{ "storage": {}, "autoload": { "config": { "s3": { "AWS_ACCESS_KEY_ID": "::getENV", "AWS_SECRET_ACCESS_KEY": "::getENV", "AWS_BUCKET_NAME": "::getENV" }, "files": { "creditfairadmin": { "bureau": { "type": "s3", "misc": [ { "applicant_type": 1, "file": "{identifier}/{identifier}_response_experian_soft_1.json", "type": "experian_soft_pull" }, { "applicant_type": 2, "file": "{identifier}/{identifier}_response_experian_soft_2.json", "type": "experian_soft_pull" }, { "applicant_type": 1, "file": "{identifier}/{identifier}_response_experian.json", "type": "experian_hard_pull" }, { "applicant_type": 2, "file": "{identifier}/{identifier}_response_2_experian.json", "type": "experian_hard_pull" }, { "file": "{identifier}/{identifier}_response.json", "type": "equifax_hard_pull", "applicant_type": 1 }, { "file": "{identifier}/{identifier}_response_2.json", "type": "equifax_hard_pull", "applicant_type": 2 } ] }, "finbit": { "type": "s3", "misc": [{ "file": "{identifier}/{identifier}_finbit.json", "type": "finbit" }] }, "pirimid": { "type": "s3", "misc": [ { "file": "consumer/{identifier}/pirimid_analysis_json{pirimid_tracking_id}.json", "type": "pirimid_analysis_json" } ] }, "account_aggregator": { "type": "s3", "misc": [ { "file": "{identifier}/{account_aggregator_id}_account_aggregator.json", "type": "account_aggregator_json" } ] } }, "pre_eligibility": { "bureau": { "type": "s3", "misc": [ { "applicant_type": 1, "file": "pre_eligibility/{identifier}/{identifier}_response_experian_soft_1.json", "type": "experian_soft_pull" }, { "applicant_type": 2, "file": "pre_eligibility/{identifier}/{identifier}_response_experian_soft_2.json", "type": "experian_soft_pull" }, { "applicant_type": 1, "file": "pre_eligibility/{identifier}/{identifier}_response_experian.json", "type": "experian_hard_pull" }, { "applicant_type": 2, "file": "pre_eligibility/{identifier}/{identifier}_response_2_experian.json", "type": "experian_hard_pull" }, { "file": "pre_eligibility/{identifier}/{identifier}_response.json", "type": "equifax_hard_pull", "applicant_type": 1 }, { "file": "pre_eligibility/{identifier}/{identifier}_response_2.json", "type": "equifax_hard_pull", "applicant_type": 2 } ] } } }, "dataConfig" : { "term_loans" : ["Auto Loan", "Housing Loan", "Personal Loan", "Consumer Loan", "Education Loan", "Loan to Professional", "Two-wheeler Loan", "Commercial Vehicle Loan", "Used Car Loan", "Business Loan-Priority Sector-Small Business", "Business Loan - Priority Sector- Agriculture", "Business Loan - Priority Sector- Others", "Business Non-Funded Credit Facility", "Business Non-Funded Credit Facility - Priority", "Staff Loan"], "experian_ownership": { "Individual": "1", "Joint": "2", "Joint Account": "2", "Authorized User": "3", "Authorized user": "3", "Guarantor": "7", "Deceased": "20" }, "equifax_dpd": { "NEW": 0, "90+": 90, "120+": 120, "180+": 180 }, "account_status_mappings": { "experian": {"date_fields": ["Date_Closed", "Date_of_Last_Payment", "Date_Reported"], "value_field": "Account_Status"}, "equifax": {"date_fields": ["sch:DateClosed", "sch:LastPaymentDate", "sch:DateReported"], "value_field": "sch:AccountStatus"} }, "account_status": {"00": "No Suit Filed", "89": "Wilful default", "93": "Suit Filed(Wilful default)", "97": "Suit Filed(Wilful Default) and Written-off", "30": "Restructured", "31": "Restructured Loan (Govt. Mandated)", "32": "Settled", "33": "Post (WO) Settled", "34": "Account Sold", "35": "Written Off and Account Sold", "36": "Account Purchased", "37": "Account Purchased and Written Off", "38": "Account Purchased and Settled", "39": "Account Purchased and Restructured", "40": "Status Cleared", "41": "Restructured Loan", "42": "Restructured Loan (Govt. Mandated)", "43": "Written-off", "44": "Settled", "45": "Post (WO) Settled", "46": "Account Sold", "47": "Written Off and Account Sold", "48": "Account Purchased", "49": "Account Purchased and Written Off", "50": "Account Purchased and Settled", "51": "Account Purchased and Restructured", "52": "Status Cleared", "53": "Suit Filed", "54": "Suit Filed and Written-off", "55": "Suit Filed and Settled", "56": "Suit Filed and Post (WO) Settled", "57": "Suit Filed and Account Sold", "58": "Suit Filed and Written Off and Account Sold", "59": "Suit Filed and Account Purchased", "60": "Suit Filed and Account Purchased and Written Off", "61": "Suit Filed and Account Purchased and Settled", "62": "Suit Filed and Account Purchased and Restructured", "63": "Suit Filed and Status Cleared", "64": "Wilful Default and Restructured Loan", "65": "Wilful Default and Restructured Loan (Govt. Mandated)", "66": "Wilful Default and Settled", "67": "Wilful Default and Post (WO) Settled", "68": "Wilful Default and Account Sold", "69": "Wilful Default and Written Off and Account Sold", "70": "Wilful Default and Account Purchased", "72": "Wilful Default and Account Purchased and Written Off", "73": "Wilful Default and Account Purchased and Settled", "74": "Wilful Default and Account Purchased and Restructured", "75": "Wilful Default and Status Cleared", "76": "Suit filed (Wilful default) and Restructured", "77": "Suit filed (Wilful default) and Restructured Loan (Govt. Mandated)", "79": "Suit filed (Wilful default) and Settled", "81": "Suit filed (Wilful default) and Post (WO) Settled", "85": "Suit filed (Wilful default) and Account Sold", "86": "Suit filed (Wilful default) and Written Off and Account Sold", "87": "Suit filed (Wilful default) and Account Purchased", "88": "Suit filed (Wilful default) and Account Purchased and Written Off", "94": "Suit filed (Wilful default) and Account Purchased and Settled", "90": "Suit filed (Wilful default) and Account Purchased and Restructured", "91": "Suit filed (Wilful default) and Status Cleared", "13": "CLOSED", "14": "CLOSED", "15": "CLOSED", "16": "CLOSED", "17": "CLOSED", "12": "CLOSED", "11": "ACTIVE", "71": "ACTIVE", "78": "ACTIVE", "80": "ACTIVE", "82": "ACTIVE", "83": "ACTIVE", "84": "ACTIVE", "21": "ACTIVE", "22": "ACTIVE", "23": "ACTIVE", "24": "ACTIVE", "25": "ACTIVE", "131": "Restructured due to natural calamity", "130": "Restructured due to COVID-19"}, "account_type": { "1": "Auto Loan", "01": "Auto Loan", "2": "Housing Loan", "02": "Housing Loan", "3": "Property Loan", "03": "Property Loan", "4": "LOAN AGAINST SHARES/SECURITIES", "04": "LOAN AGAINST SHARES/SECURITIES", "5": "Personal Loan", "05": "Personal Loan", "6": "Consumer Loan", "06": "Consumer Loan", "7": "Gold Loan", "07": "Gold Loan", "8": "EDUCATIONAL LOAN", "08": "EDUCATIONAL LOAN", "9": "LOAN TO PROFESSIONAL", "09": "LOAN TO PROFESSIONAL", "10": "Credit Card", "11": "LEASING", "12": "OVERDRAFT", "13": "TWO-WHEELER LOAN", "14": "NON-FUNDED CREDIT FACILITY", "15": "LOAN AGAINST BANK DEPOSITS", "16": "FLEET CARD", "17": "Commercial Vehicle Loan", "18": "Telco – Wireless", "19": "Telco – Broadband", "20": "Telco – Landline", "23": "GECL Secured", "24": "GECL Unsecured", "31": "Secured Credit Card", "32": "Used Car Loan", "33": "Construction Equipment Loan", "34": "Tractor Loan", "35": "Corporate Credit Card", "36": "Kisan Credit Card", "37": "Loan on Credit Card", "38": "Prime Minister Jaan Dhan Yojana - Overdraft", "39": "Mudra Loans – Shishu / Kishor / Tarun", "40": "Microfinance – Business Loan", "41": "Microfinance – Personal Loan", "42": "Microfinance – Housing Loan", "43": "Microfinance – Others", "44": "Pradhan Mantri Awas Yojana - Credit Link Subsidy Scheme MAY CLSS", "45": "P2P Personal Loan", "46": "P2P Auto Loan", "47": "P2P Education Loan", "51": "BUSINESS LOAN – GENERAL", "52": "BUSINESS LOAN –PRIORITY SECTOR – SMALL BUSINESS", "53": "BUSINESS LOAN –PRIORITY SECTOR – AGRICULTURE", "54": "BUSINESS LOAN –PRIORITY SECTOR – OTHERS", "55": "BUSINESS NON-FUNDED CREDIT FACILITY – GENERAL", "56": "BUSINESS NON-FUNDED CREDIT FACILITY – PRIORITY SECTOR – SMALL BUSINESS", "57": "BUSINESS NON-FUNDED CREDIT FACILITY – PRIORITY SECTOR – AGRICULTURE", "58": "BUSINESS NON-FUNDED CREDIT FACILITY – PRIORITY SECTOR – OTHERS", "59": "BUSINESS LOANS AGAINST BANK DEPOSITS", "60": "Staff Loan", "61": "Business Loan - Unsecured", "00": "Others", "50": "Business Loan - Secured", "69": "Short Term Personal Loan [Unsecured]", "70": "Priority Sector Gold Loan [Secured]", "71": "Temporary Overdraft [Unsecured]" }, "surrogate_account_mappings" : { "Home Loan": {"experian": ["2", "02", "42"], "equifax": ["Housing Loan", "Home Loan", "Microfinance – Housing Loan", "MicroFinance Housing Loan"]}, "Business Loan": {"experian":["51", "61", "54", "40"], "equifax": ["Business Loan – General", "Business Loan", "Business Loan - Unsecured", "Business Loan –priority Sector – Small Business", "Business Loan - Priority Sector- Small Business", "Business Loan –priority Sector – Others", "Business Loan - Priority Sector- Others", "Microfinance – Business Loan", "Business Loan - Secured", "MicroFinance Business Loan"]}, "Auto Loan": {"experian":["1", "01", "32", "46"], "equifax": ["Auto Loan", "Used Car Loan", "P2p Auto Loan"]} } } } }, "functions": { "lang": [ { "python": [ { "dateDiffInYear": "from datetime import datetime\n\ndef dateDiffInYear(date1, date2):\n if date1 is not None and date2 is not None:\n date1 = datetime.strptime(date1, '%Y-%m-%d')\n date2 = datetime.strptime(date2, '%Y-%m-%d')\n delta = date2 - date1\n return int(delta.days / 365)\n return None", "date_diff_in_month()": "from datetime import datetime\n\ndef date_diff_in_month(date1, date2):\n date1 = datetime.strptime(date1, '%Y-%m-%d')\n date2 = datetime.strptime(date2, '%Y-%m-%d')\n delta = date2.year * 12 + date2.month - (date1.year * 12 + date1.month)\n return int(delta)\n", "getCurrentDate()": "from datetime import date\n\ndef getCurrentDate():\n return date.today().strftime('%Y-%m-%d')\n", "check_surrogate_rule": "from datetime import datetime\nimport traceback\nfrom collections import defaultdict\ndef check_surrogate_rule(approved_amount, created_at, bureau_surrogate_rules, accounts, ownership_dict, account_mappings, bureau_type):\n try:\n success = False\n reverse_account_mappings = {val: key for key, val in account_mappings.items()}\n bureau_surrogate_rules = [item for item in bureau_surrogate_rules.get(\"values\") if item.get(\"status\") == 1]\n available_sanction_amt_list = []\n account_type_grouped_accts = defaultdict(dict)\n current_date = datetime.now()\n personal_loan_count = 0\n for acct in accounts:\n if str(acct.get(\"Account_Type\")) in (\"5\", \"05\") or str(acct.get(\"sch:AccountType\")) in (\"5\", \"05\"):\n open_date_str = acct.get(\"Open_Date\") if bureau_type == 'experian' else acct.get(\"sch:DateOpened\")\n date_fmt = '%Y%m%d' if bureau_type == 'experian' else '%Y-%m-%d'\n if open_date_str:\n try:\n open_date = datetime.strptime(str(open_date_str), date_fmt)\n if calculate_bureau_months(current_date, open_date) <= 12:\n personal_loan_count += 1\n except Exception:\n pass\n print(personal_loan_count)\n available_account_types = {account for item in bureau_surrogate_rules for account in item['account_type']}\n if bureau_type == 'experian':\n created_at = '' if not created_at else datetime.strptime(created_at, '%Y%m%d')\n available_account_types = {key for key, value in account_mappings.items() if value in available_account_types}\n else:\n created_at = '' if not created_at else datetime.strptime(created_at, '%d-%m-%Y')\n for distinct_account in available_account_types:\n account_type_grouped_accts[distinct_account]['accounts'] = [acct for acct in accounts if (str(acct.get(\"Account_Type\")) == distinct_account or str(acct.get(\"sch:AccountType\")) == distinct_account)]\n for rules in bureau_surrogate_rules:\n rule_ownership_list = rules.get(\"ownership_type\")\n rule_open_vintage = rules.get(\"open_vintage\")\n rule_close_vintage = rules.get(\"close_vintage\")\n rule_percentage = rules.get(\"limit\")\n rule_account_type = rules.get(\"account_type\")\n rule_account_type_count = rules.get(\"account_type_count\")\n check_accounts = []\n for selections in rule_account_type:\n check_accounts.append(int(reverse_account_mappings.get(selections)))\n relevant_ownerships = [ownership_dict.get(i.capitalize()) for i in rule_ownership_list]\n for type, accts in account_type_grouped_accts.items():\n available_sanction_list = []\n available_sanction_amount = 0\n for acct in accts.get(\"accounts\", []):\n if str(acct.get(\"AccountHoldertypeCode\")) in relevant_ownerships or str(acct.get(\"sch:OwnershipType\")) in rule_ownership_list:\n if bureau_type == 'experian':\n close_date_key = \"Date_Closed\"\n total_payment_grid = len(acct.get(\"CAIS_Account_History\")) if acct.get(\"CAIS_Account_History\") else 0\n sanction_amount_key = \"Highest_Credit_or_Original_Loan_Amount\"\n close_date = '' if not acct.get(close_date_key) else datetime.strptime(acct.get(close_date_key), '%Y%m%d')\n else:\n close_date_key = \"sch:DateClosed\"\n total_payment_grid = len(acct.get(\"sch:History48Months\").get(\"sch:Month\")) if acct.get(\"sch:History48Months\").get(\"sch:Month\") else 0\n sanction_amount_key = \"sch:SanctionAmount\" if acct.get(\"sch:SanctionAmount\") else \"sch:HighCredit\"\n close_date = '' if not acct.get(close_date_key) else datetime.strptime(acct.get(close_date_key),'%Y-%m-%d')\n acct_close_vintage = calculate_bureau_months(created_at, close_date)\n passes_vintage = rule_account_type_count or (total_payment_grid >= rule_open_vintage and acct_close_vintage <= rule_close_vintage)\n if passes_vintage:\n if acct.get(sanction_amount_key):\n available_sanction_list.append(float(acct.get(sanction_amount_key)))\n if available_sanction_list and int(type) in check_accounts:\n if rule_account_type_count:\n matched_percentage = None\n for count_rule in rule_account_type_count:\n if personal_loan_count == count_rule.get(\"personal_count\"):\n matched_percentage = count_rule.get(\"percentage_limit\")\n break\n if matched_percentage is not None:\n available_sanction_amount = max(available_sanction_list) * matched_percentage / 100\n else:\n continue\n else:\n available_sanction_amount = max(available_sanction_list) * rule_percentage / 100\n available_sanction_amt_list.append({\",\".join(rules.get(\"account_type\")): available_sanction_amount})\n if available_sanction_amount >= 50000:\n success = True\n return [1 if success else 0], available_sanction_amt_list\n except Exception as ex:\n print(traceback.print_exc())", "calculate_bureau_months": "def calculate_bureau_months(date1, date2):\n if not date1 or not date2:\n return 0\n return (date1 - date2).days // 30", "check_close_date": "def check_close_date(bureau_close):\n return 1 if bureau_close <= 60 else 0", "check_amount": "def check_amount(sanctioned_percentage, requested_loan_amount):\n return 1 if sanctioned_percentage >= requested_loan_amount else 0", "dpd_reject_rule": "from datetime import date\nimport operator\nfrom dateutil.relativedelta import relativedelta\nfrom collections import defaultdict\nimport traceback\ndef dpd_reject_rule(rule_data, bureau_data, bureau_type, OwnershipDict, account_type_dict, approval=True):\n try:\n ops = {\n '>': operator.gt,\n '<': operator.lt,\n '>=': operator.ge,\n '<=': operator.le,\n '==': operator.eq,\n '!=': operator.ne\n }\n historical_key_map = {'experian': {'dpd': 'Days_Past_Due', 'keyword': 'Asset_Classification'},\n 'equifax': {'dpd': 'sch:PaymentStatus', 'keyword': 'sch:AssetClassificationStatus'}}\n numeric_map = {'experian': {'dpd': True, 'keyword': False},\n 'equifax': {'dpd': False, 'keyword': False}}\n dpd_rules = rule_data.get(\"rules\")\n ownership = rule_data.get(\"Ownership\") if rule_data.get(\"Ownership\") else rule_data.get(\"ownership\")\n dpd_ownership_list = [OwnershipDict[i.capitalize()] for i in ownership]\n dpd_excluded_account_types = rule_data.get(\"account_types\")\n dpd_order = [i.get(\"value\") for i in dpd_rules]\n grouped_by_month = defaultdict(list)\n rule_sources = [\n (dpd_rules, 'dpd')\n ]\n for current_rules_list, source_name in rule_sources:\n for item in current_rules_list:\n month_key = item.get(\"months\")\n if month_key is not None:\n remaining_data = {k: v for k, v in item.items() if k != 'months'}\n remaining_data['name'] = source_name\n grouped_by_month[month_key].append(remaining_data)\n\n triggered_rules = []\n rule_definitions = {}\n all_lookbacks = set()\n today = date.today()\n lookback_start_dates = {}\n\n for months_str, rules_list in grouped_by_month.items():\n try:\n months_int = int(months_str) if months_str != '*' else '*'\n all_lookbacks.add(months_int)\n if months_int not in lookback_start_dates:\n lookback_start_dates[months_int] = today.replace(day=1) - relativedelta(months=months_int - 1) if months_int != '*' else 0\n for rule in rules_list:\n rule_id = (months_int, rule.get('name'), rule.get('operator'), rule.get('value'))\n if None not in rule_id and rule_id not in rule_definitions:\n try:\n req_tradelines = int(rule.get('tradelines', 1))\n rule_definitions[rule_id] = (rule, req_tradelines)\n except ValueError:\n rule_definitions[rule_id] = (rule, 1)\n except ValueError:\n continue\n sorted_lookbacks = sorted([elem for elem in all_lookbacks if elem != '*'])\n if '*' in all_lookbacks:\n sorted_lookbacks.append('*')\n rule_tradeline_match_counts = defaultdict(int)\n record_details = {}\n if bureau_data:\n for account in bureau_data:\n rule_to_check = []\n if (str(account.get(\"AccountHoldertypeCode\")) in dpd_ownership_list or\n OwnershipDict.get(account.get(\"sch:OwnershipType\")) in dpd_ownership_list):\n if dpd_excluded_account_types:\n if (account_type_dict.get(str(account.get(\"Account_Type\"))) not in dpd_excluded_account_types and account.get(\"sch:AccountType\") not in dpd_excluded_account_types):\n rule_to_check.append('dpd')\n else:\n rule_to_check.append('dpd')\n tradeline_name = account.get(\n \"Account_Number\") if bureau_type == \"experian\" else f\"{account.get('@seq')}_{account.get('sch:AccountNumber')}\"\n if bureau_type == \"experian\":\n tradeline_records = [account.get(\"CAIS_Account_History\")] if isinstance(\n account.get(\"CAIS_Account_History\"), dict) else account.get(\"CAIS_Account_History\")\n else:\n tradeline_records = [account.get(\"sch:History48Months\").get(\"sch:Month\")] \\\n if isinstance(account.get(\"sch:History48Months\").get(\"sch:Month\"), dict) else account.get(\n \"sch:History48Months\").get(\"sch:Month\")\n satisfied_rules_per_tradeline = set()\n if rule_to_check and check_if_open_account(account, bureau_type, approval) == 1:\n if tradeline_records:\n for record in tradeline_records:\n try:\n if bureau_type == \"equifax\":\n time_key = record.get(\"@key\")\n record_year = f\"20{time_key.split('-')[-1]}\"\n record_month = time_key.split('-')[0]\n else:\n record_year = record.get(\"Year\")\n record_month = record.get(\"Month\")\n if not record_year or not record_month: continue\n record_date = date(int(record_year), int(record_month), 1)\n if record_date > today: continue\n except (ValueError, TypeError, KeyError):\n continue\n for months_lookback in sorted_lookbacks:\n start_date = lookback_start_dates[months_lookback] if months_lookback != '*' else '0000-00-00'\n if start_date == '0000-00-00' or record_date >= start_date:\n months_str = str(months_lookback)\n if months_str not in grouped_by_month: continue\n filtered_grouped_by_month = [item for item in grouped_by_month[months_str] if\n item.get(\"name\") in rule_to_check]\n for rule in filtered_grouped_by_month:\n rule_id = (months_lookback, rule.get('name'), rule.get('operator'),\n rule.get('value'))\n if rule_id in satisfied_rules_per_tradeline:\n continue\n rule_name = rule.get('name')\n rule_value_str = rule.get('value')\n rule_operator = '>' if rule_name == 'dpd' else '=='\n historical_key = historical_key_map.get(bureau_type).get(rule_name)\n value_is_numeric = numeric_map.get(bureau_type).get(rule_name, False)\n\n if not all([rule_name, rule_value_str, rule_operator,\n historical_key is not None]): continue\n try:\n compare_value_rule = int(rule_value_str) if value_is_numeric else rule_value_str\n except ValueError:\n continue\n record_value_raw = record.get(historical_key)\n compare_value_record = None\n try:\n if record_value_raw is not None:\n compare_value_record = int(record_value_raw) if value_is_numeric else str(\n record_value_raw)\n except (ValueError, TypeError):\n if value_is_numeric: continue\n if bureau_type == 'experian':\n condition_result = check_condition(compare_value_record, rule_operator,\n compare_value_rule, rule_name,ops, dpd_order)\n else:\n condition_result = check_condition_equifax(compare_value_record, rule_operator,\n compare_value_rule, rule_name,ops,\n dpd_order)\n if condition_result:\n satisfied_rules_per_tradeline.add(rule_id)\n rule_tradeline_match_counts[rule_id] += 1\n if rule_id not in record_details:\n record_details[rule_id] = []\n record_details[rule_id].append(f\"{tradeline_name} - {record}\")\n sorted_rule_ids = sorted(rule_tradeline_match_counts.keys(), key=lambda x: (float('inf') if x[0] == '*' else x[0], x[1:]))\n for rule_id in sorted_rule_ids:\n count = rule_tradeline_match_counts[rule_id]\n if rule_id in rule_definitions:\n original_rule, required_tradelines = rule_definitions[rule_id]\n original_rule['months'] = rule_id[0]\n print(f\" Rule {rule_id}: Matched on {count} tradeline(s). Required: {required_tradelines}\")\n op_func = ops.get('==' if original_rule.get(\"operator\") == '=' else original_rule.get(\"operator\"))\n if (not approval and op_func(count, required_tradelines)) or (approval and not op_func(count, required_tradelines)):\n print(f\" -> TRIGGERED\")\n triggered_rules.append({\n \"rule\": original_rule,\n \"records\": record_details[rule_id]\n })\n if triggered_rules:\n return [1], triggered_rules\n return 0\n except Exception as ex:\n traceback.print_exc()", "check_if_open_account": "def check_if_open_account(account, type, is_approval):\n try:\n open_account = 1\n if is_approval:\n return open_account\n if type == 'experian' and account.get(\"Date_Closed\"):\n open_account = 0\n elif type == 'equifax' and not account.get(\"sch:DateOpened\"):\n open_account = 0\n return open_account\n except Exception as ex:\n return 0", "check_condition": "def check_condition(val1, op_str, val2, name, ops, order=()):\n if name == 'dpd' and order and val1 is not None:\n for elem in order:\n if order[-1] != elem and int(elem) == val2:\n compare_start = int(val2)\n compare_end = int(val2) + (int(order[order.index(elem) + 1]) - int(val2))\n if compare_start < val1 <= compare_end:\n return True\n elif order[-1] == elem and int(elem) == val2:\n if int(val2) < int(val1):\n return True\n else:\n op_func = ops.get(op_str)\n if op_func and val1 is not None and val2 is not None:\n return op_func(val1, val2)\n return False", "check_condition_equifax": "def check_condition_equifax(val1, op_str, val2, name, ops, order=()):\n EquifaxDPD = {\n 'NEW': 0,\n '90+': 90,\n '120+': 120,\n '180+': 180\n }\n if name == 'dpd' and val1:\n if f\"{val2}+\" == val1 and val2 != order[-1]:\n return True\n elif val2 == order[-1]:\n if int(EquifaxDPD.get(val1, 0)) >= int(val2):\n return True\n else:\n op_func = ops.get(op_str)\n if op_func and val1 is not None and val2 is not None:\n return op_func(val1, val2)\n return False", "keyword_reject_rule": "from datetime import date\nfrom dateutil.relativedelta import relativedelta\nimport operator\nfrom collections import defaultdict\nimport traceback\ndef keyword_reject_rule(rule_data, bureau_data, bureau_type, OwnershipDict, account_type_dict, approval=True):\n try:\n ops = {\n '>': operator.gt,\n '<': operator.lt,\n '>=': operator.ge,\n '<=': operator.le,\n '==': operator.eq,\n '!=': operator.ne\n }\n historical_key_map = {'experian': {'dpd': 'Days_Past_Due', 'keyword': 'Asset_Classification'},\n 'equifax': {'dpd': 'sch:PaymentStatus', 'keyword': 'sch:AssetClassificationStatus'}}\n keyword_rules = rule_data.get(\"rules\")\n ownership = rule_data.get(\"Ownership\") if rule_data.get(\"Ownership\") else rule_data.get(\"ownership\")\n keyword_ownership_list = [OwnershipDict[i.capitalize()] for i in ownership]\n keyword_excluded_account_types = rule_data.get(\"account_types\")\n grouped_by_month = defaultdict(list)\n rule_sources = [(keyword_rules, 'keyword')]\n for current_rules_list, source_name in rule_sources:\n for item in current_rules_list:\n month_key = item.get(\"months\")\n if month_key is not None:\n remaining_data = {k: v for k, v in item.items() if k != 'months'}\n remaining_data['name'] = source_name\n grouped_by_month[month_key].append(remaining_data)\n triggered_rules = []\n rule_definitions = {}\n all_lookbacks = set()\n today = date.today()\n lookback_start_dates = {}\n for months_str, rules_list in grouped_by_month.items():\n try:\n months_int = int(months_str) if months_str != '*' else '*'\n all_lookbacks.add(months_int)\n if months_int not in lookback_start_dates:\n lookback_start_dates[months_int] = today.replace(day=1) - relativedelta(months=months_int - 1) if months_int != '*' else 0\n for rule in rules_list:\n rule_id = (months_int, rule.get('name'), rule.get('operator'), rule.get('value'))\n if None not in rule_id and rule_id not in rule_definitions:\n try:\n req_tradelines = int(rule.get('tradelines', 1))\n rule_definitions[rule_id] = (rule, req_tradelines if req_tradelines > 0 else 1)\n except ValueError:\n rule_definitions[rule_id] = (rule, 1)\n except ValueError:\n continue\n sorted_lookbacks = sorted([elem for elem in all_lookbacks if elem != '*'])\n if '*' in all_lookbacks:\n sorted_lookbacks.append('*')\n rule_tradeline_match_counts = defaultdict(int)\n record_details = {}\n if bureau_data:\n for account in bureau_data:\n rule_to_check = []\n if (str(account.get(\"AccountHoldertypeCode\")) in keyword_ownership_list or\n OwnershipDict.get(account.get(\"sch:OwnershipType\")) in keyword_ownership_list):\n if keyword_excluded_account_types:\n if (account_type_dict.get(\n str(account.get(\"Account_Type\"))) not in keyword_excluded_account_types and account.get(\n \"sch:AccountType\") not in keyword_excluded_account_types):\n rule_to_check.append('keyword')\n else:\n rule_to_check.append('keyword')\n tradeline_name = account.get(\n \"Account_Number\") if bureau_type == \"experian\" else f\"{account.get('@seq')}_{account.get('sch:AccountNumber')}\"\n if bureau_type == \"experian\":\n tradeline_records = [account.get(\"CAIS_Account_History\")] if isinstance(account.get(\"CAIS_Account_History\"), dict) else account.get(\"CAIS_Account_History\")\n else:\n tradeline_records = [account.get(\"sch:History48Months\").get(\"sch:Month\")] if isinstance(account.get(\"sch:History48Months\").get(\"sch:Month\"), dict) else account.get(\"sch:History48Months\").get(\"sch:Month\")\n satisfied_rules_per_tradeline = set()\n if rule_to_check and check_if_open_account(account, bureau_type, approval) == 1:\n if tradeline_records:\n for record in tradeline_records:\n try:\n if bureau_type == \"equifax\":\n time_key = record.get(\"@key\")\n record_year = f\"20{time_key.split('-')[-1]}\"\n record_month = time_key.split('-')[0]\n else:\n record_year = record.get(\"Year\")\n record_month = record.get(\"Month\")\n if not record_year or not record_month: continue\n record_date = date(int(record_year), int(record_month), 1)\n if record_date > today: continue\n except (ValueError, TypeError, KeyError):\n continue\n for months_lookback in sorted_lookbacks:\n start_date = lookback_start_dates[months_lookback] if months_lookback != '*' else '0000-00-00'\n if start_date == '0000-00-00' or record_date >= start_date:\n months_str = str(months_lookback)\n if months_str not in grouped_by_month: continue\n filtered_grouped_by_month = [item for item in grouped_by_month[months_str] if\n item.get(\"name\") in rule_to_check]\n for rule in filtered_grouped_by_month:\n rule_id = (months_lookback, rule.get('name'), rule.get('operator'),\n rule.get('value'))\n if rule_id in satisfied_rules_per_tradeline:\n continue\n rule_name = rule.get('name')\n rule_value_str = rule.get('value')\n rule_operator = '=='\n historical_key = historical_key_map.get(bureau_type).get(rule_name)\n if not all([rule_name, rule_value_str, rule_operator,\n historical_key is not None]): continue\n try:\n compare_value_rule = rule_value_str\n except ValueError:\n continue\n record_value_raw = record.get(historical_key)\n compare_value_record = None\n if record_value_raw is not None:\n compare_value_record = str(record_value_raw)\n if bureau_type == 'experian':\n condition_result = check_condition(compare_value_record, rule_operator,\n compare_value_rule, rule_name, ops)\n else:\n condition_result = check_condition_equifax(compare_value_record, rule_operator,\n compare_value_rule, rule_name, ops)\n if condition_result:\n satisfied_rules_per_tradeline.add(rule_id)\n rule_tradeline_match_counts[rule_id] += 1\n if rule_id not in record_details:\n record_details[rule_id] = []\n record_details[rule_id].append(f\"{tradeline_name} - {record}\")\n sorted_rule_ids = sorted(rule_tradeline_match_counts.keys())\n for rule_id in sorted_rule_ids:\n count = rule_tradeline_match_counts[rule_id]\n if rule_id in rule_definitions:\n original_rule, required_tradelines = rule_definitions[rule_id]\n original_rule['months'] = rule_id[0]\n print(f\" Rule {rule_id}: Matched on {count} tradeline(s). Required: {required_tradelines}\")\n op_func = ops.get('==' if original_rule.get(\"operator\") == '=' else original_rule.get(\"operator\"))\n if op_func(count, required_tradelines):\n print(f\"-> TRIGGERED\")\n triggered_rules.append({\n \"rule\": original_rule,\n \"records\": record_details[rule_id]\n })\n if triggered_rules:\n return [1], triggered_rules\n return 0\n except Exception as ex:\n error_message = f'Exception occurred: {str(ex)}\\n{traceback.format_exc()}'\n print(error_message)", "check_account_status": "def check_account_status(rule_data, bureau_data, OwnershipDict, bureau_type, field_mappings, acct_dict):\n try:\n rules = rule_data.get(\"rules\")\n ownership_list = [OwnershipDict[i.capitalize()] for i in rule_data.get(\"ownership\")]\n violations = []\n data_key = field_mappings.get(bureau_type).get(\"value_field\")\n if bureau_data:\n for account in bureau_data:\n if (str(account.get(\"AccountHoldertypeCode\")) in ownership_list or\n OwnershipDict.get(account.get(\"sch:OwnershipType\")) in ownership_list):\n tradeline_name = account.get(\n \"Account_Number\") if bureau_type == \"experian\" else f\"{account.get('@seq')}_{account.get('sch:AccountNumber')}\"\n\n for specific_rule in rules:\n tradeline_value_raw = acct_dict.get(\n (account.get(data_key))) if bureau_type == 'experian' else account.get(data_key)\n if tradeline_value_raw is None:\n continue\n\n rule_value_str = specific_rule.get(\"value\")\n if rule_value_str is None:\n rule_value_str = specific_rule.get(\"values\")\n rule_months = specific_rule.get(\"months\")\n\n violation_found = False\n relevant_date = None\n rule_value_str = rule_value_str.replace('amp;', '')\n if tradeline_value_raw.replace(\" \", \"\").replace(\"&\", \"and\").replace(\"-\",\n \"\").lower() == rule_value_str.replace(\n \" \", \"\").replace(\"&\", \"and\").replace(\"-\", \"\").lower():\n if rule_months != '*':\n for date_field in field_mappings.get(bureau_type).get(\"date_fields\"):\n relevant_date_str = account.get(date_field)\n if relevant_date_str:\n fmt = \"%Y%m%d\" if bureau_type == 'experian' else \"%Y-%m-%d\"\n relevant_date = datetime.strptime(relevant_date_str, fmt)\n if relevant_date >= datetime.today() - relativedelta(months=int(rule_months)):\n violation_found = True\n break\n else:\n violation_found = True\n if violation_found:\n violations.append({\n \"tradeline_name\": tradeline_name,\n \"rule\": specific_rule,\n \"relevance_date\": relevant_date if relevant_date else '*'\n })\n if violations:\n return [1], violations\n return 0\n except Exception as ex:\n error_message = f\"Exception occurred: {str(ex)}\\n{traceback.format_exc()}\"\n print(error_message)", "check_principal_write_off": "def check_principal_write_off(rule_data, bureau_data, bureau_type, OwnershipDict):\n field_mappings = {'experian': {'date_fields': ['Date_Closed', 'Date_of_Last_Payment', 'Date_Reported'],\n 'value_field': 'Written_Off_Amt_Principal'},\n 'equifax': {'date_fields': ['sch:DateClosed', 'sch:LastPaymentDate', 'sch:DateReported'],\n 'value_field': 'sch:WriteOffAmount'}}\n rules = rule_data.get(\"rules\")\n failed_rules = []\n ownership_list = [OwnershipDict[i.capitalize()] for i in rule_data.get(\"ownership\")]\n data_key = field_mappings.get(bureau_type).get(\"value_field\")\n distinct_month = find_distinct_month('months', rules)\n month_write_off = dict.fromkeys(distinct_month, 0)\n if bureau_data:\n for account in bureau_data:\n if (str(account.get(\"AccountHoldertypeCode\")) in ownership_list or OwnershipDict.get(account.get(\"sch:OwnershipType\")) in ownership_list):\n write_off_amt = int(account.get(data_key)) if account.get(data_key) else 0\n for rule_month in distinct_month:\n if rule_month != '*':\n for date_field in field_mappings.get(bureau_type).get(\"date_fields\"):\n relevant_date_str = account.get(date_field)\n if relevant_date_str:\n fmt = \"%Y%m%d\" if bureau_type == 'experian' else \"%Y-%m-%d\"\n relevant_date = datetime.strptime(relevant_date_str, fmt)\n if relevant_date >= datetime.today() - relativedelta(months=int(rule_month)):\n month_write_off[rule_month] += write_off_amt\n break\n for rule in rules:\n if int(rule.get(\"value\")) <= month_write_off[rule.get(\"months\")]:\n failed_rules.append({\n 'rule': rule,\n 'total_principal_write_off': month_write_off[rule.get(\"months\")],\n 'rule_principal_write_off': rule.get(\"value\")\n })\n if failed_rules:\n return [1], failed_rules\n return 0", "find_distinct_month": "def find_distinct_month(key, rules):\n distinct_values = []\n for item_dict in rules:\n if key in item_dict:\n value = item_dict[key]\n if value not in distinct_values:\n distinct_values.append(value)\n return distinct_values", "check_total_past_dues": "def check_total_past_dues(rule_data, bureau_data, bureau_type, OwnershipDict, AccountTypeDict, approval=True):\n try:\n rules = rule_data.get(\"rules\")\n ownership_list = [OwnershipDict[i.capitalize()] for i in rule_data.get(\"ownership\")]\n rule_failed = []\n if bureau_type == 'equifax':\n data_key = \"sch:PastDueAmount\"\n reported_date_key = \"sch:DateReported\"\n else:\n data_key = \"Amount_Past_Due\"\n reported_date_key = \"Date_Reported\"\n for rule in rules:\n rule_account_type = rule[\"account_type\"]\n rule_value = float(rule[\"value\"])\n months_back = int(rule[\"months\"])\n date_cutoff = datetime.today() - relativedelta(months=months_back)\n total_overdue = 0\n if bureau_data:\n for account in bureau_data:\n if (str(account.get(\"AccountHoldertypeCode\")) in ownership_list or OwnershipDict.get(account.get(\"sch:OwnershipType\")) in ownership_list):\n tradeline_account_type = AccountTypeDict.get(str(account.get(\"Account_Type\")), \"others\") if bureau_type == 'experian' else account.get(\"sch:AccountType\", \"others\")\n is_open = check_if_open_account(account, bureau_type, approval)\n if is_open and re.sub(r'[^a-zA-Z0-9]', '', tradeline_account_type).lower() == re.sub(r'[^a-zA-Z0-9]', '', rule_account_type).lower():\n reported_datetime = datetime.strptime(account.get(reported_date_key),\"%Y%m%d\") if bureau_type == 'experian' else datetime.strptime(account.get(reported_date_key), \"%Y-%m-%d\")\n if reported_datetime >= date_cutoff:\n if account.get(data_key) is not None:\n total_overdue += 0 if account.get(data_key) == \"\" else int(account.get(data_key))\n if total_overdue >= rule_value:\n rule_failed.append({\n \"rule\": rule,\n \"total_past_dues\": total_overdue,\n \"account_type\": tradeline_account_type\n })\n if rule_failed:\n return [1], rule_failed\n return 0\n except Exception as ex:\n print(traceback.print_exc())", "check_bounce_number": "def check_bounce_number(data, rule):\n if rule:\n import operator\n ops = {'>': operator.gt, '<': operator.lt, '>=': operator.ge, '<=': operator.le, '==': operator.eq, '!=': operator.ne}\n rule_month = rule[0].get(\"rules\")[0].get(\"months\")\n bounce_threshold = rule[0].get(\"rules\")[0].get(\"value\")\n operator = rule[0].get(\"rules\")[0].get(\"operator\")\n lookback_period = datetime.today().replace(day=1, hour=0, minute=0, second=0, microsecond=0) - relativedelta(months=int(rule_month) - 1)\n if data:\n if data.get(\"completeBounceAnalysis\"):\n data = data.get(\"completeBounceAnalysis\")\n elif data.get(\"monthlyBounceAnalysis\"):\n data = data.get(\"monthlyBounceAnalysis\")\n elif data.get('bounceAnalysis'):\n data = data.get('bounceAnalysis')\n else:\n return 0\n if data.get(\"accountWiseBounceAnalytics\"):\n total_bounce = 0\n bounce_log = []\n for month_data in data.get(\"accountWiseBounceAnalytics\")[0].get(\"monthlyBounceAnalysis\"):\n if (month_data.get(\"totalEmiBounceTxn\") and month_data.get(\"totalEmiBounceAmt\")) or (month_data.get(\"totalEcsBounceChargeTxns\") and month_data.get(\"totalEcsBounceChargeAmount\")):\n current_month_year = datetime.strptime(f\"{month_data.get('year')}-{month_data.get('month')}\",\n \"%Y-%m\")\n if lookback_period <= current_month_year:\n if month_data.get(\"totalEmiBounceTxn\") > 0 and month_data.get(\"totalEmiBounceAmt\") > 0:\n total_bounce += month_data.get(\"totalEmiBounceTxn\")\n bounce_log.append({\n 'rule': rule[0],\n 'month': current_month_year,\n 'totalEmiBounceTxn': month_data.get(\"totalEmiBounceTxn\"),\n 'totalEmiBounceAmt': month_data.get(\"totalEmiBounceAmt\")\n })\n if month_data.get(\"totalEcsBounceChargeTxns\") > 0 and month_data.get(\n \"totalEcsBounceChargeAmount\") > 0:\n total_bounce += month_data.get(\"totalEcsBounceChargeTxns\")\n bounce_log.append({\n 'rule': rule[0],\n 'month': current_month_year,\n 'totalEcsBounceChargeTxns': month_data.get(\"totalEcsBounceChargeTxns\"),\n 'totalEcsBounceChargeAmount': month_data.get(\"totalEcsBounceChargeAmount\")\n })\n op_func = ops.get(operator)\n if bounce_log and op_func(total_bounce, int(bounce_threshold)):\n return [1], bounce_log\n return 0", "get_max": "def getMax(lists):\n numeric_values = [int(item) for sublist in lists.values() for item in sublist if item.isdigit()]\n if numeric_values:\n return max(numeric_values)\n else:\n return 0", "string_to_date": "from datetime import datetime\ndef string_to_date(string, format):\n return datetime.strptime(string, format).date()", "before_x_years_date": "from datetime import date\ndef before_x_years_date(years):\n today = date.today()\n start_date = date(today.year - years, today.month, today.day)\n return start_date", "dpd_check_equifax": "from datetime import datetime, date\n\n\ndef dpd_check_equifax(dpd_years_above_0, dpd_count_above_0, equifax_data):\n if dpd_years_above_0 is None or dpd_count_above_0 is None or not equifax_data:\n return 0\n start_date = before_x_years_date(dpd_years_above_0)\n count_per_tradeline = 0\n\n if equifax_data:\n try:\n for data in equifax_data:\n count = 0\n if string_to_date(data['sch:DateOpened'], '%Y-%m-%d') > start_date:\n for index, History in data['sch:History48Months'].items():\n count = 0\n if isinstance(History, list):\n count = sum([1 if '+' in MonthWise['sch:PaymentStatus'] else 0 for MonthWise in History])\n elif isinstance(History, dict):\n if '+' in History['sch:PaymentStatus']:\n count = 1\n else:\n count = 0\n count_per_tradeline += count\n if count_per_tradeline > dpd_count_above_0:\n return 0\n if count_per_tradeline <= dpd_count_above_0:\n return 1\n except:\n return 'No Equifax Bureau'\n else:\n return 1", "dpd_check_experian": "from datetime import datetime, date\n\ndef dpd_check_experian(dpd_years_above_0, dpd_count_above_0, experian_data):\n if dpd_years_above_0 is None or dpd_count_above_0 is None:\n return 0\n start_date = before_x_years_date(dpd_years_above_0)\n count_per_tradeline = 0\n if experian_data:\n try:\n if isinstance(experian_data, list):\n for data in experian_data:\n count = 0\n if string_to_date(data[\"Open_Date\"], \"%Y%m%d\") > start_date:\n if isinstance(data['CAIS_Account_History'], list):\n count = sum([1 for MonthWise in data['CAIS_Account_History'] if MonthWise['Days_Past_Due'] not in (\"0\", 0, None)])\n elif isinstance(data['CAIS_Account_History'], dict):\n if data['CAIS_Account_History']['Days_Past_Due'] not in (0, '0', None):\n count = 1\n else:\n count = 0\n count_per_tradeline += count\n if count_per_tradeline >= dpd_count_above_0:\n return 0\n if count_per_tradeline < dpd_count_above_0:\n return 1\n if isinstance(experian_data, dict):\n data = experian_data\n count = 0\n if string_to_date(data[\"Open_Date\"], \"%Y%m%d\") > start_date:\n if isinstance(data['CAIS_Account_History'], list):\n count = sum([1 for MonthWise in data['CAIS_Account_History'] if \"0\" != MonthWise['Days_Past_Due']])\n elif isinstance(data['CAIS_Account_History'], dict):\n if \"0\" != data['CAIS_Account_History']['Days_Past_Due']:\n count = 1\n else:\n count = 0\n count_per_tradeline += count\n print(count_per_tradeline)\n if count_per_tradeline > dpd_count_above_0:\n return 0\n if count_per_tradeline <= dpd_count_above_0:\n return 1\n except:\n return \"No Experian Bureau\"\n else:\n return 1", "dpd_check_both": "from datetime import datetime, date\n\ndef dpd_check_bureau(dpd_years_above_0, dpd_count_above_0, bureau_data):\n start_date = before_x_years_date(dpd_years_above_0)\n count_per_tradeline = []\n count = 0\n try: # experian\n for data in bureau_data:\n if string_to_date(data[\"Open_Date\"], \"%Y%m%d\") > start_date:\n if isinstance(data['CAIS_Account_History'], list):\n count = sum([1 if \"0\" != MonthWise['Days_Past_Due'] else 0 for MonthWise in data['CAIS_Account_History'] ])\n elif isinstance(data['CAIS_Account_History'], dict):\n if \"0\" != data['CAIS_Account_History']['Days_Past_Due']:\n count = 1\n else:\n count = 0\n count_per_tradeline.append(count)\n max_occurance = max(count_per_tradeline)\n if max_occurance>=dpd_count_above_0:\n return 0\n else:\n return 1\n except: # for equifax\n for data in bureau_data:\n if string_to_date(data[\"sch:DateOpened\"], \"%Y-%m-%d\") > start_date:\n for index, History in data['sch:History48Months'].items():\n if isinstance(History, list):\n count = sum([1 if \"+\" in MonthWise['sch:PaymentStatus'] else 0 for MonthWise in History ])\n elif isinstance(History, dict):\n if \"+\" in History['sch:PaymentStatus']:\n count = 1\n else:\n count = 0\n count_per_tradeline.append(count)\n\n max_occurance = max(count_per_tradeline)\n if max_occurance>=dpd_count_above_0:\n return 0\n else:\n return 1\n return None", "remove_special_symbols": "import re\n\ndef remove_special_symbols(input_string):\n pattern = r'[^\\w\\s]'\n result = re.sub(pattern, '', input_string)\n return result", "is_merchant_eligible": "def is_merchant_eligible(loan_amount, total_amount):\n if int(loan_amount) > int(total_amount):\n return 0\n return 1", "is_employment_verification": "import json\n\ndef is_employment_verification(data):\n epf_data = json.loads(data)\n is_passed = 1\n reason = ''\n is_employed = is_recent = False\n\n if epf_data:\n summary = epf_data.get('result', {}).get('summary', {})\n if summary:\n current_employer = summary.get('uanLookup', {}).get('currentEmployer', None)\n if current_employer:\n uan_data = epf_data.get('result', {}).get('uan', [])\n if uan_data and len(uan_data) > 0:\n employers = uan_data[0].get('employer', [])\n current_employer_details = list(filter(lambda employer: employer['name'] == current_employer, employers))\n if current_employer_details:\n is_employed = current_employer_details[0].get('isEmployed', False)\n is_recent = current_employer_details[0].get('isRecent', False)\n\n if not is_employed or not is_recent:\n if not is_employed:\n reason = \"epf employment verification failed - epf\"\n elif not is_recent:\n reason = \"PF Filing not Recent - epf\"\n is_passed = 0\n\n return [is_passed, reason]", "credit_card_check": "import json\ndef credit_card_check(percentage, foir_data, loan_amount):\n is_passed = 1\n reason = 'No CC History'\n if foir_data:\n credit_card_limits = [float(foir['ApprovedAmount']) - float(foir['Balance']) for foir in json.loads(foir_data) if foir['AccountType'] == 'Credit Card' and foir['DateClosed'] == '-']\n if percentage and loan_amount and credit_card_limits:\n cr_limit_should = float(loan_amount) * float(percentage) / 100\n if max(credit_card_limits) < cr_limit_should:\n is_passed = 0\n reason = f'Credit Card tradeline due amount does not match the grade criteria: {cr_limit_should}'\n return [is_passed, reason]", "credit_card_check2": "import json\ndef credit_card_check2(percentage, foir_data, loan_amount):\n is_passed = 0\n reason = 'No CC History'\n if foir_data:\n credit_card_limits = [float(foir['ApprovedAmount']) - float(foir['Balance']) for foir in json.loads(foir_data) if foir['AccountType'] == 'Credit Card' and foir['DateClosed'] == '-']\n if percentage and loan_amount and credit_card_limits:\n cr_limit_should = float(loan_amount) * float(percentage) / 100\n if max(credit_card_limits) >= cr_limit_should:\n is_passed = 1\n reason = \"\"\n else:\n reason = f'Credit Card tradeline due amount does not match the grade criteria: {cr_limit_should}'\n return [is_passed, reason]", "account_type_check": "def account_type_check(foir_data):\n if foir_data is None:\n return 1\n gold_loan_count = sum(1 for foir in json.loads(foir_data) if foir['AccountType'] == 'Gold Loan' and foir['DateClosed'] == '-')\n\n is_passed = 1\n reason = ''\n\n if gold_loan_count >= 3:\n reason = 'Gold Loan tradeline count does not match the grade criteria'\n is_passed = 0\n\n return [is_passed, reason]", "pre_dpd_check": "def pre_dpd_check(dpd_count, dpd_years, dpd_keywords, foir_data):\n dpdRange = 0\n dpdSumAllTradeline = 0\n is_passed = 1\n reason = \"\"\n if foir_data:\n foir_data = unquote_dict_and_list(foir_data)\n if dpd_count is not None and dpd_years is not None and dpd_keywords is not None:\n for foir in json.loads(foir_data):\n foirData = foir['dpd']\n dpdRange = dpd_count\n dateRange = dpd_years\n created_at = datetime.strptime(foir['created_at'], '%Y-%m-%d')\n if foir['DateOpened'] == '0000-00-00':\n continue\n diff_days = (created_at - datetime.strptime(foir['DateOpened'], '%Y-%m-%d')).days if foir[\n 'DateOpened'] else 0\n all_keywords = []\n years = diff_days / 365\n if dpd_keywords and dpd_keywords != '':\n keywords = dpd_keywords.split(\",\")\n for key in keywords:\n keyword = key.strip()\n keyword = keyword.strip('\"')\n all_keywords.append(keyword)\n if foirData:\n foirClean = [key for key in foirData.keys() if key not in ['000', 'NEW', 'CLSD']]\n if foirClean:\n if years <= dateRange:\n for key, item in foirData.items():\n if key not in all_keywords:\n dpdSumAllTradeline += item\n if dpdSumAllTradeline > dpdRange:\n is_passed = 0\n reason = 'Pre DPD count (within a certain period) does not not match the grade criteria'\n\n if dpd_count is None or dpd_years is None or dpd_keywords is None:\n is_passed = 0\n reason = \"Pre DPD count rule not configured properly\"\n if not foir_data:\n is_passed = 0\n reason = \"No Foir Data\"\n return [is_passed, reason]", "case_type_check": "import json\n\ndef case_type_check(case_type_values, foir_data):\n if not case_type_values:\n return 0\n case_exist = 1\n if foir_data:\n for foir in json.loads(foir_data):\n if case_type_values:\n keywords = case_type_values.split(',')\n if foir['dpd']:\n case_type = json.loads(foir['dpd'])\n if case_type:\n for case_type_item in case_type.keys():\n if case_type_item in keywords:\n case_exist = 0\n break\n else : \n case_exist = None\n return case_exist", "post_dpd_check": "import json\nfrom datetime import datetime\ndef post_dpd_check(dpd_count, dpd_years, dpd_keywords, foir_data):\n print(\"In func\")\n print(dpd_count)\n print(dpd_years)\n print(dpd_keywords)\n dpdRange = 0\n dpdSumAllTradeline = 0\n is_passed = 1\n reason = \"\"\n if foir_data:\n foir_data = unquote_dict_and_list(foir_data)\n if dpd_count is not None and dpd_years is not None and dpd_keywords is not None:\n for foir in json.loads(foir_data):\n foirData = foir['dpd']\n dpdRange = dpd_count\n dateRange = dpd_years\n created_at = datetime.strptime(foir['created_at'], '%Y-%m-%d')\n if foir['DateOpened'] == '0000-00-00':\n continue\n diff_days = (created_at - datetime.strptime(foir['DateOpened'], '%Y-%m-%d')).days if foir['DateOpened'] else 0\n all_keywords = []\n years = diff_days / 365\n if dpd_keywords and dpd_keywords != '':\n keywords = dpd_keywords.split(\",\")\n for key in keywords:\n keyword = key.strip()\n keyword = keyword.strip('\"')\n all_keywords.append(keyword)\n if foirData:\n foirClean = [key for key in foirData.keys() if key not in ['000', 'NEW', 'CLSD']]\n if foirClean:\n if years >= dateRange:\n for key, item in foirData.items():\n if key not in all_keywords:\n dpdSumAllTradeline += item\n if dpdSumAllTradeline > dpdRange:\n is_passed = 0\n reason = \"Post DPD count (above a certain period) does not not match the grade criteria\"\n if dpd_count is None or dpd_years is None or dpd_keywords is None:\n is_passed = 0\n reason = \"Post DPD count not configured properly\"\n else:\n is_passed = 0\n reason = \"No Foir Data\"\n return [is_passed, reason]", "is_term_loan": "import json\ndef is_term_loan(foir_data, is_valid_credit_score, term_loans, current_date, term_loan_amount_paid_percentage, loan_amount):\n if not term_loan_amount_paid_percentage or not loan_amount:\n return 0\n max_sanctioned = []\n old_loan = 0 if is_valid_credit_score else 1\n unsecured_loan = 0 if is_valid_credit_score else 1\n if foir_data:\n foir_data = unquote_dict_and_list(foir_data)\n for foir in json.loads(foir_data):\n if not(foir.get('DateOpened') == '-') and (foir.get('DateClosed')=='-'):\n month_diff = date_diff_in_month(foir['DateOpened'], current_date)\n term_loan = month_diff if month_diff >= 6 else 0\n\n if term_loan > 0:\n old_loan = 0\n\n if foir['AccountType'] in term_loans:\n unsecured_loan = 0\n\n if foir['AccountType'] in term_loans and old_loan == 0 and unsecured_loan == 0 and foir.get('DateClosed')=='-':\n max_sanctioned.append(float(foir['ApprovedAmount']) - float(foir['Balance']))\n\n if term_loan_amount_paid_percentage and loan_amount and max_sanctioned:\n cr_limit_should = float(loan_amount) * term_loan_amount_paid_percentage / 100\n if max(max_sanctioned) < cr_limit_should:\n return 0\n\n return 1", "unquote_dict_and_list": "def unquote_dict_and_list(data):\n if data is not None:\n replacement_dict = {'\\\\': '', '\"{': '{', '}\"': '}', '\"[': '[', ']\"': ']'}\n for key, value in replacement_dict.items():\n data = data.replace(key, value)\n else:\n return data\n return data", "similar_text": "import difflib\n\n\ndef similar_text(str1, str2):\n # Create a SequenceMatcher instance\n matcher = difflib.SequenceMatcher(None, str1, str2)\n\n # Get the similarity ratio (between 0 and 1)\n ratio = matcher.ratio()\n\n # Get the number of matching characters\n matching_blocks = matcher.get_matching_blocks()\n matching_chars = sum(block.size for block in matching_blocks)\n\n # Calculate the percentage similarity\n similarity_percentage = ratio * 100 # Convert the ratio to percentage\n\n # Return the percentage similarity and the number of matching characters\n return similarity_percentage", "bank_validation_check": "def bank_validation_check(validation_perc, transaction_status, applicant_name, account_name):\n account_name = handle_byte_array(bytearray(account_name))\n transaction_status = handle_byte_array(bytearray(transaction_status))\n\n is_passed = 0\n reason = \"Bank details not present in the application\"\n if not validation_perc:\n return 0\n status_check = 0\n name_match = 0\n if transaction_status:\n if transaction_status.lower() in ('transaction successful', 'successful transaction'):\n status_check = 1\n if account_name:\n try:\n percentage = 0\n percentage = similar_text(applicant_name.lower(), account_name.lower()) if account_name else 0\n name_match = 1 if round(percentage) >= validation_perc else 0\n except Exception as e:\n print(\"Exception while name match check\", e)\n if status_check == 0 and name_match == 1:\n reason = \"Status check failed\"\n elif status_check == 1 and name_match == 0:\n reason = \"Name match failed\"\n if status_check == 1 and name_match == 1:\n is_passed = 1\n reason = \"\"\n return [is_passed, reason]", "handle_byte_array": "def handle_byte_array(byte_data):\n # Check if the input is a byte object (either bytes or bytearray)\n if isinstance(byte_data, (bytes, bytearray)):\n # Decode it to a string\n decoded_string = byte_data.decode('utf-8')\n return decoded_string\n else:\n # If it's already a string, just return it\n return byte_data", "check_track_record": "from datetime import datetime\ndef check_track_record(reported_date, net_loan_amount, ownership_dict, accounts, rule, bureau_type, account_type_dict):\n passed_account = []\n success = total_payment_grid = 0\n rule = rule.get(\"value\")\n ownership_type = rule.get(\"ownership\")\n reported_date = '' if not reported_date else datetime.strptime(reported_date, '%Y%m%d')\n relevant_ownerships = [ownership_dict.get(i.capitalize()) for i in ownership_type]\n exception_account_types = rule.get(\"exception\")\n exception_account_types = [i.replace(\" \", \"\").lower() for i in exception_account_types]\n sanction_amount_threshold = rule.get(\"amount\")\n percentage_threshold = (net_loan_amount * rule.get(\"percentage\")/100)\n close_months = rule.get(\"close_months\")\n open_months = rule.get(\"open_months\")\n for account in accounts:\n if str(account.get(\"AccountHoldertypeCode\")) in relevant_ownerships or str(account.get(\"sch:OwnershipType\")) in ownership_type:\n if bureau_type == 'experian':\n close_date_key = \"Date_Closed\"\n account_type_key, account_amount_key = \"Account_Type\", \"Highest_Credit_or_Original_Loan_Amount\"\n if account.get(\"CAIS_Account_History\") and isinstance(account.get(\"CAIS_Account_History\"), list):\n total_payment_grid = len(account.get(\"CAIS_Account_History\"))\n elif isinstance(account.get(\"CAIS_Account_History\"), dict):\n total_payment_grid = 1\n else:\n close_date_key = \"sch:DateClosed\"\n account_type_key, account_amount_key = \"sch:AccountType\", \"sch:SanctionAmount\"\n if account.get(\"sch:History48Months\").get(\"sch:Month\") and isinstance(account.get(\"sch:History48Months\").get(\"sch:Month\"), list):\n total_payment_grid = len(account.get(\"sch:History48Months\").get(\"sch:Month\"))\n elif isinstance(account.get(\"sch:History48Months\").get(\"sch:Month\"), dict):\n total_payment_grid = 1\n threshold_amount = percentage_threshold if percentage_threshold < sanction_amount_threshold else sanction_amount_threshold\n formatted_close_months = '' if not account.get(close_date_key) else datetime.strptime(account.get(close_date_key), '%Y%m%d')\n account_close_months = calculate_bureau_months(reported_date, formatted_close_months)\n if account_close_months is not None and account_close_months < close_months and total_payment_grid > open_months:\n tradeline_account_type = account_type_dict.get(str(account.get(account_type_key))) if bureau_type == 'experian' else account.get(account_type_key)\n if tradeline_account_type and tradeline_account_type.replace(\" \", \"\").lower() not in exception_account_types:\n if bureau_type == 'equifax' and not account.get(account_amount_key):\n account_amount_key = \"sch:HighCredit\"\n if account.get(account_amount_key) and float(account.get(account_amount_key)) > float(threshold_amount):\n success = 1\n passed_account = [account.get(\"Account_Number\")]\n break\n return [success], passed_account", "check_ebill_name_match": "import json\ndef check_ebill_name_match(ebill_name_verify, threshold):\n result = 0\n name_matches = json.loads(ebill_name_verify) if ebill_name_verify else None\n if name_matches:\n if int(name_matches.get(\"applicant_match\")) >= int(threshold):\n result = 1\n if result == 0 and name_matches.get(\"co_applicant_match\") and int(name_matches.get(\"co_applicant_match\")) >= int(threshold):\n result = 1\n return result", "get_banking_month_count": "def get_banking_month_count(analytics_data):\n from datetime import datetime\n \n # --- LIST SANITIZER ---\n if isinstance(analytics_data, list):\n analytics_data = analytics_data[0] if analytics_data else {}\n \n if not analytics_data or not isinstance(analytics_data, dict):\n return 0\n \n account_profiles = analytics_data.get('accountProfiles', [])\n if not account_profiles or not isinstance(account_profiles, list):\n return 0\n \n min_date = None\n max_date = None\n \n for profile in account_profiles:\n summary = profile.get('summary', {})\n start_str = summary.get('transactionStartDate')\n end_str = summary.get('transactionEndDate')\n if start_str:\n try:\n s_date = datetime.strptime(start_str, '%Y-%m-%d')\n if min_date is None or s_date < min_date: min_date = s_date\n except: pass\n if end_str:\n try:\n e_date = datetime.strptime(end_str, '%Y-%m-%d')\n if max_date is None or e_date > max_date: max_date = e_date\n except: pass\n \n if not min_date or not max_date:\n return 0\n \n days_gap = (datetime.now() - max_date).days\n if days_gap > 45:\n return 0 \n \n months = (max_date.year - min_date.year) * 12 + (max_date.month - min_date.month) + 1\n return int(months)", "get_name_match_score": "def get_name_match_score(analytics_data, applicant_name):\n from fuzzywuzzy import fuzz\n \n # --- LIST SANITIZER ---\n if isinstance(analytics_data, list):\n analytics_data = analytics_data[0] if analytics_data else {}\n\n if not applicant_name or not analytics_data or not isinstance(analytics_data, dict):\n return 0\n\n account_profiles = analytics_data.get('accountProfiles', [])\n if not account_profiles:\n return 0\n\n applicant_name = str(applicant_name).lower().strip()\n max_score = 0\n\n for profile in account_profiles:\n holders = profile.get('Holders', {}).get('Holder', [])\n if not holders:\n continue\n holder_name = holders[0].get('name', '')\n if not holder_name:\n continue\n holder_name = str(holder_name).lower().strip()\n name1_set = set(applicant_name.split())\n name2_set = set(holder_name.split())\n if name1_set.issubset(name2_set):\n score = 100\n else:\n score = fuzz.ratio(applicant_name, holder_name)\n if score > max_score:\n max_score = score\n return max_score", "get_recent_bounce_count": "def get_recent_bounce_count(analytics_data, lookback_months):\n from datetime import datetime\n from dateutil.relativedelta import relativedelta\n analytics_data = analytics_data[0] if isinstance(analytics_data, list) and analytics_data else analytics_data\n if not analytics_data or not isinstance(analytics_data, dict):\n return 0\n months_val = int(lookback_months) if str(lookback_months).isdigit() else 3\n lookback_period = datetime.today().replace(day=1, hour=0, minute=0, second=0, microsecond=0) - relativedelta(months=months_val - 1)\n bounce_root = analytics_data.get('bounceAnalysis', {})\n target_data = bounce_root.get('completeBounceAnalysis') or bounce_root.get('monthlyBounceAnalysis') or bounce_root.get('bounceAnalysis') or bounce_root\n total_bounce = 0\n account_wise_list = target_data.get('accountWiseBounceAnalytics') if target_data else None\n account_data = account_wise_list[0] if isinstance(account_wise_list, list) and account_wise_list else {}\n monthly_data = account_data.get('monthlyBounceAnalysis', [])\n for month_data in monthly_data:\n try:\n current_month_year = datetime.strptime(f\"{month_data.get('year')}-{month_data.get('month')}\", \"%Y-%m\")\n if lookback_period <= current_month_year:\n total_bounce += month_data.get('totalEmiBounceTxn', 0) if month_data.get('totalEmiBounceTxn', 0) > 0 and month_data.get('totalEmiBounceAmt', 0) > 0 else 0\n total_bounce += month_data.get('totalEcsBounceChargeTxns', 0) if month_data.get('totalEcsBounceChargeTxns', 0) > 0 and month_data.get('totalEcsBounceChargeAmount', 0) > 0 else 0\n total_bounce += month_data.get('totalInwardNonTechChequeBounceTxns', 0) if month_data.get('totalInwardNonTechChequeBounceTxns', 0) > 0 else 0\n total_bounce += month_data.get('totalIWNonChqBounceChargeTxns', 0) if month_data.get('totalIWNonChqBounceChargeTxns', 0) > 0 else 0\n except Exception:\n continue\n return int(total_bounce)", "get_speculative_spend_percentage": "def get_speculative_spend_percentage(analytics_data, monthly_income):\n from datetime import datetime\n \n # --- LIST SANITIZER ---\n if isinstance(analytics_data, list):\n analytics_data = analytics_data[0] if analytics_data else {}\n\n if not analytics_data or not monthly_income:\n return 0.0\n try:\n income = float(monthly_income)\n if income <= 0: return 0.0\n except:\n return 0.0\n\n account_profiles = analytics_data.get('accountProfiles', [])\n all_transactions = analytics_data.get('allTransactions', [])\n min_date = None\n max_date = None\n\n if account_profiles and isinstance(account_profiles, list):\n for profile in account_profiles:\n summary = profile.get('summary', {})\n start_str = summary.get('transactionStartDate')\n end_str = summary.get('transactionEndDate')\n if start_str:\n try:\n s_date = datetime.strptime(start_str, '%Y-%m-%d')\n if min_date is None or s_date < min_date: min_date = s_date\n except: pass\n if end_str:\n try:\n e_date = datetime.strptime(end_str, '%Y-%m-%d')\n if max_date is None or e_date > max_date: max_date = e_date\n except: pass\n\n months_count = 1\n if min_date and max_date:\n months_count = (max_date.year - min_date.year) * 12 + (max_date.month - min_date.month) + 1\n if months_count < 1: months_count = 1\n\n speculative_categories = [\"Gambling & Betting\", \"Gaming\"]\n total_spec_amount = 0.0\n for txn in all_transactions:\n category = txn.get('category', '')\n if category in speculative_categories:\n amt = float(txn.get('amount', 0))\n if amt < 0: total_spec_amount += abs(amt)\n else: total_spec_amount += amt\n\n avg_monthly_spec_spend = total_spec_amount / months_count\n percentage = (avg_monthly_spec_spend / income) * 100\n return float(f\"{percentage:.2f}\")", "checkAgeAtLastEMI": "from datetime import datetime\nfrom dateutil.relativedelta import relativedelta\ndef age_at_end_of_tenure(dob, tenure):\n if isinstance(dob, str):\n if \"-\" in dob:\n dob = datetime.strptime(dob, \"%Y-%m-%d\")\n else:\n dob = datetime.strptime(dob, \"%Y%m%d\")\n\n end_date = datetime.today() + relativedelta(months=int(tenure))\n return relativedelta(end_date, dob).years" } ] } ] }, "sources": { "sourceFile": { "cf": "cfwebbeta", "loc": "line_of_credit" }, "connections": [ { "db": "cfwebbeta", "status": true, "table": [ { "consumer_application": [ "loan_amount", "monthly_income", "dob", "employee_status" ], "emi_calculation": [ "remaining_emi" ], "credit_report": [ "Score", "panValidation" ], "foir": [ "CFFoir" ], "status_logs": [ "rejected" ] } ] }, { "db": "line_of_credit", "table": [ { "applications": [ "loan_amount" ] } ] } ] } }