Solve dabstep Fee_Delta_and_Impact_Simulation questions: computing fee deltas when a fee's rate changes, identifying which merchants are affected by fee rule changes, and computing fee deltas when a merchant's MCC changes. Use when asked about fee impact, delta payments, rate changes, MCC code changes, or which merchants would be affected by modifying a fee rule.
Solve dabstep Fee_Delta_and_Impact_Simulation questions: computing fee deltas when a fee's rate changes, identifying which merchants are affected by fee rule changes, and computing fee deltas when a merchant's MCC changes. Use when asked about fee impact, delta payments, rate changes, MCC code changes, or which merchants would be affected by modifying a fee rule.
Fee Delta and Impact Simulation
This skill covers three question types:
Rate-change delta: How much more/less would a merchant pay if a specific fee's rate changed?
Affected-merchant identification: Which merchants would be impacted if a fee rule's applicability changed?
MCC-change delta: How much more/less would a merchant pay if its MCC code changed?
CRITICAL: Submit Immediately After Computing
Do NOT spend extra turns verifying individual transactions or investigating unmatched transactions. Once you have the computed delta, print it and submit. It is completely normal for 40–70% of transactions to have no matching fee rule — this is expected behavior, not a bug.
Data Files
File
Purpose
fees.json
1000 fee rules with matching criteria and rates
payments.csv
138,236 transactions (all year 2023)
merchant_data.json
30 merchants with account_type, MCC, capture_delay, acquirers
acquirer_countries.csv
Maps acquirer names → country codes (rarely needed)
Critical: Empty list [] for account_type, merchant_category_code, and aci means "applies to all values".
When multiple fees match a transaction: Use the first matching fee — iterate fees.json in order (fees are sorted by ascending ID) and return the FIRST fee that satisfies ALL criteria. Do NOT pick the most-specific one or sum them all.
Capture Delay Mapping
Merchant capture_delay values must be mapped to fee rule categories:
defcapture_delay_matches(merchant_val, fee_val):
if fee_val isNone: returnTrueif merchant_val in ('immediate', 'manual'): return merchant_val == fee_val
n = int(merchant_val)
if fee_val == '<3': return n < 3if fee_val == '3-5': return3 <= n <= 5if fee_val == '>5': return n > 5returnFalse
Monthly Fraud Level Calculation
Compute per merchant per natural calendar month (using day_of_year):
fraud_volume = sum(eur_amount where has_fraudulent_dispute == True)
total_volume = sum(eur_amount)
fraud_ratio = fraud_volume / total_volume * 100# as percentage# Map to fee rule category:# '<7.2%' : ratio < 7.2# '7.2%-7.7%': 7.2 <= ratio <= 7.7# '7.7%-8.3%': 7.7 <= ratio <= 8.3# '>8.3%' : ratio > 8.3
Monthly Volume Calculation
Compute per merchant per natural calendar month:
monthly_total = sum(eur_amount) # in euros# Map to fee rule category:# '<100k' : total < 100,000# '100k-1m': 100,000 <= total <= 1,000,000# '1m-5m' : 1,000,000 < total <= 5,000,000# '>5m' : total > 5,000,000
Month → day_of_year (2023, non-leap year)
Month
day_of_year range
January
1–31
February
32–59
March
60–90
April
91–120
May
121–151
June
152–181
July
182–212
August
213–243
September
244–273
October
274–304
November
305–334
December
335–365
Intracountry
Use acquirer_country directly from payments.csv (already resolved). Compare with issuing_country:
Question pattern: "In [period] what delta would [merchant] pay if the relative fee of the fee with ID=[X] changed to [Y]?"
CRITICAL: Do NOT Check First-Match for Rate-Change
For Q1, do NOT verify whether fee X is the first matching fee for each transaction. The question asks for the direct impact of changing fee X's rate. Simply filter all transactions that match fee X's criteria (card_scheme, account_type, MCC, capture_delay, is_credit, aci, intracountry) and compute the delta. This is intentional — you are computing the delta on all transactions to which fee X's criteria apply.
Step-by-step Algorithm
import json, pandas as pd
# 1. Load fee rulewithopen('fees.json') as f:
fees = json.load(f)
fee = next(f for f in fees if f['ID'] == X)
old_rate = fee['rate']
new_rate = Y # from question# 2. Load merchant propertieswithopen('merchant_data.json') as f:
merchants = json.load(f)
merchant = next(m for m in merchants if m['merchant'] == MERCHANT_NAME)
# 3. Load payments and filter by merchant + time period
payments = pd.read_csv('payments.csv')
mask = (payments['merchant'] == MERCHANT_NAME)
# For a specific month: also filter day_of_year range# For a full year: no day_of_year filter needed# 4. Check merchant-level criteria (checked once, not per transaction)if fee['account_type'] and merchant['account_type'] notin fee['account_type']:
print("Delta = 0 (merchant does not match fee's account_type)")
elif fee['merchant_category_code'] and merchant['merchant_category_code'] notin fee['merchant_category_code']:
print("Delta = 0 (merchant does not match fee's MCC)")
elif fee['capture_delay'] isnotNoneandnot capture_delay_matches(merchant['capture_delay'], fee['capture_delay']):
print("Delta = 0 (merchant does not match fee's capture_delay)")
else:
# 5. Filter transactions by fee's transaction-level criteria
mask &= (payments['card_scheme'] == fee['card_scheme'])
if fee['is_credit'] isnotNone:
mask &= (payments['is_credit'] == fee['is_credit'])
if fee['aci']: # non-empty list
mask &= payments['aci'].isin(fee['aci'])
if fee['intracountry'] isnotNone:
if fee['intracountry'] == 1.0:
mask &= (payments['issuing_country'] == payments['acquirer_country'])
else:
mask &= (payments['issuing_country'] != payments['acquirer_country'])
# 6. Handle monthly conditions (if fee has monthly_fraud_level or monthly_volume)# If both are null: just sum all matching transactions# If either is non-null: must filter month by month
matching = payments[mask]
total_amount = matching['eur_amount'].sum()
delta = (new_rate - old_rate) * total_amount / 10000print(f"{delta:.14f}")
Important: Monthly Conditions for Rate-Change
When the fee has monthly_fraud_level or monthly_volume != null, these are merchant-level, month-level conditions. For a single-month query, calculate for that month. For a full-year query, the fee may apply in some months but not others — filter transactions month-by-month.
Question Type 2: Affected Merchants (Account Type Change)
Question pattern: "During 2023, imagine if the Fee with ID [X] was only applied to account type [T], which merchants would have been affected by this change?"
Interpretation
"Affected" = merchants whose fee X application STATUS CHANGES:
Currently: if account_type = [] (applies to all), ALL matching merchants use fee X
After change: only account_type T merchants use fee X
Affected = merchants whose current status ≠ new status AND who have transactions matched by other fee criteria
Step-by-step Algorithm
# 1. Load fee rule
fee = next(f for f in fees if f['ID'] == X)
# 2. For each merchant, check if fee status changes
affected = []
for m in merchants:
# Check merchant-level criteria (MCC, capture_delay) - excluding account_typeif fee['merchant_category_code'] and m['merchant_category_code'] notin fee['merchant_category_code']:
continueif fee['capture_delay'] isnotNoneandnot capture_delay_matches(m['capture_delay'], fee['capture_delay']):
continue# Check if merchant has any matching transactions (transaction-level filters)
m_payments = payments[payments['merchant'] == m['merchant']]
mask = (m_payments['card_scheme'] == fee['card_scheme'])
if fee['is_credit'] isnotNone:
mask &= (m_payments['is_credit'] == fee['is_credit'])
if fee['aci']:
mask &= m_payments['aci'].isin(fee['aci'])
if fee['intracountry'] isnotNone:
if fee['intracountry'] == 1.0:
mask &= (m_payments['issuing_country'] == m_payments['acquirer_country'])
else:
mask &= (m_payments['issuing_country'] != m_payments['acquirer_country'])
if mask.sum() == 0:
continue# No matching transactions# Check if account_type status changes
currently_applies = not fee['account_type'] or m['account_type'] in fee['account_type']
would_apply = (m['account_type'] == T) # after the changeif currently_applies != would_apply:
affected.append(m['merchant'])
print(', '.join(sorted(affected)))
Question Type 3: MCC-Change Delta
Question pattern: "Imagine the merchant [M] had changed its MCC code to [NEW_MCC] before 2023 started, what amount delta will it have to pay in fees for the year 2023?"
Algorithm
For each transaction, find the first matching fee under the OLD MCC and NEW MCC separately. Delta = total_fees(new_mcc) - total_fees(old_mcc).
Write this as a single complete script — do not split into multiple exploration steps to avoid hitting turn limits.
import json
import pandas as pd
# Load datawithopen('fees.json') as f:
fees = json.load(f)
withopen('merchant_data.json') as f:
merchants = json.load(f)
payments = pd.read_csv('payments.csv')
# Merchant info
merchant = next(m for m in merchants if m['merchant'] == MERCHANT_NAME)
old_mcc = merchant['merchant_category_code']
new_mcc = NEW_MCC # from question
acct_type = merchant['account_type']
cap_delay = str(merchant['capture_delay'])
# Filter transactions for this merchant
df = payments[payments['merchant'] == MERCHANT_NAME].copy()
MONTH_RANGES = {
1:(1,31), 2:(32,59), 3:(60,90), 4:(91,120),
5:(121,151), 6:(152,181), 7:(182,212), 8:(213,243),
9:(244,273), 10:(274,304), 11:(305,334), 12:(335,365)
}
defday_to_month(day):
for m, (s, e) in MONTH_RANGES.items():
if s <= day <= e: return m
return12
df['month'] = df['day_of_year'].apply(day_to_month)
# Precompute monthly fraud and volume categoriesdefget_fraud_cat(ratio):
if ratio < 7.2: return'<7.2%'if ratio <= 7.7: return'7.2%-7.7%'if ratio <= 8.3: return'7.7%-8.3%'return'>8.3%'defget_vol_cat(vol):
if vol < 100000: return'<100k'if vol <= 1000000: return'100k-1m'if vol <= 5000000: return'1m-5m'return'>5m'
monthly_cats = {}
for month inrange(1, 13):
md = df[df['month'] == month]
fv = md[md['has_fraudulent_dispute'] == True]['eur_amount'].sum()
tv = md['eur_amount'].sum()
ratio = (fv / tv * 100) if tv > 0else0
monthly_cats[month] = (get_fraud_cat(ratio), get_vol_cat(tv))
defcapture_delay_matches(merchant_val, fee_val):
if fee_val isNone: returnTrueif merchant_val in ('immediate', 'manual'): return merchant_val == fee_val
n = int(merchant_val)
if fee_val == '<3': return n < 3if fee_val == '3-5': return3 <= n <= 5if fee_val == '>5': return n > 5returnFalsedeffind_first_matching_fee(tx, mcc, fraud_cat, vol_cat):
"""Return the FIRST fee (by ID order) matching all criteria, or None."""
cs = tx['card_scheme']
ic = tx['is_credit']
aci = tx['aci']
intra = (tx['issuing_country'] == tx['acquirer_country'])
for fee in fees: # fees.json is sorted by ascending IDif fee['card_scheme'] != cs: continueif fee['account_type'] and acct_type notin fee['account_type']: continueifnot capture_delay_matches(cap_delay, fee['capture_delay']): continueif fee['monthly_fraud_level'] isnotNoneand fee['monthly_fraud_level'] != fraud_cat: continueif fee['monthly_volume'] isnotNoneand fee['monthly_volume'] != vol_cat: continueif fee['merchant_category_code'] and mcc notin fee['merchant_category_code']: continueif fee['is_credit'] isnotNoneand fee['is_credit'] != ic: continueif fee['aci'] and aci notin fee['aci']: continueif fee['intracountry'] isnotNone:
if fee['intracountry'] == 1.0andnot intra: continueif fee['intracountry'] == 0.0and intra: continuereturn fee
returnNone# Calculate total fees under both MCC scenarios
total_old = 0.0
total_new = 0.0for _, tx in df.iterrows():
month = int(tx['month'])
fraud_cat, vol_cat = monthly_cats[month]
amount = tx['eur_amount']
fee_old = find_first_matching_fee(tx, old_mcc, fraud_cat, vol_cat)
if fee_old:
total_old += fee_old['fixed_amount'] + fee_old['rate'] * amount / 10000
fee_new = find_first_matching_fee(tx, new_mcc, fraud_cat, vol_cat)
if fee_new:
total_new += fee_new['fixed_amount'] + fee_new['rate'] * amount / 10000
delta = total_new - total_old
print(f"{delta:.6f}")
Common Pitfalls
[Q1] Adding first-match verification: For Rate-Change Delta, do NOT check whether fee X is the first matching fee for transactions. Simply filter by fee X's criteria. The question asks for the direct impact on all transactions matching that fee's rules.
[Q3] Using "most specific" fee: Always use the FIRST matching fee (by ascending ID order). Do NOT rank by specificity. The first fee in fees.json that matches all criteria is the applicable one.
[Q3] Splitting computation into multiple steps: Write the entire MCC-change calculation as ONE script. Iterating through 20,000–30,000 transactions is fast in Python. Debugging step-by-step exhausts turn limits.
Empty list ≠ null for account_type/MCC/aci: Both mean "applies to all", but the data uses [] not null for these list fields.
Intracountry uses payments.csv acquirer_country directly — do NOT look up acquirer_countries.csv per transaction.
Capture delay numeric mapping: merchant value "1" or "2" maps to '<3', "7" maps to '>5'. Always convert the numeric string to int before comparing.
Delta sign: delta = (new_fees - old_fees). Positive = merchant pays more; negative = merchant pays less.
Month filtering: January = day_of_year 1–31, December = 335–365. Use day_of_year column directly.
Monthly conditions are merchant+month scoped: Calculate fraud level / volume from ALL of that merchant's transactions in that natural month (not just the filtered subset).
Rate-change output format: Round to 14 decimal places. Example: f"{delta:.14f}".
MCC-change output format: Round to 6 decimal places. Example: f"{delta:.6f}".
Unmatched transactions are normal: 40–70% of transactions having no matching fee is expected. Do not investigate or try to fix this.
