routing-and-cost-optimization

name	Routing_and_Cost_Optimization
description	Solve payment routing and cost optimization problems in the dabstep dataset. Use this skill when asked to determine the optimal card scheme or Authorization Characteristics Indicator (ACI) for a merchant to minimize or maximize fees, or when questions involve routing fraudulent transactions to a different ACI. Applies to monthly or annual fee optimization across merchants and time periods.

Routing and Cost Optimization

Solve payment routing optimization problems: find which card scheme or ACI minimizes/maximizes fees for a merchant over a time period.

Question Types

1. Card Scheme Routing: "Which card scheme should merchant X steer traffic to in [month/year] to pay [minimum/maximum] fees?"
2. ACI Optimization: "For merchant X in [period], if we move fraudulent transactions to a different ACI, what is the preferred choice considering [lowest] fees?"

Answer format: {option}:{total_fee_rounded_to_2_decimals} — e.g., GlobalCard:142.37 or B:102.59

Required Datasets

File	Purpose
payments.csv	Transactions (merchant, card_scheme, aci, is_credit, eur_amount, issuing_country, acquirer_country, has_fraudulent_dispute, day_of_year, year)
fees.json	Fee rules (1000 rules with matching criteria + fixed_amount + rate)
merchant_data.json	Merchant profile list: account_type, capture_delay, merchant_category_code, acquirer list

Always read manual.md and payments-readme.md first for domain definitions.

Fee Calculation Formula

fee = fixed_amount + rate * transaction_value / 10000

When multiple fee rules match a transaction, select the one producing the lowest fee.

CRITICAL: Month Filtering

Always use pd.to_datetime with format='%Y-%j' and filter by .dt.month. Never compute manual day ranges.

# CORRECT - handles all months including February leap-year edge cases
payments['date'] = pd.to_datetime(
    payments['year'].astype(str) + '-' + payments['day_of_year'].astype(str),
    format='%Y-%j'
)
subset = payments[(payments['merchant'] == merchant_name) & (payments['date'].dt.month == target_month)]

# WRONG - manual day ranges are off-by-one for February and other months
subset = payments[(payments['day_of_year'] >= 32) & (payments['day_of_year'] <= 60)]  # DO NOT DO THIS

CRITICAL: Acquirer Country for Intracountry

Always use the acquirer_country column from payments.csv directly — one per transaction row.

NEVER compute acquirer country by looking up the merchant's acquirer list in acquirer_countries.csv.

# CORRECT
intracountry = row['issuing_country'] == row['acquirer_country']  # from payments.csv

# WRONG
acquirer_country = acq_countries[acq_countries['acquirer'] == merchant_acquirer]['country_code'].values[0]

CRITICAL: Card-Scheme Routing Interpretation

"Steer traffic to scheme X" means: compare fees for transactions already on each scheme — do NOT hypothetically compute fees as if ALL transactions moved to scheme X.

# CORRECT: for each scheme, iterate over transactions already on that scheme
for scheme in subset['card_scheme'].unique():
    txns = subset[subset['card_scheme'] == scheme]
    # calculate fees for these transactions only

# WRONG: do NOT apply all transactions to each scheme
for scheme in all_schemes:
    txns = subset  # all transactions — this is WRONG

merchant_data.json Structure

This file is a list (not a dict). Use a loop to find a merchant:

with open('.../merchant_data.json') as f:
    merchants = json.load(f)  # list of dicts
merchant = next(m for m in merchants if m['merchant'] == merchant_name)

Fee Rule Matching Logic

Match ALL of these criteria. A rule applies when:

Field	Rule applies when
card_scheme	Exactly matches transaction's card scheme
account_type	[] (empty list) or null → all; else merchant's type must be in list
capture_delay	null → all; else must match mapped merchant capture delay
merchant_category_code	[] (empty list) or null → all; else merchant's MCC must be in list
is_credit	null → all; else must match transaction's is_credit
aci	[] (empty list) or null → all; else transaction/target ACI must be in list
intracountry	null → all; 1.0/true → domestic only; 0.0/false → international only
monthly_fraud_level	null → all; else merchant's period fraud % must fall in range
monthly_volume	null → all; else merchant's period EUR volume must fall in range

Empty list [] = applies to all (same as null). This is the most common mistake to avoid.

Capture Delay Mapping

Merchant's capture_delay in merchant_data.json may be numeric (days) or a string bracket:

• "0" or "immediate" → immediate
• "1" or "2" (1-2 days) → <3
• "3", "4", "5" (3-5 days) → 3-5
• "6", "7", or any number > 5 → >5
• "manual" → manual

Range Parsing

Monthly fraud level (fraudulent_volume / total_volume × 100):

'<7.2%'    → fraud_pct < 7.2
'7.2%-7.7%' → 7.2 <= fraud_pct <= 7.7
'7.7%-8.3%' → 7.7 <= fraud_pct <= 8.3
'>8.3%'    → fraud_pct > 8.3

Monthly volume (sum of eur_amount in EUR):

'<100k'   → volume < 100_000
'100k-1m' → 100_000 <= volume <= 1_000_000
'1m-5m'   → 1_000_000 < volume <= 5_000_000
'>5m'     → volume > 5_000_000

Step-by-Step Solution Process

Step 1: Load data

import pandas as pd, json
payments = pd.read_csv('.../payments.csv')
fees = json.load(open('.../fees.json'))
merchants = json.load(open('.../merchant_data.json'))  # list!

Step 2: Filter transactions for the period

# Monthly question (e.g., September):
payments['date'] = pd.to_datetime(
    payments['year'].astype(str) + '-' + payments['day_of_year'].astype(str), format='%Y-%j'
)
subset = payments[(payments['merchant'] == merchant_name) & (payments['date'].dt.month == 9)]

# Annual question (e.g., 2023):
subset = payments[(payments['merchant'] == merchant_name) & (payments['year'] == 2023)]

Step 3: Compute period metrics

total_volume = subset['eur_amount'].sum()
fraud_volume = subset[subset['has_fraudulent_dispute'] == True]['eur_amount'].sum()
fraud_pct = fraud_volume / total_volume * 100

For annual questions, compute these over the entire year (not per natural month).

Step 4: Determine intracountry per transaction

subset = subset.copy()
subset['intracountry'] = subset['issuing_country'] == subset['acquirer_country']

Step 5: Build the fee matching function

def matches_rule(rule, account_type, capture_delay, fraud_pct, volume, mcc,
                 is_credit, aci, intracountry):
    at = rule.get('account_type') or []
    if at and account_type not in at: return False
    cd = rule.get('capture_delay')
    if cd is not None and cd != capture_delay: return False
    mfl = rule.get('monthly_fraud_level')
    if mfl:
        if mfl == '<7.2%' and not fraud_pct < 7.2: return False
        elif mfl == '7.2%-7.7%' and not (7.2 <= fraud_pct <= 7.7): return False
        elif mfl == '7.7%-8.3%' and not (7.7 <= fraud_pct <= 8.3): return False
        elif mfl == '>8.3%' and not fraud_pct > 8.3: return False
    mv = rule.get('monthly_volume')
     mv:
         mv ==    volume < :  
         mv ==    ( <= volume <= ):  
         mv ==    ( < volume <= ):  
         mv ==    volume > :  
    mccs = rule.get()  []
     mccs  mcc   mccs:  
    ic = rule.get()
     ic     ic != is_credit:  
    rule_aci = rule.get()  []
     rule_aci  aci   rule_aci:  
    intra = rule.get()
     intra     (intra) != intracountry:  
     

 ():
     rule[] + rule[] * amount / 

Step 6: Calculate total fees per option

Card-scheme routing: iterate over transactions already on each scheme. ACI optimization: iterate over only fraudulent transactions, testing each ACI (A–F).

# Card-scheme routing
results = {}
for scheme in subset['card_scheme'].unique():
    txns = subset[subset['card_scheme'] == scheme]
    total_fee = 0.0
    no_rule_count = 0
    for _, row in txns.iterrows():
        matching = [r for r in fees
                    if r['card_scheme'] == row['card_scheme']
                    and matches_rule(r, account_type, capture_delay, fraud_pct, total_volume,
                                     mcc, row['is_credit'], row['aci'], row['intracountry'])]
        if matching:
            total_fee += min(calc_fee(r, row['eur_amount']) for r in matching)
        else:
            no_rule_count += 1  # contributes 0 to total fee
    results[scheme] = {'total_fee': total_fee, 'no_rule': no_rule_count}

# ACI optimization (fraudulent transactions only)
fraud_txns = subset[subset['has_fraudulent_dispute'] == True].copy()
# intracountry must already be set on subset before filtering
aci_options = ['A', 'B', 'C', 'D', 'E', 'F']  # G has no fee rules

results = {}
for aci_target in aci_options:
    total_fee = 0.0
    no_rule_count = 0
    for _, row  fraud_txns.iterrows():
        matching = [r  r  fees
                     r[] == row[]
                     matches_rule(r, account_type, capture_delay, fraud_pct, total_volume,
                                     mcc, row[], aci_target, row[])]
         matching:
            total_fee += (calc_fee(r, row[])  r  matching)
        :
            no_rule_count += 
    results[aci_target] = {: total_fee, : no_rule_count}

Step 7: Select and format answer — do this immediately after computing results

For card-scheme questions: Pick the scheme with min/max total fee across all schemes. Transactions without matching rules contribute 0 — do NOT filter by coverage.

For ACI questions: Only consider ACIs with full coverage (no transactions without a matching rule). Among fully-covered ACIs, pick the one with lowest total fee.

# Card scheme: no coverage filter
best = min(results, key=lambda k: results[k]['total_fee'])  # or max for maximum
fee = round(results[best]['total_fee'], 2)
print(f"{best}:{fee}")

# ACI: filter to full coverage first
full_coverage = {k: v for k, v in results.items() if v['no_rule'] == 0}
best = min(full_coverage, key=lambda k: full_coverage[k]['total_fee'])
fee = round(full_coverage[best]['total_fee'], 2)
print(f"{best}:{fee}")

Produce the <answer> tag immediately after computing results. Do NOT investigate why individual transactions have no matching rules — this wastes turns and causes turn-limit failures. The no-rule count is expected and normal; just proceed.

Common Pitfalls

• Wrong month filter: Using manual day ranges (e.g., days 32-60 for February) instead of pd.to_datetime(..., format='%Y-%j').dt.month. February 2023 is days 32–59, not 32–60. Always use dt.month.
• Wrong card-scheme routing: Computing fees as if all transactions were on each scheme. Only iterate over transactions already on that scheme.
• Using acquirer_countries.csv for intracountry: The acquirer_country in payments.csv is per-transaction and authoritative.
• Treating [] as "no match": Empty list in account_type, aci, or merchant_category_code means "applies to all".
• Missing ACI coverage check: For ACI optimization, ACIs with any uncovered transaction must be excluded even if their partial fee sum is lower.
• Applying ACI coverage filter to card-scheme routing: For card-scheme routing, transactions with no matching rule contribute 0 — do not exclude entire schemes.
• Not using minimum fee: When multiple rules match, always use the rule producing the lowest fee.
• Turn exhaustion from over-debugging: Once you compute results, answer immediately. Do not spend extra turns investigating no-rule transactions.