routing-and-cost-optimization

name

Routing_and_Cost_Optimization

description

Solves payment routing and cost optimization problems in the dabstep dataset. Use when the question asks which card scheme a merchant should steer traffic to (for minimum or maximum fees), or which ACI (Authorization Characteristics Indicator) to incentivize for fraudulent transactions to achieve the lowest possible fees. Triggers on: "steer traffic", "card scheme", "minimum fees", "maximum fees", "ACI", "incentivize", "fraudulent transactions", "lowest possible fees", payment routing.

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

---

Required Data Files

File	Purpose
manual.md	Fee formula, field definitions (READ FIRST)
payments-readme.md	Column descriptions for payments.csv
payments.csv	Transaction data
fees.json	1000 fee rules
merchant_data.json	account_type, capture_delay, mcc, acquirers

Always read manual.md and payments-readme.md first.

---

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):

Month	Start	End
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

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):
    """
    Match a fee rule to a transaction's characteristics.
    Empty list [] 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.

Critical note on monthly metrics: Do NOT filter fee rules by monthly_fraud_level or monthly_volume. These fields add additional context but should not restrict which rules are considered applicable — always pick the minimum available fee across all matching rules regardless of monthly tier.

ACI 'G': No explicit rules in fees.json target ACI 'G'. When computing alternatives, ACI G transactions have limited rule coverage.

Step 4A: Card Scheme Routing (Type 1)

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)

# 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}")

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']  # Exclude 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

# Only consider ACIs that cover ALL fraudulent transactions (no missing rules)
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}")

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Common Pitfalls

1. Empty list ≠ "no match": [] for account_type, aci, merchant_category_code means "applies to all".
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 from the acquirer_country column in payments.csv directly (no need to use acquirer_countries.csv).
4. Multiple matching rules: Always take the minimum fee among all matching rules for each transaction.
5. ACI coverage check: For Type 2, only report ACIs that have matching fee rules for every fraudulent transaction. Exclude ACIs with missing coverage.
6. ACI 'G' is the current ACI for most fraudulent transactions and typically has limited/no explicit fee rules. Explore ACIs A–F as alternatives.

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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
• If no applicable data: Not Applicable