routing-and-cost-optimization

name

Routing_and_Cost_Optimization

description

Solve payment routing and cost optimization problems in the dabstep dataset. Use this skill for questions about which card scheme to steer merchant traffic to (for minimum or maximum fees), or which Authorization Characteristics Indicator (ACI) to incentivize for fraudulent transactions to minimize fees. Always invoke this skill when the question asks about steering traffic, optimal card scheme selection, or ACI optimization with fee comparison.

Routing and Cost Optimization

Problem Types

Type 1: Card Scheme Routing

"Which card scheme should merchant X steer traffic to in order to pay min/max fees in [month/year]?"

Compute total fees per card scheme for ALL merchant transactions in the period, then pick the scheme with min/max total.

Type 2: ACI Optimization for Fraudulent Transactions

"If fraudulent transactions were moved to a different ACI, what would be the preferred choice for lowest fees?"

Compute total fees per candidate ACI for FRAUDULENT transactions only, then pick the ACI with lowest total that covers ALL fraudulent transactions.

Answer Format

{card_scheme_or_ACI}:{total_fee_rounded_to_2_decimals} Examples: GlobalCard:2800.62 or B:1167.15

Month-to-Day Mapping (2023, non-leap year)

Jan:1-31, Feb:32-59, Mar:60-90,  Apr:91-120,  May:121-151, Jun:152-181
Jul:182-212, Aug:213-243, Sep:244-273, Oct:274-304, Nov:305-334, Dec:335-365
Full year: 1-365

Core Setup

import json
import pandas as pd

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

# Get merchant characteristics
merchant_name = 'MerchantName'
merch = next(m for m in merchant_data if m['merchant'] == merchant_name)
account_type = merch['account_type']
mcc = merch['merchant_category_code']

def normalize_capture_delay(delay):
    if delay in ('immediate', 'manual'):
        return delay
    n = int(delay)
    if n < 3: return '<3'
    elif n <= 5: return '3-5'
    else: return '>5'

capture_delay_norm = normalize_capture_delay(merch['capture_delay'])

# Filter transactions for the period
DAY_START, DAY_END = 32, 59  # e.g., February
merch_txns = payments[
    (payments['merchant'] == merchant_name) &
    (payments['day_of_year'] >= DAY_START) &
    (payments['day_of_year'] <= DAY_END)
].copy()
merch_txns['intracountry'] = merch_txns['issuing_country'] == merch_txns['acquirer_country']

# Monthly metrics (from ALL merchant transactions in the period)
monthly_vol = merch_txns['eur_amount'].sum()
fraud_vol = merch_txns[merch_txns['has_fraudulent_dispute'] == True]['eur_amount'].sum()
fraud_pct = (fraud_vol / monthly_vol) * 100 if monthly_vol > 0 else 0

def get_vol_cat(vol):
    if vol < 100000: return '<100k'
    elif vol < 1000000: return '100k-1m'
    elif vol < 5000000: return '1m-5m'
    else: return '>5m'

def get_fraud_cat(pct):
    if pct < 7.2: return '<7.2%'
    elif pct < 7.7: return '7.2%-7.7%'
    elif pct < 8.3: return '7.7%-8.3%'
    else: return '>8.3%'

vol_cat = get_vol_cat(monthly_vol)
fraud_cat = get_fraud_cat(fraud_pct)

Fee Rule Matching

CRITICAL: Empty list [] means "match all" (same as null). Never treat [] as "match nothing".

def rule_matches(rule, card_scheme, aci, is_credit, intracountry,
                 acct=account_type, cap=capture_delay_norm, m=mcc,
                 vc=vol_cat, fc=fraud_cat):
    if rule['card_scheme'] != card_scheme: return False
    if rule['account_type'] and acct not in rule['account_type']: return False
    if rule['capture_delay'] is not None and rule['capture_delay'] != cap: return False
    if rule['merchant_category_code'] and m 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['aci'] and aci not in rule['aci']: return False
    if rule['intracountry'] is not None and bool(rule['intracountry']) != intracountry: return False
    if rule['monthly_fraud_level'] is not None and rule['monthly_fraud_level'] != fc: return False
    if rule['monthly_volume'] is not None and rule['monthly_volume'] != vc: return False
    return True

def get_min_fee_for_combo(card_scheme, aci, is_credit, intracountry, amount):
    applicable = [r for r in fees if rule_matches(r, card_scheme, aci, is_credit, intracountry)]
    if not applicable: return None
    return min(r['fixed_amount'] + r['rate'] * amount / 10000 for r in applicable)

Type 1: Card Scheme Routing (Monthly)

all_schemes = ['GlobalCard', 'NexPay', 'SwiftCharge', 'TransactPlus']
n = len(merch_txns)

scheme_totals = {}
for scheme in all_schemes:
    total, matched = 0, 0
    for _, txn in merch_txns.iterrows():
        fee = get_min_fee_for_combo(scheme, txn['aci'], txn['is_credit'],
                                    txn['intracountry'], txn['eur_amount'])
        if fee is not None:
            total += fee; matched += 1
    scheme_totals[scheme] = {'total': total, 'matched': matched}
    print(f"{scheme}: {round(total,2)} ({matched}/{n} matched)")

# Partial coverage (matched < n) is NORMAL for some schemes — do not debug it.
# Prefer schemes with full coverage; among those, pick min or max as required.
full = {k: v for k, v in scheme_totals.items() if v['matched'] == n}
candidates = full if full else scheme_totals
# For minimum fees: min(); for maximum fees: max()
best = min(candidates, key=lambda k: candidates[k]['total'])  # change to max() for 'maximum fees'
print(f"Answer: {best}:{round(scheme_totals[best]['total'], 2)}")

Type 2: ACI Optimization (Monthly)

fraud_txns = merch_txns[merch_txns['has_fraudulent_dispute'] == True].copy()
print(f"Fraudulent transactions: {len(fraud_txns)}, current ACIs: {fraud_txns['aci'].unique()}")

current_acis = set(fraud_txns['aci'].unique())
candidate_acis = [a for a in ['A','B','C','D','E','F','G'] if a not in current_acis]

n_fraud = len(fraud_txns)
aci_totals = {}
for target_aci in candidate_acis:
    total, matched = 0, 0
    for _, txn in fraud_txns.iterrows():
        fee = get_min_fee_for_combo(txn['card_scheme'], target_aci, txn['is_credit'],
                                    txn['intracountry'], txn['eur_amount'])
        if fee is not None:
            total += fee; matched += 1
    aci_totals[target_aci] = {'total': total, 'matched': matched}
    print(f"ACI {target_aci}: total={round(total,4)}, matched={matched}/{n_fraud}")

full = {k: v for k, v in aci_totals.items() if v['matched'] == n_fraud}
candidates = full if full else aci_totals
best_aci = min(candidates, key=lambda k: candidates[k]['total'])
print(f"Answer: {best_aci}:{round(aci_totals[best_aci]['total'], 2)}")

Yearly Questions (2023)

Monthly metrics (vol_cat, fraud_cat) MUST be recomputed per natural month even for yearly questions. Fee rules with monthly_fraud_level or monthly_volume fields depend on the specific month's data.

Yearly Card Scheme Routing

MONTHS = [(1,31),(32,59),(60,90),(91,120),(121,151),(152,181),
          (182,212),(213,243),(244,273),(274,304),(305,334),(335,365)]

all_schemes = ['GlobalCard', 'NexPay', 'SwiftCharge', 'TransactPlus']
scheme_totals_yearly = {s: 0 for s in all_schemes}

for d_start, d_end in MONTHS:
    m_txns = payments[
        (payments['merchant'] == merchant_name) &
        (payments['day_of_year'] >= d_start) &
        (payments['day_of_year'] <= d_end)
    ].copy()
    if len(m_txns) == 0:
        continue
    m_txns['intracountry'] = m_txns['issuing_country'] == m_txns['acquirer_country']
    mv = m_txns['eur_amount'].sum()
    fv = m_txns[m_txns['has_fraudulent_dispute']==True]['eur_amount'].sum()
    vc = get_vol_cat(mv)
    fc = get_fraud_cat((fv/mv)*100 if mv > 0 else 0)

    for scheme in all_schemes:
        for _, txn in m_txns.iterrows():
            applicable = [r for r in fees if rule_matches(r, scheme, txn['aci'],
                          txn['is_credit'], txn['intracountry'], vc=vc, fc=fc)]
            if applicable:
                scheme_totals_yearly[scheme] += min(
                    r['fixed_amount'] + r['rate'] * txn['eur_amount'] / 10000
                    for r in applicable)

best = min(scheme_totals_yearly, key=lambda k: scheme_totals_yearly[k])
print(f"Answer: {best}:{round(scheme_totals_yearly[best], 2)}")

Yearly ACI Optimization

For yearly ACI questions, each fraudulent transaction must use the vol_cat/fraud_cat from its own natural month.

MONTHS = [(1,31),(32,59),(60,90),(91,120),(121,151),(152,181),
          (182,212),(213,243),(244,273),(274,304),(305,334),(335,365)]

# All year transactions for this merchant
all_year_txns = payments[payments['merchant'] == merchant_name].copy()
all_year_txns['intracountry'] = all_year_txns['issuing_country'] == all_year_txns['acquirer_country']

# Pre-compute monthly vol/fraud categories
monthly_metrics = {}
for i, (d_start, d_end) in enumerate(MONTHS):
    m = all_year_txns[(all_year_txns['day_of_year'] >= d_start) &
                      (all_year_txns['day_of_year'] <= d_end)]
    if len(m) > 0:
        mv = m['eur_amount'].sum()
        fv = m[m['has_fraudulent_dispute']==True]['eur_amount'].sum()
        monthly_metrics[i] = {
            'vc': get_vol_cat(mv),
            'fc': get_fraud_cat((fv/mv)*100 if mv > 0 else 0)
        }

def get_month_idx(day):
    for i, (s, e) in enumerate(MONTHS):
        if s <= day <= e: return i
    return None

fraud_txns_yr = all_year_txns[all_year_txns['has_fraudulent_dispute'] == True].copy()
print(f"Fraudulent transactions (full year): {len(fraud_txns_yr)}, ACIs: {fraud_txns_yr['aci'].unique()}")

current_acis = set(fraud_txns_yr['aci'].unique())
candidate_acis = [a for a in ['A','B','C','D','E','F','G'] if a not in current_acis]

n_fraud = len(fraud_txns_yr)
aci_totals = {}
for target_aci in candidate_acis:
    total, matched = 0, 0
    for _, txn in fraud_txns_yr.iterrows():
        idx = get_month_idx(txn['day_of_year'])
        if idx is None or idx not in monthly_metrics: continue
        vc = monthly_metrics[idx]['vc']
        fc = monthly_metrics[idx]['fc']
        applicable = [r for r in fees if rule_matches(r, txn['card_scheme'], target_aci,
                      txn['is_credit'], txn['intracountry'], vc=vc, fc=fc)]
        if applicable:
            total += min(r['fixed_amount'] + r['rate'] * txn['eur_amount'] / 10000
                         for r in applicable)
            matched += 1
    aci_totals[target_aci] = {'total': total, 'matched': matched}
    print(f"ACI {target_aci}: total={round(total,4)}, matched={matched}/{n_fraud}")

full = {k: v for k, v in aci_totals.items() if v['matched'] == n_fraud}
candidates = full if full else aci_totals
best_aci = min(candidates, key=lambda k: candidates[k]['total'])
print(f"Answer: {best_aci}:{round(aci_totals[best_aci]['total'], 2)}")

Key Pitfalls

1. [] = match all: Empty list and null both mean "applies to all". This is the most common source of bugs — if you treat [] as "no match", you'll find zero matching rules for many transactions.

2. capture_delay normalization: merchant_data stores numeric strings like "7" or "2". Map to fee-rule categories: int < 3 → "<3", int 3-5 → "3-5", int > 5 → ">5". Named values ("immediate", "manual") stay unchanged.

3. MCC absent from fee rules: Some MCCs don't appear in any rule's specific MCC list. Only rules with empty [] MCC list apply — these are "catch-all" rules. This is expected and normal.

4. intracountry: acquirer_country is already a 2-letter country code. Compute intracountry = issuing_country == acquirer_country directly.

5. Multiple matching rules → minimum fee: When multiple rules match a transaction, the merchant pays the minimum applicable fee.

6. Scope of monthly metrics: Use ALL merchant transactions in the period (not just fraudulent) to compute monthly_vol and fraud_pct.

7. Partial coverage is NORMAL: Some schemes (e.g., NexPay, SwiftCharge) often have no matching rule for certain ACI/credit/intracountry combinations. This means matched < n for those schemes. Do NOT investigate or debug this — just use the preference logic: prefer full-coverage candidates; if none, use all candidates by total.