records and corresponding rules, then output results sorted by merchant name in lexicographical order.

Input Structure

You'll receive three things:

1. transactions_list
   All transaction records for the day

2. rules_list
   A rule list that corresponds one-to-one with transactions

3. merchants_list
   Each merchant has a base_score initial value

Note a critically important point:
Transactions and rules strictly correspond one-to-one — this is crucial for implementation.

🎯 Overall Solution Approach

Never process this transaction-by-transaction as you read them, or the logic will become extremely chaotic.

The correct approach: Process by rule type in stages, first collect state, then settle in batches.

I would break this into five clear steps.

Step 1: Build Merchants Dictionary First

Don't rush to calculate scores. First transform merchants_list into a dictionary structure:

merchant_id -> current_score

Where current_score is initialized to base_score.

All subsequent rules apply multiplication or addition/subtraction to current_score.

Step 2: Merge Transaction and Rule Into Complete Records

Since transactions_list and rules_list correspond one-to-one, strongly recommend merging them at the start.

Each complete record should contain at least these fields:

• merchant_id
• customer_id
• amount
• hour
• min_transaction_amount
• multiplicative_factor
• additive_factor
• penalty

This way all subsequent logic operates on a single records list, avoiding error-prone indexing across multiple arrays.

Step 3: First Pass — Process Amount Multiplication Rules

This is the most basic step but has an easily overlooked trap.

The rule:

If transaction_amount > min_transaction_amount
multiply the merchant's current_score by the corresponding multiplicative_factor.

Key points:

• Strictly greater than, not greater than or equal
• Each transaction must be evaluated individually
• This directly updates the merchant's current_score

This pass only handles multiplication, no addition or subtraction.

Step 4: Second Pass — Customer + Merchant Additive Rules

This is a very typical rule type that Stripe loves to test.

Rule description:

If the same customer_id has 3 or more transactions (including current) with the same merchant_id,
then add each transaction's additive_factor in that group to the merchant's current_score.

The official problem explicitly emphasizes:

• The third transaction's score already includes additive from transactions 1, 2, 3;
• The fourth transaction adds on top of that with its own additive_factor.

Therefore the correct approach is:

1. Group by (merchant_id, customer_id)
2. Count transactions
3. If count >= 3
   sum all additive_factor values in that group
4. Add once to the corresponding merchant's current_score

Don't add immediately for each transaction, or you'll easily double-count.

Step 5: Third Pass — Same-Hour Transaction Penalty Rules

This is the trickiest and most error-prone part of the entire problem.

Trigger conditions:

• Same customer_id
• Same merchant_id
• Same hour
• Transaction count reaches 3 or more (including current)

Note:
Once 3 is reached, the penalty or reward applies to ALL transactions within that hour, including the 1st and 2nd.

Different Operations for Different Time Ranges

If hour is between 12 and 17 (inclusive)
Add penalty for each transaction

If hour is between 9-11 or 18-21 (inclusive)
Subtract penalty for each transaction

If hour is outside these ranges
Do nothing

Recommended Implementation

Same as additive rules:

1. Group by (merchant_id, customer_id, hour)
2. Only trigger if count >= 3
3. Multiply penalty by transaction count in that group
4. Decide whether to add or subtract based on hour range
5. Update merchant's current_score once

Step 6: Sort and Output Results

Final step is output handling:

• Sort by merchant name in lexicographical order
• Output format must strictly be:

name, score

HackerRank is extremely strict about format — one extra space or missing comma can fail your submission.

💡 What Does This Problem Really Test?

This problem isn't fundamentally an algorithm problem, but rather:

• Complex business rule decomposition ability
• Multi-dimensional state tracking ability
• Staged accumulation rather than immediate calculation
• Engineering implementation and code organization ability

If you write while reading, you'll likely find state management becomes uncontrollable midway.

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