Average_Fee_Estimation

Average Fee Estimation

Two question types appear in this category:

1. Specific average fee: "What is the average fee that card scheme X would charge for a transaction value of Y EUR [for credit transactions / account type Z / MCC description D]?"
2. Best/worst card scheme: "In the average scenario, which card scheme is cheapest/most expensive for a transaction value of Y EUR?"

Data Sources

Only two files are needed — do not read manual.md, payments-readme.md, or payments.csv for this category:

• fees.json — array of 1000 fee rule objects
• merchant_category_codes.csv — columns mcc (int) and description (string); needed only when question specifies an MCC by text description

Fee Formula

fee = fixed_amount + rate * transaction_value / 10000

The average fee is the arithmetic mean across all matching rules.

Null / Empty = "Applies to All"

When the question specifies a filter value, include a rule if its field is null/empty OR explicitly contains that value.

Account Type Codes

Question Type 1: Specific Average Fee

Filter fees.json to applicable rules, compute each rule's fee, then average.

Filter logic for stated constraints

• is_credit (when question says "credit transactions"): Include rules where is_credit is True or is_credit is None

• account_type (when question specifies account type, e.g., "account type H"): Include rules where account_type == [] or specified code is in account_type

• merchant_category_code (when question specifies an MCC description):

  1. Look up 4-digit MCC in merchant_category_codes.csv matching the description text
  2. Include rules where merchant_category_code == [] or that MCC is in the list

• Unspecified fields: Do not filter — include rules with any value for that field.

Pattern A: Credit filter only

import json
with open('fees.json') as f:
    fees = json.load(f)

transaction_value = 50
matching = [r for r in fees
            if r['card_scheme'] == 'GlobalCard'
            and (r['is_credit'] is None or r['is_credit'] == True)]

fee_values = [r['fixed_amount'] + r['rate'] * transaction_value / 10000 for r in matching]
answer = round(sum(fee_values) / len(fee_values), 6)

Pattern B: Account type filter

matching = [r for r in fees
            if r['card_scheme'] == 'NexPay'
            and (r['account_type'] == [] or 'R' in r['account_type'])]

fee_values = [r['fixed_amount'] + r['rate'] * transaction_value / 10000 for r in matching]
answer = round(sum(fee_values) / len(fee_values), 6)

Pattern C: Account type + MCC description filter

import pandas as pd
mcc_df = pd.read_csv('merchant_category_codes.csv')
mcc_code = int(mcc_df[mcc_df['description'].str.contains('Drinking Places', case=False)]['mcc'].values[0])

matching = [r for r in fees
            if r['card_scheme'] == 'GlobalCard'
            and (r['account_type'] == [] or 'H' in r['account_type'])
            and (r['merchant_category_code'] == [] or mcc_code in r['merchant_category_code'])]

fee_values = [r['fixed_amount'] + r['rate'] * transaction_value / 10000 for r in matching]
answer = round(sum(fee_values) / len(fee_values), 6)

Output format

Single float rounded to 6 decimal places. Example: 0.560694

Question Type 2: Which Card Scheme is Cheapest / Most Expensive

"In the average scenario" means average across all fee rules for each card scheme — apply no filtering by is_credit, account_type, or any other field.

import json
from collections import defaultdict

with open('fees.json') as f:
    fees = json.load(f)

transaction_value = 10  # EUR from question

scheme_fees = defaultdict(list)
for rule in fees:
    fee = rule['fixed_amount'] + rule['rate'] * transaction_value / 10000
    scheme_fees[rule['card_scheme']].append(fee)

avg_by_scheme = {scheme: sum(f) / len(f) for scheme, f in scheme_fees.items()}

cheapest = min(avg_by_scheme, key=avg_by_scheme.get)       # for "cheapest" questions
most_expensive = max(avg_by_scheme, key=avg_by_scheme.get) # for "most expensive" questions

Output format

Single string — the card scheme name exactly as it appears in the data: NexPay, GlobalCard, SwiftCharge, or TransactPlus.

Common Mistakes

1. Missing null/empty rules: Rules with is_credit = null apply to credit transactions; rules with account_type = [] apply to all account types. Omitting them produces the wrong count and a wrong average.

2. Only including explicitly-listed rules: A rule with account_type = [] covers account type F even though F is not in the list. Always include empty-list rules.

3. Wrong MCC column name: The description column in merchant_category_codes.csv is named description (not edited_description). Use mcc_df['description'].str.contains(...).

4. Wrong "average scenario" interpretation: For cheapest/most expensive scheme questions, do not filter by specific transaction characteristics — average over ALL rules per scheme.

5. Precision: Numeric answers require exactly 6 decimal places.

Verified Examples