average-fee-estimation

name

Average_Fee_Estimation

description

Solve dabstep questions that ask for average payment processing fees. Use this skill for: (1) computing the average fee a specific card scheme charges for a given transaction value, filtered by credit/debit type, account type, or MCC description; (2) determining which card scheme provides the cheapest or most expensive fee in an average scenario. Trigger whenever a question involves average fee, fee estimation, card scheme fee, or fee comparison in the dabstep payment dataset.

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"

Field type	"Applies to all" value
Scalar (is_credit, capture_delay, etc.)	null
List (account_type, merchant_category_code, aci)	[] (empty list)

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

Account Type Codes

Code	Meaning
R	Enterprise - Retail
D	Enterprise - Digital
H	Enterprise - Hospitality
F	Platform - Franchise
S	Platform - SaaS
O	Other

---

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

Task	Query	Approach	Answer
1277	GlobalCard, credit, 50 EUR	is_credit=True or None → 144 rules	0.315937
1279	SwiftCharge, credit, 50 EUR	is_credit=True or None → 156 rules	0.338686
1281	GlobalCard, credit, 100 EUR	is_credit=True or None → 144 rules	0.560694
1538	NexPay, acct=R, 1000 EUR	acct=R or [] → 167 rules	5.625868
1570	NexPay, acct=D, 1000 EUR	acct=D or [] → 167 rules	5.504371
1574	NexPay, acct=D, 5000 EUR	acct=D or [] → 167 rules	27.250479
1577	GlobalCard, acct=D, 1234 EUR	acct=D or [] → 221 rules	6.580365
1341	GlobalCard, acct=H, MCC=5813, 50 EUR	acct=H or [], MCC=5813 or [] → 74 rules	0.369324
1347	SwiftCharge, acct=H, MCC=5813, 100 EUR	acct=H or [], MCC=5813 or [] → 86 rules	0.626512
1505	Cheapest for 10 EUR	all rules per scheme (no filter)	GlobalCard
1508	Most expensive for 50 EUR	all rules per scheme (no filter)	NexPay
1509	Cheapest for 100 EUR	all rules per scheme (no filter)	GlobalCard
1515	Cheapest for 5000 EUR	all rules per scheme (no filter)	GlobalCard