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

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CRITICAL: Submit Immediately

Once you compute fees per scheme/ACI and identify the min/max, print the answer and submit immediately. Do NOT:

• Investigate why some transactions have no matching rules
• Verify individual transaction fee calculations
• Debug rule-matching for specific transactions

It is completely normal for 50–90% of transactions to have no matching fee rule. ACI='G' appears in payments but never in any fee rule's aci list — it can only match rules where aci: []. Many scheme/ACI/account_type combinations simply have no rules; those transactions legitimately contribute 0. This is not a bug.

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CRITICAL DISTINCTION: Coverage Requirements

Problem Type	Coverage Requirement
Type 1 (card scheme routing)	NO coverage requirement. Sum fees for matched transactions only; skip unmatched (contribute 0). Pick min/max regardless of how many transactions had no matching rules.
Type 2 (ACI optimization)	MUST have 100% coverage. Only consider ACIs that have matching rules for ALL fraudulent transactions. Exclude ACIs with any missing rules.

Do NOT apply the 100% coverage constraint to Type 1. This is the most common mistake. A scheme with only 10% of transactions matched is still valid — sum those fees and include the scheme in the comparison.

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.

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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

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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):
    try:
        n = int(cd)
        return '<3' if n < 3 else ('3-5' if n <= 5 else '>5')
    except (ValueError, TypeError):
        return cd  # 'immediate' or 'manual' returned as-is

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 (or filter by year == 2023).

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'.
    intracountry in fees.json: 1.0 = domestic, 0.0 = international, None = 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.

fees_by_scheme = {}
matched_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)
        matched_by_scheme[scheme] = matched_by_scheme.get(scheme, 0) + 1

# 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}")
# SUBMIT THIS IMMEDIATELY — do not continue debugging

Key points for Type 1:

• Compare total fees per scheme based on that scheme's actual transactions.
• Schemes with very few matched transactions naturally have lower totals — this is expected and valid.
• 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']  # Current fraudulent ACIs are usually 'G'
aci_totals = {}
aci_missing = {}

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_missing[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_missing[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}")
# SUBMIT THIS IMMEDIATELY

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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 "1", "2" → "<3"; "7" → ">5"; "immediate", "manual" stay as-is.
3. Intracountry from payments.csv: Compute issuing_country == acquirer_country directly. In fees.json: 1.0 = domestic, 0.0 = international, None = all.
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 sums whatever fees are computable and picks the min/max.
6. ACI 'G' in Type 2: No explicit rules target ACI 'G'. Fraudulent transactions use 'G'; explore alternatives A–F only.
7. Do not investigate unmatched transactions: High unmatched counts (50–90%) are normal. Just sum matched fees and report the min/max.

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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
• Return Not Applicable only if the merchant has zero transactions in the specified period.