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). Key fields per rule:

• card_scheme: string — GlobalCard, NexPay, SwiftCharge, TransactPlus
• account_type: list — [] means applies to ALL account types (R, D, H, F, S, O)
• merchant_category_code: list — [] means applies to ALL MCCs
• aci: list — [] means applies to ALL ACI values
• is_credit: bool or null — null means applies to BOTH credit and debit
• intracountry: float or null — 1.0 = domestic, 0.0 = international, null = both
• capture_delay, monthly_fraud_level, monthly_volume: string or null — null = applies to all
• fixed_amount: float (EUR), rate: integer

Filter rule: For list fields, [] = applies to all → not r['account_type'] is True for []. For scalar/bool fields, None = applies to all → r['is_credit'] is None.

Question Types

Type A — Average Scenario Comparison

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

Use ALL 1000 rules grouped by scheme — no additional filtering whatsoever.

import json
from collections import defaultdict

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

txn = 100  # 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()}

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

Type B — Filtered Average (specific scheme + conditions)

"For [account type / credit / debit / MCC / combination], what is the average fee that [scheme] would charge for X EUR?"

Filter rules matching the scheme and all stated conditions, then compute the average.

Filter logic by field type:

import json, pandas as pd

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

scheme = 'GlobalCard'
account_type = 'H'
txn = 50
# is_credit = True        # True for "credit", False for "debit"

# If MCC is given as description, look it up first:
mcc_df = pd.read_csv('merchant_category_codes.csv')
keyword = 'Drinking Places'  # distinctive keyword from the question's MCC description
mcc_code = int(mcc_df[mcc_df['description'].str.contains(keyword, case=False)]['mcc'].iloc[0])

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'])
              # and (r['is_credit'] is None or r['is_credit'] == is_credit)  # add if question specifies
             ]

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

Add or remove filter conditions based on exactly what the question specifies. Only filter on fields explicitly mentioned in the question.

Output Format

• Numerical answers: round to 6 decimal places — round(avg, 6)
• Card scheme name: exact case from fees.json — GlobalCard, NexPay, SwiftCharge, TransactPlus
• Wrap in: <answer>VALUE</answer>

Validation Checklist

1. Formula: fixed_amount + rate * txn / 10000?
2. Empty-list rules included? not r['account_type'] → True for []?
3. Null scalar fields included? r['is_credit'] is None → include?
4. "Average scenario": ALL rules used, NO extra filters?
5. MCC code looked up from CSV, not guessed?
6. Rounded to 6 decimal places?
7. At least 1 applicable rule found? (if 0, re-check filter logic)