Average_Fee_Estimation

Average Fee Estimation — Dabstep Dataset

Core Formula

fee = fixed_amount + rate * transaction_value / 10000

Fee rules live in fees.json (1000 rules). Each rule has: card_scheme, account_type (list), merchant_category_code (list), is_credit (bool/null), aci (list), capture_delay, monthly_fraud_level, monthly_volume, intracountry, fixed_amount, rate.

Empty list [] = null = applies to all values of that field. A rule with account_type: [] applies to every account type. Same for merchant_category_code: [], aci: [].

Question Types

Type 1 — Credit/Debit Average

"For credit transactions, what would be the average fee that [scheme] would charge for a transaction value of X EUR?"

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

scheme = 'GlobalCard'  # from question
txn = 50               # from question
is_credit = True       # from question ("credit" → True, "debit" → False)

applicable = [r for r in fees
              if r['card_scheme'] == scheme
              and (r['is_credit'] is None or r['is_credit'] == is_credit)]

fee_list = [r['fixed_amount'] + r['rate'] * txn / 10000 for r in applicable]
avg = sum(fee_list) / len(fee_list)
print(round(avg, 6))

Type 2 — Account Type Only Average

"For account type R, what would be the average fee that [scheme] would charge for a transaction value of X EUR?"

scheme = 'NexPay'
account_type = 'R'
txn = 1000

applicable = [r for r in fees
              if r['card_scheme'] == scheme
              and (not r['account_type'] or account_type in r['account_type'])]

fee_list = [r['fixed_amount'] + r['rate'] * txn / 10000 for r in applicable]
avg = sum(fee_list) / len(fee_list)
print(round(avg, 6))

Type 3 — Account Type + MCC Average

"For account type H and the MCC description: Drinking Places..., what would be the average fee that [scheme] would charge for X EUR?"

First, look up the MCC numeric code from merchant_category_codes.csv:

import pandas as pd
mcc_df = pd.read_csv('merchant_category_codes.csv')
# Search by partial description match
row = mcc_df[mcc_df['description'].str.contains('Drinking Places', case=False)]
mcc_code = int(row['mcc'].iloc[0])  # e.g., 5813

Then filter:

scheme = 'GlobalCard'
account_type = 'H'
txn = 50

applicable = [r for r in fees
              if r['card_scheme'] == scheme
              and (not r['account_type'] or account_type in r['account_type'])
              and (not r['merchant_category_code'] or mcc_code in r['merchant_category_code'])]

fee_list = [r['fixed_amount'] + r['rate'] * txn / 10000 for r in applicable]
avg = sum(fee_list) / len(fee_list)
print(round(avg, 6))

Type 4 — Average Scenario Comparison

"In the average scenario, which card scheme would provide the cheapest/most expensive fee for a transaction value of X EUR?"

Average ALL rules for each card scheme (no additional filtering):

from collections import defaultdict

txn = 50  # from question
scheme_fees = defaultdict(list)
for r in fees:
    scheme_fees[r['card_scheme']].append(r['fixed_amount'] + r['rate'] * txn / 10000)

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

# cheapest:
answer = min(avg_by_scheme, key=avg_by_scheme.get)
# most expensive:
answer = max(avg_by_scheme, key=avg_by_scheme.get)
print(answer)

Output Format

• Numerical answers: round to 6 decimal places (e.g., 0.315937)
• Card scheme name answers: exact name as in fees.json (e.g., GlobalCard, NexPay, SwiftCharge, TransactPlus)
• Format: <answer>VALUE</answer>

Key Rules Summary

MCC Lookup

When the question gives an MCC description instead of a numeric code, look it up:

import pandas as pd
mcc_df = pd.read_csv('merchant_category_codes.csv')
# Use partial match for robustness:
keyword = 'Drinking Places'  # distinctive keyword from description
mcc_code = int(mcc_df[mcc_df['description'].str.contains(keyword, case=False)]['mcc'].iloc[0])

Validation Checklist

1. Used the correct fee formula? fixed_amount + rate * txn / 10000
2. Included rules where the list field is empty [] (= applies to all)?
3. For "credit transactions": filtered is_credit == True or is_credit is None?
4. For "average scenario": used ALL rules per scheme, no additional filters?
5. Rounded to 6 decimals?
6. MCC code looked up from CSV (not guessed)?