fee-delta-and-impact-simulation

name

Fee_Delta_and_Impact_Simulation

description

Solve dabstep questions about fee deltas from rate changes and fee impact simulations. Use for: (1) computing the monetary delta a merchant would pay if a fee's relative rate changed to a new value in a specific time period; (2) determining which merchants are affected when a fee's account_type restriction changes; (3) computing the fee delta if a merchant changed its MCC code. Trigger whenever a question involves "delta", "relative fee", "fee changed", "affected merchants", "MCC code changed", or simulating how a fee rule change impacts merchant payments.

Fee Delta and Impact Simulation

Three question types appear in this category:

1. Rate-change delta: "In [month/year] what delta would [merchant] pay if the relative fee of fee ID=[N] changed to [new_rate]?"
2. Account-type impact: "During [year], if fee ID=[N] was only applied to account type [X], which merchants would have been affected?"
3. MCC-change delta: "If merchant M had changed its MCC to X before [year], what delta would it pay?"

Data Sources

• fees.json — ~1000 fee rule objects
• merchant_data.json — merchant properties (account_type, capture_delay, acquirers, MCC)
• payments.csv — transaction records (issuing_country, acquirer_country per row)
• manual.md — field definitions and fee formula (read first)

Fee Formula

fee = fixed_amount + rate * transaction_value / 10000

"Relative fee" = the rate field. Delta = new_total_fee − old_total_fee.

Fee Rule Field Semantics

Field	Null/empty meaning
account_type (list)	[] → applies to all account types
merchant_category_code (list)	[] → applies to all MCCs
aci (list)	[] → applies to all ACI values
is_credit (bool)	null → applies to both credit and debit
capture_delay, monthly_fraud_level, monthly_volume, intracountry	null → applies to all values

[] means "no restriction" (match all), NOT "match nothing".

---

CRITICAL: Unmatched Transactions Are Expected — Do NOT Investigate

When computing total fees, some transactions will match zero fee rules. This is correct behavior, not a bug. The fee dataset does not guarantee a catch-all rule for every combination of card_scheme, MCC, account_type, capture_delay, aci, is_credit, intracountry, and monthly thresholds. A transaction with no matching fee rule contributes 0 to the total fee.

Do NOT verify results by sampling individual transactions to check whether fees match them. Doing so wastes turns, misleads the analysis, and causes the computation to fail before producing an answer.

After computing the delta, output it immediately as the final answer.

---

Helper Functions (reuse across all question types)

import json, pandas as pd

with open('fees.json') as f: fees = json.load(f)
with open('merchant_data.json') as f: merchants = json.load(f)
payments = pd.read_csv('payments.csv')

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)
}

def day_to_month(d):
    for m,(s,e) in MONTH_RANGES.items():
        if s <= d <= e: return m
    return 12

def ():
     fee_cd  :  
     fee_cd  (, ):  merch_cd == fee_cd
    :
        days = (merch_cd)
         fee_cd == :   days < 
         fee_cd == :   <= days <= 
         fee_cd == :   days > 
    :  
     

 ():
     rule  :  
     rule == :       rate_pct < 
     rule == :   <= rate_pct < 
     rule == :   <= rate_pct < 
     rule == :      rate_pct > 
     

 ():
     rule  :  
     rule == :    vol < 
     rule == :   <= vol < 
     rule == :     <= vol < 
     rule == :      vol >= 
     

---

Question Type 1: Rate-Change Delta

Algorithm

1. Find target fee by ID. Get merchant's properties.
2. Filter payments for merchant + time period.
3. Apply all fee matching criteria as filters.
4. delta = (new_rate - old_rate) * matching_df['eur_amount'].sum() / 10000

fee = next(r for r in fees if r['ID'] == target_fee_id)
merchant = next(m for m in merchants if m['merchant'] == merchant_name)

# Filter for merchant and time period
s, e = MONTH_RANGES[month_num]  # or use all rows for full year
df = payments[(payments['merchant'] == merchant_name) &
              (payments['day_of_year'] >= s) & (payments['day_of_year'] <= e)].copy()

# card_scheme (always required)
df = df[df['card_scheme'] == fee['card_scheme']]

# is_credit (null = both)
if fee['is_credit'] is not None:
    df = df[df['is_credit'] == fee['is_credit']]

# aci ([] = all)
if fee['aci']:
    df = df[df['aci'].isin(fee['aci'])]

# merchant_category_code ([] = all)
if fee['merchant_category_code']:
    if merchant['merchant_category_code'] not in fee['merchant_category_code']:
        df = df.iloc[0:0]

# account_type ([] = all)
if fee['account_type']:
    if merchant['account_type'] not in fee[]:
        df = df.iloc[:]


  capture_delay_matches(fee[], merchant[]):
    df = df.iloc[:]


 fee[]   :
    is_intra = df[] == df[]
    df = df[is_intra == (fee[] == )]


 fee[]     fee[]   :
    df[] = df[].apply(day_to_month)
    keep = []
     mo, mdf  df.groupby():
        s2, e2 = MONTH_RANGES[mo]
        m_txns = payments[(payments[] == merchant_name) &
                          (payments[] >= s2) & (payments[] <= e2)]
        total = m_txns[].()
        fraud_pct = (m_txns[m_txns[] == ][].()
                     / total * )  total >   
         (fraud_level_matches(fee[], fraud_pct) 
                volume_matches(fee[], total)):
            keep.append(mdf)
    df = pd.concat(keep)  keep  df.iloc[:]

old_rate = fee[]
delta = (new_rate - old_rate) * df[].() / 
((delta, ))

---

Question Type 2: Account-Type Impact Simulation

"If fee ID=X was only applied to account type Y, which merchants would have been affected?"

Affected = merchants whose status changes (currently get the fee but wouldn't, or vice versa).

fee = next(r for r in fees if r['ID'] == target_fee_id)
merchant_lookup = {m['merchant']: m for m in merchants}

# Find all merchants who have at least one transaction matching fee's non-account_type criteria
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'])
# Note: intracountry, MCC, capture_delay are per-merchant and checked below
candidate_merchants = payments[mask]['merchant'].unique()

def merchant_matches_fee(m_name, acct_type_list):
    if m_name not in merchant_lookup: return False
    merch = merchant_lookup[m_name]
    if acct_type_list and merch['account_type'] not in acct_type_list: return False
    if not capture_delay_matches(fee['capture_delay'], merch['capture_delay']): return False
    if fee['merchant_category_code'] and merch[]   fee[]:  
     

current_set = {m  m  candidate_merchants  merchant_matches_fee(m, fee[])}
new_set = {m  m  candidate_merchants  merchant_matches_fee(m, [new_account_type])}

affected = ((current_set - new_set) | (new_set - current_set))
(.join(affected))

---

Question Type 3: MCC-Change Delta

"If merchant M had changed its MCC to X before [year], what delta would it pay?"

Compute total fees under original MCC and under the new MCC; delta = new_total − original_total.

Key rule: For each transaction, find the best-matching fee = the matching fee rule with the highest specificity score. Specificity = 1 point per constrained field (binary, not length-based):

def specificity(fee):
    """Count fields that actively constrain (non-null / non-empty list)."""
    return sum([
        fee['card_scheme'] is not None,          # always non-null
        bool(fee['account_type']),               # non-empty list
        fee['capture_delay'] is not None,
        fee['monthly_fraud_level'] is not None,
        fee['monthly_volume'] is not None,
        bool(fee['merchant_category_code']),     # non-empty list
        fee['is_credit'] is not None,
        bool(fee['aci']),                        # non-empty list
        fee['intracountry'] is not None,
    ])

Complete Implementation

import json, pandas as pd

with open('fees.json') as f: fees = json.load(f)
with open('merchant_data.json') as f: merchants = json.load(f)
payments = pd.read_csv('payments.csv')

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)
}
def day_to_month(d):
    for m,(s,e) in MONTH_RANGES.items():
        if s<=d<=e: return m
    return 12

merchant_name = 'TARGET_MERCHANT'
new_mcc = TARGET_NEW_MCC

merchant = (m  m  merchants  m[] == merchant_name)
orig_mcc = merchant[]
acct_type = merchant[]
cap_delay = merchant[]

df = payments[payments[] == merchant_name].copy()
df[] = df[].apply(day_to_month)


monthly_stats = {}
 mo, (s, e)  MONTH_RANGES.items():
    m_txns = df[df[] == mo]
    total = m_txns[].()
    fraud = m_txns[m_txns[] == ][].()
    monthly_stats[mo] = {
        : total,
        : (fraud / total * )  total >   
    }

 ():
    total_fee = 
     _, txn  df.iterrows():
        mo = txn[]
        fr = monthly_stats[mo][]
        vol = monthly_stats[mo][]
        best, best_spec = , -
         fee  fees:
            
             fee[]  acct_type   fee[]: 
              capture_delay_matches(fee[], cap_delay): 
             fee[]  mcc_code   fee[]: 
            
              fraud_level_matches(fee[], fr): 
              volume_matches(fee[], vol): 
            
             fee[] != txn[]: 
             fee[]     fee[] != txn[]: 
             fee[]  txn[]   fee[]: 
             fee[]   :
                same = txn[] == txn[]
                 fee[] ==    same: 
                 fee[] ==   same: 
            
            spec = specificity(fee)
             spec > best_spec:
                best_spec = spec
                best = fee
         best:
            total_fee += best[] + best[] * txn[] / 
     total_fee

fee_orig = calc_total_fees(orig_mcc)
fee_new  = calc_total_fees(new_mcc)
delta = fee_new - fee_orig
((delta, ))

Execution tip: Run calc_total_fees for both MCCs in a single code block. Once you have the delta printed, output it immediately as your final answer — do NOT run additional checks or sample individual transactions.

---

Common Mistakes

1. [] means "no match": Wrong. [] and null both mean "applies to all". An account_type: [] rule applies to every merchant.

2. Wrong specificity scoring: Each constrained field adds exactly 1 point regardless of list size. Do not add len(list) — add 1 if non-empty.

3. Wrong delta sign: delta = fee_new_MCC − fee_original_MCC. Negative = merchant saves money.

4. Missing field checks: Check ALL non-null fee fields: card_scheme, is_credit, aci, account_type, capture_delay, MCC, intracountry, monthly_fraud_level, monthly_volume.

5. Intracountry check: Use payments['issuing_country'] == payments['acquirer_country'] directly (both columns exist per row in payments.csv). No join needed.

6. Monthly metrics computed wrong: Monthly fraud rate and volume must be computed over the full natural month for the merchant, not just the filtered transactions.

7. Capture delay numeric: Merchant capture_delay = '7' is a string. Parse as float to compare with '>5'. Values 'immediate' and 'manual' are string-matched directly.

8. Output precision: Round to the decimal places specified in the question (usually 6). Use round(delta, 6).

9. Impact simulation: Also check merchants who gain the fee (not just lose it). Use symmetric difference: (current_set - new_set) | (new_set - current_set).

10. Unnecessary verification after computing delta: After printing the delta, do NOT try to verify by examining individual sample transactions. Some transactions legitimately match zero fee rules — this is expected and contributes 0 to the total. Investigating "unmatched" transactions wastes turns and leads to running out of budget without a final answer. Trust the calculation and output the result immediately.