Routing_and_Cost_Optimization

Routing and Cost Optimization Skill

Problem Types

Type 1 – Card Scheme Routing: "To which card scheme should merchant X steer traffic to pay minimum/maximum fees in [month/year]?"

Type 2 – ACI Optimization: "For merchant X in [month/year], if fraudulent transactions were moved to a different ACI to achieve the lowest fees, what would be the preferred ACI?"

Output format (both types): {card_scheme_or_ACI}:{fee_rounded_to_2_decimals}

• Type 1 example: SwiftCharge:37.21
• Type 2 example: C:17.82

CRITICAL DISTINCTION: Coverage Requirements

Do NOT apply the 100% coverage constraint to Type 1. This is the most common mistake. Many transactions (especially those with ACI='G') have no matching rules for some schemes — this is expected. Simply exclude those transactions from that scheme's fee total.

Never output Not Applicable solely because some transactions lack matching rules. Only return Not Applicable if the merchant has zero transactions in the specified period.

Required Data Files

Step-by-Step Algorithm

Step 1: Load Merchant Characteristics

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)

merchant = next(m for m in merchants if m['merchant'] == MERCHANT_NAME)
account_type = merchant['account_type']
mcc = merchant['merchant_category_code']

Map capture_delay to fee rule categories:

def map_capture_delay(cd):
    if cd in ('immediate', 'manual'): return cd
    days = int(cd)
    if days < 3: return '<3'
    elif days <= 5: return '3-5'
    else: return '>5'

capture_delay = map_capture_delay(merchant['capture_delay'])

Step 2: Filter Transactions

payments = pd.read_csv('payments.csv')

Day-of-year ranges for 2023 (non-leap year):

For full-year questions use the full range 1–365.

period_txns = payments[
    (payments['merchant'] == MERCHANT_NAME) &
    (payments['day_of_year'] >= DOY_START) &
    (payments['day_of_year'] <= DOY_END)
].copy()
period_txns['intracountry'] = period_txns['issuing_country'] == period_txns['acquirer_country']

Step 3: Fee Rule Matching Function

def rule_matches(rule, account_type, capture_delay, mcc, is_credit, intracountry, aci):
    """
    Empty list [] or None for account_type/aci/merchant_category_code means 'applies to all'.
    """
    if rule['account_type'] and account_type not in rule['account_type']:
        return False
    if rule['capture_delay'] is not None and rule['capture_delay'] != capture_delay:
        return False
    if rule['merchant_category_code'] and mcc not in rule['merchant_category_code']:
        return False
    if rule['is_credit'] is not None and rule['is_credit'] != is_credit:
        return False
    if rule['intracountry'] is not None and bool(rule['intracountry']) != intracountry:
        return False
    if rule['aci'] and aci not in rule['aci']:
        return False
    return True

Fee formula: fee = fixed_amount + rate * eur_amount / 10000

When multiple rules match: use the minimum fee among all matching rules.

No matching rules: skip the transaction (contributes 0 to that scheme/ACI's total).

monthly_fraud_level / monthly_volume fields: Ignore for rule matching — these fields do NOT restrict which rules apply.

Step 4A: Card Scheme Routing (Type 1)

Iterate over ALL merchant transactions for the period. Use each transaction's actual card_scheme field. Accumulate fees by scheme. Transactions with no matching rules are simply skipped.

fees_by_scheme = {}
for _, txn in period_txns.iterrows():
    scheme = txn['card_scheme']
    is_credit = bool(txn['is_credit'])
    aci = txn['aci']
    intracountry = bool(txn['intracountry'])
    amount = txn['eur_amount']

    matches = [
        rule['fixed_amount'] + rule['rate'] * amount / 10000
        for rule in fees
        if rule['card_scheme'] == scheme and
           rule_matches(rule, account_type, capture_delay, mcc, is_credit, intracountry, aci)
    ]
    if matches:
        fees_by_scheme[scheme] = fees_by_scheme.get(scheme, 0) + min(matches)
    # No match → skip (contributes 0); do NOT disqualify the scheme

# For minimum fees:
answer_scheme = min(fees_by_scheme, key=fees_by_scheme.get)
# For maximum fees:
answer_scheme = max(fees_by_scheme, key=fees_by_scheme.get)

answer_fee = round(fees_by_scheme[answer_scheme], 2)
print(f"{answer_scheme}:{answer_fee}")

Key points for Type 1:

• We are comparing total fees per scheme based on that scheme's actual transactions.
• A scheme that processes 100 transactions but only 80 have matching rules contributes the sum of those 80 transactions' fees.
• Schemes with very few matched transactions naturally have lower totals — this is expected and valid; pick the minimum/maximum as computed.
• Do NOT require 100% coverage. Do NOT exclude schemes with unmatched transactions.

Step 4B: ACI Optimization for Fraudulent Transactions (Type 2)

The question asks which alternative ACI gives the lowest total fees for the fraudulent transactions.

fraud_txns = period_txns[period_txns['has_fraudulent_dispute'] == True].copy()

aci_options = ['A', 'B', 'C', 'D', 'E', 'F']  # Explore alternatives to current ACI (usually 'G')
aci_totals = {}
aci_no_rule = {}

for target_aci in aci_options:
    total = 0.0
    missing = 0
    for _, txn in fraud_txns.iterrows():
        scheme = txn['card_scheme']
        is_credit = bool(txn['is_credit'])
        intracountry = bool(txn['intracountry'])
        amount = txn['eur_amount']

        matches = [
            rule['fixed_amount'] + rule['rate'] * amount / 10000
            for rule in fees
            if rule['card_scheme'] == scheme and
               rule_matches(rule, account_type, capture_delay, mcc, is_credit, intracountry, target_aci)
        ]
        if matches:
            total += min(matches)
        else:
            missing += 1

    aci_totals[target_aci] = total
    aci_no_rule[target_aci] = missing

# For Type 2: ONLY consider ACIs with zero missing rules (100% coverage required)
valid_acis = {a: t for a, t in aci_totals.items() if aci_no_rule[a] == 0}
best_aci = min(valid_acis, key=valid_acis.get)
answer_fee = round(valid_acis[best_aci], 2)
print(f"{best_aci}:{answer_fee}")

Common Pitfalls

1. Empty list ≠ "no match": [] for account_type, aci, merchant_category_code means "applies to all values".
2. Capture delay must be mapped: Numeric strings like "7" → ">5", "2" → "<3", "1" → "<3".
3. Intracountry from payments.csv: Compute issuing_country == acquirer_country directly; no need for acquirer_countries.csv.
4. Multiple matching rules: Always take the minimum fee among all matching rules per transaction.
5. Do NOT apply coverage constraint to Type 1: Only Type 2 (ACI) excludes options with missing rules. Type 1 (card scheme) sums whatever fees are computable.
6. Do NOT output null for unmatched transactions: High unmatched counts are normal (ACI='G' has no explicit rules for most schemes). Just sum matched fees and report the min/max.
7. ACI 'G' in Type 2: No explicit rules target ACI 'G'. The current fraudulent transactions use 'G'; explore alternatives A–F.

Answer Format

• Card scheme question: {card_scheme}:{total_fee_rounded_to_2_decimals}
  • Example: SwiftCharge:37.21
• ACI question: {ACI}:{total_fee_rounded_to_2_decimals}
  • Example: C:17.82
• Return Not Applicable only if the merchant has zero transactions in the specified period.