import json, csv from decimal import Decimal, getcontext getcontext().prec = 50 with open('fees.json') as f: fees = json.load(f) with open('merchant_data.json') as f: merchants = json.load(f) belles = [m for m in merchants if m['merchant']=='Belles_cookbook_store'][0] rows = [] with open('payments.csv', newline='') as f: reader = csv.DictReader(f) for r in reader: if r['merchant']=='Belles_cookbook_store' and r['year']=='2023' and 1 <= int(r['day_of_year']) <= 31: rows.append(r) # fraud level for Belles Jan 2023 total = sum(Decimal(r['eur_amount']) for r in rows) fraud_vol = sum(Decimal(r['eur_amount']) for r in rows if r['has_fraudulent_dispute']=='True') fraud_ratio = fraud_vol/total print("total:", total, "fraud_vol:", fraud_vol, "ratio:", fraud_ratio) print("monthly volume in EUR:", total) def vol_bucket(v): if v < 100000: return '<100k' if v < 1000000: return '100k-1m' if v < 5000000: return '1m-5m' return '>5m' print("vol bucket:", vol_bucket(total)) def fraud_match(rule_bucket, ratio): if rule_bucket is None: return True b = rule_bucket if b == '<7.2%': return ratio < Decimal('0.072') if b == '7.2%-7.7%': return Decimal('0.072') <= ratio < Decimal('0.077') if b == '7.7%-8.3%': return Decimal('0.077') <= ratio <= Decimal('0.083') if b == '>8.3%': return ratio > Decimal('0.083') return False def capture_match(rule_cd, merchant_cd): if rule_cd is None: return True try: md = int(merchant_cd) except: md = None if rule_cd=='immediate' and merchant_cd=='immediate': return True if rule_cd=='manual' and merchant_cd=='manual': return True if rule_cd=='<3' and md is not None and md<3: return True if rule_cd=='3-5' and md is not None and 3<=md<=5: return True if rule_cd=='>5' and md is not None and md>5: return True return False def rule_matches(fee, r, merch, ratio): if fee['card_scheme'] != r['card_scheme']: return False if fee['account_type'] and merch['account_type'] not in fee['account_type']: return False if not capture_match(fee['capture_delay'], merch['capture_delay']): return False if not fraud_match(fee['monthly_fraud_level'], ratio): return False if fee['monthly_volume'] and fee['monthly_volume'] != vol_bucket(total): return False if fee['merchant_category_code'] and merch['merchant_category_code'] not in fee['merchant_category_code']: return False if fee['is_credit'] is not None and fee['is_credit'] != (r['is_credit']=='True'): return False if fee['aci'] and r['aci'] not in fee['aci']: return False if fee['intracountry'] is not None: intra = (r['issuing_country'] == r['acquirer_country']) if intra != (fee['intracountry']==1.0): return False return True targets = [r for r in rows if r['card_scheme']=='NexPay' and r['is_credit']=='True' and r['aci'] in ('C','B')] print("num targets:", len(targets)) # check acquirer countries from collections import Counter print(Counter(r['acquirer_country'] for r in targets)) # For each target, find matching NexPay credit rules for r in targets: matches = [] for fee in fees: if rule_matches(fee, r, belles, fraud_ratio): matches.append((fee['ID'], fee['fixed_amount'], fee['rate'])) print(r['aci'], r['issuing_country'], r['eur_amount'], "->", matches)

tasks/dabstep/dabstep-fee-delta-fee-384-jan-2023