generated from user_client2024/77
550 lines
21 KiB
Plaintext
550 lines
21 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 53,
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"id": "e706cfb0-2234-4c4c-95d8-d1968f656aa0",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"import pandas as pd"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 43,
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"id": "2f9a4ca7-c066-4d93-9957-0d9145f9265d",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"from tms_data_interface import SQLQueryInterface\n",
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"seq = SQLQueryInterface(schema=\"transactionschema\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "fc212ace-ca7a-45f2-8137-f436c6123652",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[['account_data_v1'],\n",
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" ['account_data_v2'],\n",
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" ['alert_data_v1'],\n",
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" ['alert_data_v2'],\n",
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" ['customer_data_v1'],\n",
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" ['customer_data_v2'],\n",
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" ['transaction10m'],\n",
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" ['transaction60m']]"
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]
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"seq.execute_raw(\"show tables\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 54,
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"id": "134d0b3d-5481-4975-af07-c80ab09d6dd2",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"query = \"\"\"\n",
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" select final.CUSTOMER_NUMBER_main as Focal_id,\n",
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" final.Credit_transaction_amount,\n",
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" final.Total_no_of_credit_transactions,\n",
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" final.Debit_transaction_amount,\n",
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" final.Total_no_of_debit_transactions,\n",
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" final.Wash_Ratio,\n",
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" final.SEGMENT,\n",
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" final.RISK,\n",
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" final.SAR_FLAG\n",
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" from \n",
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" (\n",
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" (\n",
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" select subquery.CUSTOMER_NUMBER_1 as CUSTOMER_NUMBER_main,\n",
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" subquery.Credit_transaction_amount,\n",
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" subquery.Total_no_of_credit_transactions,\n",
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" case\n",
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" when subquery.Debit_transaction_amount is NULL then 0\n",
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" else Debit_transaction_amount\n",
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" end as Debit_transaction_amount,\n",
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" case\n",
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" when subquery.Total_no_of_debit_transactions is NULL then 0\n",
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" else Total_no_of_debit_transactions\n",
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" end as Total_no_of_debit_transactions,\n",
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" case\n",
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" when subquery.Debit_transaction_amount = 0\n",
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" or subquery.Debit_transaction_amount is NULL then 0\n",
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" else subquery.Credit_transaction_amount / subquery.Debit_transaction_amount\n",
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" end as Wash_Ratio\n",
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" from \n",
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" (\n",
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" (\n",
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" select customer_number as CUSTOMER_NUMBER_1, \n",
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" sum(transaction_amount) as Credit_transaction_amount, \n",
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" count(*) as Total_no_of_credit_transactions\n",
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" from \n",
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" (\n",
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" select * \n",
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" from {trans_data} as trans_table left join {acc_data} as acc_table\n",
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" on trans_table.benef_account_number = acc_table.account_number\n",
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" )\n",
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" where account_number not in ('None')\n",
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" group by 1\n",
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" ) credit left join\n",
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" (\n",
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" select customer_number as CUSTOMER_NUMBER_2, \n",
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" sum(transaction_amount) as Debit_transaction_amount, \n",
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" count(*) as Total_no_of_debit_transactions\n",
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" from \n",
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" (\n",
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" select * \n",
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" from {trans_data} as trans_table left join {acc_data} as acc_table\n",
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" on trans_table.orig_account_number = acc_table.account_number\n",
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" )\n",
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" where account_number not in ('None')\n",
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" group by 1\n",
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" ) debit on credit.CUSTOMER_NUMBER_1 = debit.CUSTOMER_NUMBER_2 \n",
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" ) subquery\n",
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" ) main left join \n",
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" (\n",
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" select subquery.CUSTOMER_NUMBER_3 as CUSTOMER_NUMBER_cust,\n",
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" subquery.SEGMENT,\n",
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" subquery.RISK,\n",
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" case\n",
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" when subquery.SAR_FLAG is NULL then 'N'\n",
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" else subquery.SAR_FLAG\n",
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" end as SAR_FLAG \n",
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" from\n",
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" (\n",
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" (\n",
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" select customer_number as CUSTOMER_NUMBER_3, \n",
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" business_segment as SEGMENT,\n",
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" case\n",
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" when RISK_CLASSIFICATION = 1 then 'Low Risk'\n",
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" when RISK_CLASSIFICATION = 2 then 'Medium Risk'\n",
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" when RISK_CLASSIFICATION = 3 then 'High Risk'\n",
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" else 'Unknown Risk'\n",
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" end AS RISK\n",
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" from {cust_data}\n",
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" ) cd left join\n",
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" (\n",
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" select customer_number as CUSTOMER_NUMBER_4, \n",
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" sar_flag as SAR_FLAG\n",
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" from {alert_data}\n",
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" ) ad on cd.CUSTOMER_NUMBER_3 = ad.CUSTOMER_NUMBER_4\n",
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" ) subquery\n",
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" ) cust_alert on cust_alert.CUSTOMER_NUMBER_cust = main.CUSTOMER_NUMBER_main\n",
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" ) final\n",
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"\"\"\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 57,
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"id": "d220561a-34c9-48d2-8e2f-5d174a87540b",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"from tms_data_interface import SQLQueryInterface\n",
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"\n",
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"class Scenario:\n",
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" seq = SQLQueryInterface(schema=\"transactionschema\")\n",
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"\n",
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" def logic(self, **kwargs):\n",
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" row_list = self.seq.execute_raw(query.format(trans_data=\"transaction10m\",\n",
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" cust_data=\"customer_data_v1\",\n",
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" acc_data=\"account_data_v1\",\n",
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" alert_data=\"alert_data_v1\")\n",
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" )\n",
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" cols = [\"Focal_id\", \"Credit_transaction_amount\",\n",
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" \"Total_no_of_credit_transactions\",\n",
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" \"Debit_transaction_amount\", \"Total_no_of_debit_transactions\",\n",
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" \"Wash_Ratio\", \"Segment\", \"Risk\", \"SAR_FLAG\"]\n",
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" df = pd.DataFrame(row_list, columns = cols)\n",
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" df[[\"Credit_transaction_amount\",\n",
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" \"Debit_transaction_amount\"]] = df[[\"Credit_transaction_amount\",\n",
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" \"Debit_transaction_amount\"]].astype('int')\n",
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" df[\"Wash_Ratio\"] = df[\"Wash_Ratio\"].astype('float')\n",
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" # Step 1: Compute 90th percentiles per Segment for all 3 fields\n",
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" percentiles = (\n",
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" df.groupby(\"Segment\")[[\"Credit_transaction_amount\",\n",
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" \"Debit_transaction_amount\",\n",
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" \"Wash_Ratio\"]]\n",
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" .quantile(0.98)\n",
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" .reset_index()\n",
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" )\n",
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"\n",
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" # Rename columns for clarity\n",
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" percentiles = percentiles.rename(columns={\n",
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" \"Credit_transaction_amount\": \"P90_Credit\",\n",
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" \"Debit_transaction_amount\": \"P90_Debit\",\n",
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" \"Wash_Ratio\": \"P90_Wash\"\n",
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" })\n",
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"\n",
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" # Step 2: Merge back to main df\n",
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" df = df.merge(percentiles, on=\"Segment\", how=\"left\")\n",
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"\n",
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" # Step 3: Identify customers above 90th percentile in ANY of the 3 metrics\n",
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" high_pop = (\n",
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" (df[\"Credit_transaction_amount\"] > df[\"P90_Credit\"]) |\n",
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" (df[\"Debit_transaction_amount\"] > df[\"P90_Debit\"]) |\n",
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" (df[\"Wash_Ratio\"] > df[\"P90_Wash\"])\n",
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" )\n",
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"\n",
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" # Step 4: Randomly select 0.1% sample from high-risk population\n",
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" sample_fraction = 0.1 # 0.1%\n",
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" high_pop_indices = df[high_pop].sample(frac=sample_fraction, random_state=42).index\n",
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"\n",
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" # Step 5: Set SAR_FLAG values\n",
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" df[\"SAR_FLAG\"] = \"N\" # default for all\n",
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" df.loc[high_pop_indices, \"SAR_FLAG\"] = \"Y\" # assign Y to 0.1% random high-risk population\n",
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" return df"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 58,
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"id": "2e5a0ea9-64cd-4a8d-9a5d-e5e7b36a401a",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"sen = Scenario()\n",
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"a = sen.logic()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 59,
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"id": "830c7ec3-9707-46db-9b27-ac4f9d46a03a",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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||
"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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||
" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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||
" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>Focal_id</th>\n",
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" <th>Credit_transaction_amount</th>\n",
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" <th>Total_no_of_credit_transactions</th>\n",
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" <th>Debit_transaction_amount</th>\n",
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" <th>Total_no_of_debit_transactions</th>\n",
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" <th>Wash_Ratio</th>\n",
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" <th>Segment</th>\n",
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" <th>Risk</th>\n",
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" <th>SAR_FLAG</th>\n",
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" <th>P90_Credit</th>\n",
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" <th>P90_Debit</th>\n",
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" <th>P90_Wash</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>PN478710</td>\n",
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" <td>2805352312</td>\n",
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" <td>2020</td>\n",
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" <td>2787126309</td>\n",
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" <td>2025</td>\n",
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" <td>1.006539</td>\n",
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" <td>Corporate Banking</td>\n",
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" <td>Low Risk</td>\n",
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" <td>N</td>\n",
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" <td>6.274828e+09</td>\n",
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" <td>6.259298e+09</td>\n",
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" <td>1.090121</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>PN483125</td>\n",
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" <td>3890052135</td>\n",
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" <td>2797</td>\n",
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" <td>3968882113</td>\n",
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" <td>2850</td>\n",
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" <td>0.980138</td>\n",
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" <td>Govt. Entities</td>\n",
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" <td>Low Risk</td>\n",
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" <td>N</td>\n",
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" <td>6.112897e+09</td>\n",
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" <td>6.072409e+09</td>\n",
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" <td>1.112059</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>PN890403</td>\n",
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" <td>4136296083</td>\n",
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" <td>2937</td>\n",
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" <td>3999785063</td>\n",
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" <td>2824</td>\n",
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" <td>1.034130</td>\n",
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" <td>SME</td>\n",
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" <td>Low Risk</td>\n",
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" <td>N</td>\n",
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" <td>5.709904e+09</td>\n",
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" <td>5.559419e+09</td>\n",
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" <td>1.118816</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>PN531475</td>\n",
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" <td>4183673982</td>\n",
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" <td>2861</td>\n",
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" <td>3987068168</td>\n",
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" <td>2770</td>\n",
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" <td>1.049311</td>\n",
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" <td>Corporate Banking</td>\n",
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" <td>Low Risk</td>\n",
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" <td>N</td>\n",
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" <td>6.274828e+09</td>\n",
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" <td>6.259298e+09</td>\n",
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" <td>1.090121</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>PN147722</td>\n",
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" <td>1775594615</td>\n",
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" <td>1225</td>\n",
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" <td>1641559222</td>\n",
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" <td>1221</td>\n",
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" <td>1.081651</td>\n",
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" <td>SME</td>\n",
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" <td>Low Risk</td>\n",
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" <td>N</td>\n",
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" <td>5.709904e+09</td>\n",
|
||
" <td>5.559419e+09</td>\n",
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||
" <td>1.118816</td>\n",
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||
" </tr>\n",
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||
" <tr>\n",
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||
" <th>...</th>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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||
" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" </tr>\n",
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" <tr>\n",
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||
" <th>10009</th>\n",
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||
" <td>PN955059</td>\n",
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||
" <td>31106290</td>\n",
|
||
" <td>264</td>\n",
|
||
" <td>25266130</td>\n",
|
||
" <td>369</td>\n",
|
||
" <td>1.231146</td>\n",
|
||
" <td>Priority Banking</td>\n",
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||
" <td>Low Risk</td>\n",
|
||
" <td>N</td>\n",
|
||
" <td>7.616620e+07</td>\n",
|
||
" <td>5.263062e+07</td>\n",
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||
" <td>1.921224</td>\n",
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" </tr>\n",
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" <tr>\n",
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||
" <th>10010</th>\n",
|
||
" <td>PN602067</td>\n",
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||
" <td>29780658</td>\n",
|
||
" <td>238</td>\n",
|
||
" <td>27796448</td>\n",
|
||
" <td>405</td>\n",
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" <td>1.071384</td>\n",
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" <td>Others</td>\n",
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" <td>High Risk</td>\n",
|
||
" <td>N</td>\n",
|
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" <td>7.897534e+07</td>\n",
|
||
" <td>5.488447e+07</td>\n",
|
||
" <td>1.931817</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>10011</th>\n",
|
||
" <td>PN213487</td>\n",
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||
" <td>41410071</td>\n",
|
||
" <td>274</td>\n",
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||
" <td>23896844</td>\n",
|
||
" <td>368</td>\n",
|
||
" <td>1.732868</td>\n",
|
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" <td>Others</td>\n",
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" <td>Low Risk</td>\n",
|
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" <td>N</td>\n",
|
||
" <td>7.897534e+07</td>\n",
|
||
" <td>5.488447e+07</td>\n",
|
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" <td>1.931817</td>\n",
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" </tr>\n",
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" <tr>\n",
|
||
" <th>10012</th>\n",
|
||
" <td>PN563065</td>\n",
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" <td>34009021</td>\n",
|
||
" <td>251</td>\n",
|
||
" <td>32563582</td>\n",
|
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" <td>375</td>\n",
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" <td>1.044388</td>\n",
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" <td>Others</td>\n",
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" <td>Low Risk</td>\n",
|
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" <td>N</td>\n",
|
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" <td>7.897534e+07</td>\n",
|
||
" <td>5.488447e+07</td>\n",
|
||
" <td>1.931817</td>\n",
|
||
" </tr>\n",
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" <tr>\n",
|
||
" <th>10013</th>\n",
|
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" <td>PN388875</td>\n",
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" <td>30904340</td>\n",
|
||
" <td>236</td>\n",
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||
" <td>21938266</td>\n",
|
||
" <td>344</td>\n",
|
||
" <td>1.408696</td>\n",
|
||
" <td>Mass Market</td>\n",
|
||
" <td>Medium Risk</td>\n",
|
||
" <td>N</td>\n",
|
||
" <td>7.921967e+07</td>\n",
|
||
" <td>5.290545e+07</td>\n",
|
||
" <td>1.915159</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>10014 rows × 12 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" Focal_id Credit_transaction_amount Total_no_of_credit_transactions \\\n",
|
||
"0 PN478710 2805352312 2020 \n",
|
||
"1 PN483125 3890052135 2797 \n",
|
||
"2 PN890403 4136296083 2937 \n",
|
||
"3 PN531475 4183673982 2861 \n",
|
||
"4 PN147722 1775594615 1225 \n",
|
||
"... ... ... ... \n",
|
||
"10009 PN955059 31106290 264 \n",
|
||
"10010 PN602067 29780658 238 \n",
|
||
"10011 PN213487 41410071 274 \n",
|
||
"10012 PN563065 34009021 251 \n",
|
||
"10013 PN388875 30904340 236 \n",
|
||
"\n",
|
||
" Debit_transaction_amount Total_no_of_debit_transactions Wash_Ratio \\\n",
|
||
"0 2787126309 2025 1.006539 \n",
|
||
"1 3968882113 2850 0.980138 \n",
|
||
"2 3999785063 2824 1.034130 \n",
|
||
"3 3987068168 2770 1.049311 \n",
|
||
"4 1641559222 1221 1.081651 \n",
|
||
"... ... ... ... \n",
|
||
"10009 25266130 369 1.231146 \n",
|
||
"10010 27796448 405 1.071384 \n",
|
||
"10011 23896844 368 1.732868 \n",
|
||
"10012 32563582 375 1.044388 \n",
|
||
"10013 21938266 344 1.408696 \n",
|
||
"\n",
|
||
" Segment Risk SAR_FLAG P90_Credit P90_Debit \\\n",
|
||
"0 Corporate Banking Low Risk N 6.274828e+09 6.259298e+09 \n",
|
||
"1 Govt. Entities Low Risk N 6.112897e+09 6.072409e+09 \n",
|
||
"2 SME Low Risk N 5.709904e+09 5.559419e+09 \n",
|
||
"3 Corporate Banking Low Risk N 6.274828e+09 6.259298e+09 \n",
|
||
"4 SME Low Risk N 5.709904e+09 5.559419e+09 \n",
|
||
"... ... ... ... ... ... \n",
|
||
"10009 Priority Banking Low Risk N 7.616620e+07 5.263062e+07 \n",
|
||
"10010 Others High Risk N 7.897534e+07 5.488447e+07 \n",
|
||
"10011 Others Low Risk N 7.897534e+07 5.488447e+07 \n",
|
||
"10012 Others Low Risk N 7.897534e+07 5.488447e+07 \n",
|
||
"10013 Mass Market Medium Risk N 7.921967e+07 5.290545e+07 \n",
|
||
"\n",
|
||
" P90_Wash \n",
|
||
"0 1.090121 \n",
|
||
"1 1.112059 \n",
|
||
"2 1.118816 \n",
|
||
"3 1.090121 \n",
|
||
"4 1.118816 \n",
|
||
"... ... \n",
|
||
"10009 1.921224 \n",
|
||
"10010 1.931817 \n",
|
||
"10011 1.931817 \n",
|
||
"10012 1.931817 \n",
|
||
"10013 1.915159 \n",
|
||
"\n",
|
||
"[10014 rows x 12 columns]"
|
||
]
|
||
},
|
||
"execution_count": 59,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"a"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 49,
|
||
"id": "150bb5ce-6be1-44fc-a606-6d375354626d",
|
||
"metadata": {
|
||
"tags": []
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# a[a[\"SAR_FLAG\"] == \"Y\"]\n"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3 (ipykernel)",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.11.8"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|