generated from user_client2024/78
136 lines
4.2 KiB
Python
136 lines
4.2 KiB
Python
#!/usr/bin/env python
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# coding: utf-8
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# In[7]:
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import pandas as pd
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query = """
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select final.CUSTOMER_NUMBER_main as Focal_id,
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CAST(final.Cash_deposit_total AS DECIMAL(18, 2)) AS Cash_deposit_total,
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final.Cash_deposit_count,
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final.SEGMENT,
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final.RISK,
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final.SAR_FLAG
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from
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(
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(
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select subquery.CUSTOMER_NUMBER_1 as CUSTOMER_NUMBER_main,
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subquery.Cash_deposit_total,
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subquery.Cash_deposit_count
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from
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(
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select customer_number as CUSTOMER_NUMBER_1,
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sum(transaction_amount) as Cash_deposit_total,
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count(*) as Cash_deposit_count
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from
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(
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select *
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from {trans_data} trans_table
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left join {acc_data} acc_table
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on trans_table.benef_account_number = acc_table.account_number
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) trans
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where account_number not in ('None')
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and transaction_desc = 'CASH RELATED TRANSACTION'
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group by customer_number
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) subquery
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) main
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left join
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(
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select cd.CUSTOMER_NUMBER_3 as CUSTOMER_NUMBER_cust,
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cd.SEGMENT,
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cd.RISK,
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case
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when ad.SAR_FLAG is NULL then 'N'
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else ad.SAR_FLAG
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end as SAR_FLAG
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from
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(
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select customer_number as CUSTOMER_NUMBER_3,
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business_segment as SEGMENT,
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case
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when RISK_CLASSIFICATION = 1 then 'Low Risk'
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when RISK_CLASSIFICATION = 2 then 'Medium Risk'
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when RISK_CLASSIFICATION = 3 then 'High Risk'
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else 'Unknown Risk'
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end AS RISK
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from {cust_data}
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) cd
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left join
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(
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select customer_number as CUSTOMER_NUMBER_4,
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sar_flag as SAR_FLAG
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from {alert_data}
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) ad
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on cd.CUSTOMER_NUMBER_3 = ad.CUSTOMER_NUMBER_4
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) as cust_alert
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on cust_alert.CUSTOMER_NUMBER_cust = main.CUSTOMER_NUMBER_main
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) as final
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"""
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from tms_data_interface import SQLQueryInterface
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class Scenario:
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seq = SQLQueryInterface(schema="transactionschema")
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def logic(self, **kwargs):
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row_list = self.seq.execute_raw(query.format(trans_data="transaction10m",
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cust_data="customer_data_v1",
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acc_data="account_data_v1",
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alert_data="alert_data_v1")
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)
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cols = ["Focal_id", "Cash_deposit_total", "Cash_deposit_count",
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"Segment", "Risk", "SAR_FLAG"]
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df = pd.DataFrame(row_list, columns = cols)
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df["Cash_deposit_total"] = df["Cash_deposit_total"].astype(float)
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# Step 1: Compute 90th percentiles per Segment for all 3 fields
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percentiles = (
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df.groupby("Segment")[["Cash_deposit_total",
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"Cash_deposit_count"]]
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.quantile(0.98)
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.reset_index()
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)
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# Rename columns for clarity
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percentiles = percentiles.rename(columns={
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"Cash_deposit_total": "P90_Credit",
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"Cash_deposit_count": "P90_Credit_count"
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})
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# Step 2: Merge back to main df
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df = df.merge(percentiles, on="Segment", how="left")
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# Step 3: Identify customers above 90th percentile in ANY of the 3 metrics
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high_pop = (
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(df["Cash_deposit_total"] > df["P90_Credit"]) &
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(df["Cash_deposit_count"] > df["P90_Credit_count"])
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)
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# Step 4: Randomly select 0.1% sample from high-risk population
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sample_fraction = 0.1 # 0.1%
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high_pop_indices = df[high_pop].sample(frac=sample_fraction, random_state=42).index
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# Step 5: Set SAR_FLAG values
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df["SAR_FLAG"] = "N" # default for all
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df.loc[high_pop_indices, "SAR_FLAG"] = "Y" # assign Y to 0.1% random high-risk population
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return df
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# In[8]:
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# sen = Scenario()
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# a = sen.logic()
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# In[10]:
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# a[a["SAR_FLAG"] == "Y"]
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