ofs_aif.scenario package¶
Submodules¶
ofs_aif.scenario.human_trafficking module¶
- class HumanTrafficking(fic_mis_date=None, lookback=None, batch_run_id=None, bus_dmn_list=None, jrsdcn_cd=None, objective_id=None, threshold_set_id=None)¶
Bases:
Scenario
class HumanTrafficking implements APIs for Human Trafficking features
- get_data_HT()¶
API creates necessary base data for human trafficking scenario
- Returns:
- List dataframes returns from this API.
df_master : a master dataframe contains aggregated information for focus
trxn_df_mstr : a transaction dataframe which contains row transaction involved.
- Examples:
>>> df_master, trxn_df_mstr = shell.get_data_HT()
- human_trf_transaction_volume()¶
API creates datasets for human trafficking red flag for Multiple small cash deposits and withdrawals in different locations.
- Returns:
- List of features for customers. Following are the details.
Customer ID
Credit_Debit_Amount_Ratio
No_different_states_perc
Credit_Debit_Count_Ratio
Perc_days_reporting_avoided
Total Credit Amount
Perc_Activity_Between Time Limits
- Examples:
>>> feature = htObj.human_trf_transaction_volume()
ofs_aif.scenario.scenario module¶
- class Scenario(fic_mis_date=None)¶
Bases:
aif
class Scenario implements APIs for all common utility across all scenarios
- add_to_scenario_dataset(pandas_df_list, key_var='ENTITY_ID', overwrite=True)¶
- Description:
Customized merge used for concatenation multiple dataframes for AIF use case.
- Parameters:
pandas_df_list – list of dataframes to merge
key_var – Identity column. Default “ENTITY_ID”
overwrite – Boolean True/False. Overwrite existing column’s data if True.
- Returns:
resulted dataframe
- alert_details(batch_run_id=None)¶
Description: Returns description of newly created events
- Parameters:
batch_run_id – Run ID of current batch
- Returns:
resulted dataframe
- generate_break(df=None, trxn_df=None, conditions=None)¶
API creates break based on threshold parameter.
- Returns:
Insert data into all break related staging tables. On successful return “Break generated Successfully…!”
- Examples:
>>> shell.generate_break(df, filter) df : data frame containing base data. filter : Filter criteria on data frame.
- get_data(scenario=None)¶
- Description:
API consolidate all red flags togather and return one master data-frame. Internally it calls two separate API for shell and HT for shell and humantrafficking scenario respectively.
- return:
Consolidated data-frame for focus and transaction
- Examples:
>>> df_mstr, trxn_df_mstr = shell.get_data(self, scenario=None)
- run_ecm_batch()¶
ofs_aif.scenario.shell_scenario module¶
- class ShellScenario(fic_mis_date=None, lookback=None, bus_dmn_list=None, jrsdcn_cd=None, batch_run_id=None, objectiveid=None, threshold_set_id=None)¶
Bases:
Scenario
class ShellScenario implements APIs for Shell Account features
- cbk_exceed_anticipatory_profile_credit()¶
API creates datasets for Shell Account red flag credit transaction. Following are small description. for A foreign correspondent bank exceeds the anticipated volume projected in its client profile for wire transfers in a given time period.
- Returns:
- List of features for customers. Following are the details.
Customer ID
Is_anticipated_profile_exceeded_credit
Perc_Credit_Amount_CBK_exceeded
Perc_Credit_Amount_CBK_total
is_seasoned
- Examples:
>>> df_cr = shell.cbk_exceed_anticipatory_profile_credit()
- cbk_exceed_anticipatory_profile_debit()¶
API creates datasets for Shell Account red flag debit transaction. Following are small description. for A foreign correspondent bank exceeds the anticipated volume projected in its client profile for wire transfers in a given time period.
- Returns:
- List of features for customers. Following are the details.
Customer ID
Is_aniticipated_profile_exceeded_debit
Perc_Debit_Amount_CBK_exceeded
Perc_Debit_Amount_CBK_total
is_seasoned
- Examples:
>>> df_cr = shell.cbk_exceed_anticipatory_profile_debit()
- cbk_high_risk_geography_beneficiary()¶
API creates datasets for Shell Account red flag debit/credit transaction. Following are small feature description. Frequent involvement of beneficiaries located in high-risk, offshore financial centers.
- Returns:
- List of features for customers. Following are the details.
Customer ID
Percentage of high risk credit amount
Percentage of high risk debit amount
Percentage of high risk credit count
Percentage of high risk debit count
- Examples:
>>> df_hrg = shell.cbk_high_risk_geography_beneficiary()
- get_data_shell()¶
API creates necessary base data for shell scenario
- Returns:
- List dataframes returns from this API.
df_master : a master dataframe contains aggregated information for focus
trxn_df_mstr : a transaction dataframe which contains row transaction involved.
- Examples:
>>> df_master, trxn_df_mstr = shell.get_data_shell()