fee-delta-and-impact-simulation

name	Fee_Delta_and_Impact_Simulation
description	Solve dabstep Fee_Delta_and_Impact_Simulation questions: computing fee deltas when a fee's rate changes, and identifying which merchants are affected by fee rule changes. Use when asked about fee impact, delta payments, rate changes, or which merchants would be affected by modifying a fee rule.

Fee Delta and Impact Simulation

This skill covers two question types in the dabstep dataset:

1. Rate-change delta: How much more/less would a merchant pay if a specific fee's rate changed?
2. Affected-merchant identification: Which merchants would be impacted if a fee rule's applicability changed?

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
manual.md	Domain definitions (read first)

Key Domain Rules

Fee Formula

fee = fixed_amount + rate * transaction_value / 10000

"Relative fee" = the rate field. When a question says "relative fee changed to X", it means the rate changes from its current value to X.

Delta = fees paid under new rate minus fees paid under old rate:

delta = (new_rate - old_rate) * sum(matching_eur_amounts) / 10000

Fee Rule Matching

A transaction/merchant matches a fee rule when ALL conditions hold:

Fee field	Condition
card_scheme	Exact match with transaction's card_scheme
account_type	[] (empty list) = applies to all; else merchant's account_type must be in the list
merchant_category_code	[] (empty list) = applies to all; else merchant's MCC must be in the list
capture_delay	null = applies to all; else see mapping below
monthly_fraud_level	null = applies to all; else see calculation below
monthly_volume	null = applies to all; else see calculation below
is_credit	null = applies to all; else must equal transaction's is_credit
aci	[] (empty list) or null = applies to all; else transaction's aci must be in the list
intracountry	null = applies to all; 1.0 = issuing_country == acquirer_country; 0.0 = issuing_country != acquirer_country

Critical: Empty list [] for account_type, merchant_category_code, and aci means "applies to all values" — same as null for those fields.

Capture Delay Mapping

Merchant capture_delay values must be mapped to fee rule categories:

'immediate' → matches fee rule 'immediate'
'manual'    → matches fee rule 'manual'
numeric n   → '<3' if n < 3 (e.g., '1', '2')
              '3-5' if 3 ≤ n ≤ 5
              '>5' if n > 5 (e.g., '7')

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  # 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:

is_intracountry = (row['issuing_country'] == row['acquirer_country'])

---

Question Type 1: Rate-Change Delta

Question pattern: "In [period] what delta would [merchant] pay if the relative fee of the fee with ID=[X] changed to [Y]?"

Step-by-step Algorithm

import json, pandas as pd

# 1. Load fee rule
with open('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 properties
with open('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 if this fee can possibly apply given monthly conditions
# If fee has monthly_fraud_level or monthly_volume constraints,
# compute these per month first and check if they match
# If they don't match for any month in the period, delta = 0

# 5. Filter transactions by fee's transaction-level criteria
mask &= payments['card_scheme'] == fee['card_scheme']

if fee['is_credit'] is not None:
    mask &= payments['is_credit'] == fee['is_credit']

if fee[]:  
    mask &= payments[].isin(fee[])

 fee[]   :
     fee[] == :
        mask &= payments[] == payments[]
    :  
        mask &= payments[] != payments[]







merchant_matches = 
 fee[]:
    merchant_matches &= merchant[]  fee[]
 fee[]:
    merchant_matches &= merchant[]  fee[]
 fee[]   :
    merchant_matches &= capture_delay_matches(merchant[], fee[])

  merchant_matches:
    ()
:
    matching = payments[mask]
    total_amount = matching[].()
    delta = (new_rate - old_rate) * total_amount / 
    ()

Capture delay helper:

def capture_delay_matches(merchant_val, fee_val):
    if fee_val is None:
        return True
    if merchant_val in ('immediate', 'manual'):
        return merchant_val == fee_val
    n = int(merchant_val)
    if fee_val == '<3': return n < 3
    if fee_val == '3-5': return 3 <= n <= 5
    if fee_val == '>5': return n > 5
    return False

Important: Monthly Conditions

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 — you must filter transactions month-by-month.

If fee has no monthly constraints (both null): apply the filter once across the whole period.

Delta can be 0 if:

• The merchant's account type, MCC, or capture_delay doesn't match the fee rule
• The monthly fraud/volume level doesn't match in the queried period
• No transactions match the other criteria (card_scheme, is_credit, aci, intracountry)

---

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 that CURRENTLY use fee X AND are NOT account_type T (they LOSE the fee)

Note: merchants gaining the fee (if the rule was previously more restricted) would also be affected, but in practice the fee usually starts broad (empty list) and narrows.

Step-by-step Algorithm

# 1. Load fee rule and identify target account type T (from question)
fee = next(f for f in fees if f['ID'] == X)

# 2. Find merchants that currently match fee X's OTHER criteria
# (exclude account_type from matching — we'll check that separately)
with open('merchant_data.json') as f:
    merchants = json.load(f)

# 3. Filter payments for matching transactions
payments = pd.read_csv('payments.csv')
# Apply transaction-level filters (card_scheme, is_credit, aci, intracountry)
mask = payments['card_scheme'] == fee['card_scheme']
if fee['is_credit'] is not None:
    mask &= payments['is_credit'] == fee['is_credit']
if fee['aci']:
    mask &= payments['aci'].isin(fee['aci'])
if fee['intracountry'] is not None:
    if fee['intracountry'] == 1.0:
        mask &= payments['issuing_country'] == payments['acquirer_country']
    else:
        mask &= payments['issuing_country'] != payments['acquirer_country']

# 4. For each merchant, check if they have matching transactions
# AND whether their account_type != T (they'd lose the fee)
affected = []
for m  merchants:
    m_payments = payments[mask & (payments[] == m[])]
    
    
     fee[]  m[]   fee[]:
        
     fee[]      capture_delay_matches(m[], fee[]):
        
    
    
     (m_payments) == :
        
    
    
    currently_applies =  fee[]  m[]  fee[]
    would_apply = m[] == T  
    
     currently_applies != would_apply:
        affected.append(m[])

(.join((affected)))

---

Common Pitfalls

1. Empty list ≠ null for account_type/MCC: Both mean "applies to all", but the data uses [] not null for these list fields.

2. Intracountry uses payments.csv acquirer_country directly — do NOT look up acquirer_countries.csv per transaction (the column is already resolved in payments.csv).

3. 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.

4. Delta sign: delta = (new_rate - old_rate) * amount / 10000. Positive = merchant pays more; negative = merchant pays less.

5. Month filtering: January = day_of_year 1–31, December = 335–365. Use day_of_year column directly.

6. 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).

7. Output format: Round to 14 decimal places. Example: f"{delta:.14f}".

8. MCC change questions require full fee matching per transaction (for each transaction: find which fee rule applies under old MCC vs new MCC). This is substantially more complex and requires a complete fee-matching function.