def generate_cc_credits(merchant, no_trans, count):
for i in range(no_trans):
acct = random.choice(python_account_ID.accountid)
cc_list = python_CC.CC_Dict[acct]
#7)Set customer credit limit - skew to clients with $1000-$25000 and 10% with $25K - $50K
limit = max(
max((randrange(1, 101, 1) - 99), 0) *
randrange(25000, 50000, 1000), randrange(1000, 25000, 1000))
#local Amt variable to calculate customer total usage
usedAmt = 0
maxDate = datetime(0001, 01, 01)
NoTrans = randrange(100, 150, 1)
#loop to generate NoTrans transactions per customer, we can add logic for probabilities of # transactions if neccessary random number generator to avoid the constant value
for j in range(NoTrans):
dt = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
#generate amount for current transaction with 50%-50% distribution on credits and debits
tmpAmt = randrange(1, limit, 100)
#if not credit then generate debit
#if tmpAmt == 0:
#tmpAmt = randrange(1,limit,100)
#add current amount to client total account usage
usedAmt = usedAmt + tmpAmt
#pull value from dictionary for randomly selected merchant category
cat = random.choice(merchant)
tranType = ''
#set transaction type based on amount
tranType = random.choice(Transaction_Type_Credits)
#tranType random.choice(Transaction_Type)
#append values to row list
row = [str(count) + '_' + dt
] + [acct] + [gen_data.create_company_name()]
row.append(cat)
row.append(python_merchant_cat.All_Merchant_Cat[cat])
#date posted
date1 = gen_data.create_date(past=True)
if date1 > maxDate:
maxDate = date1
#date of transaction a day later
date2 = date1 - timedelta(days=1)
row.extend([
date1, date2, tranType, 'US', limit, tmpAmt, usedAmt,
cc_list[0], cc_list[1]
])
count = count + 1
writer.writerow(row)
#post generating all transactions, check account balance - if overpaid - refund $ and add a refun transaction
if usedAmt < limit * (-1):
row = [str(count) + '_' + dt] + [acct] + [''] + [''] + ['']
date1temp = maxDate + timedelta(days=90)
date2 = date1temp - timedelta(days=1)
row.extend([
date1temp, date2, 'Refund', '', limit,
abs(limit - abs(usedAmt)) * (-1), 0, cc_list[0], cc_list[1]
])
count = count + 1
usedAmt = 0
maxDate = datetime(0001, 01, 01)
writer.writerow(row)
def gen_cc_external(merchant,no_trans,count):
for i in range(no_trans):
acct=random.choice(python_account_ID.accountid)
cc_list=python_CC.CC_Dict[acct]
#7)Set customer credit limit - skew to clients with $1000-$25000 and 10% with $25K - $50K
limit = max(max((randrange(1,101,1)-99),0)* randrange(25000,50000,1000),randrange(1000,25000,1000))
#local Amt variable to calculate customer total usage
usedAmt = 0
maxDate= datetime(0001,01,01)
NoTrans = randrange(100,150,1)
#loop to generate NoTrans transactions per customer, we can add logic for probabilities of # transactions if neccessary random number generator to avoid the constant value
for j in range(NoTrans):
dt = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
#generate amount for current transaction with 50%-50% distribution on credits and debits
tmpAmt = max((randrange(1,3,1)-1),0)* randrange(1,limit,100)*(-1)
#if not credit then generate debit
if tmpAmt == 0:
tmpAmt = randrange(1,limit,100)
#add current amount to client total account usage
usedAmt = usedAmt + tmpAmt
#pull value from dictionary for randomly selected merchant category
cat = ''
tranType = ''
row = [str(count)+'_'+dt] + [acct] + [gen_data.create_company_name()]
#set transaction type based on amount
if tmpAmt < 0:
tranType = random.choice(Transaction_Type_Credits)
row.append('Non-BMO Acct')
row.append('')
else:
tranType = random.choice(Transaction_Type_Debits)
cat = random.choice(merchant)
row.append(cat)
row.append(python_merchant_cat.All_Merchant_Cat[cat])
#tranType random.choice(Transaction_Type)
#date posted
date1 = gen_data.create_date(past=True)
if date1 > maxDate:
maxDate = date1
#date of transaction a day later
date2 = date1-timedelta(days=1)
row.extend([date1, date2, tranType,random.choice(Country_Red),limit,tmpAmt,usedAmt,cc_list[0],cc_list[1]])
count = count + 1
writer.writerow(row)
#post generating all transactions, check account balance - if overpaid - refund $ and add a refun transaction
if usedAmt < limit * (-1):
row = [str(count)+'_'+dt]+ [acct] + ['']+['']+['']
date1temp=maxDate+timedelta(days=90)
date2=date1temp-timedelta(days=1)
row.extend([date1temp, date2, 'Refund','',limit,abs(limit-abs(usedAmt))*(-1),0,cc_list[0],cc_list[1]])
count = count + 1
usedAmt = 0
maxDate= datetime(0001,01,01)
writer.writerow(row)
#Creates Current Address
zip = random.choice(zips.zip)
addr = geo_data.create_city_state_zip[zip]
#Creates Previous address
zip2 = random.choice(zips.zip)
addr2 = geo_data.create_city_state_zip[zip2]
#Add additional data elements to current csv row
lrg_cash_ex = random.choice(Yes_No)
#Condition for SARs and Demarketed Clients
if (Clsd == 'Yes'):
#1% of closed accounts are demarketed but never had a SAR filed
if (max((randrange(0, 101, 1) - 99), 0) == 1 and SAR == 'No'):
demarket = 'Yes'
dem_date = gen_data.create_date(past=True)
if (max((randrange(0, 11, 1) - 9), 0) == 1 and demarket == 'No'):
#10% of closed accounts have SARs
SAR = 'Yes'
#90% of closed accounts with SARs are demarketed
if (max((randrange(0, 11, 1) - 9), 0) == 0):
demarket = 'Yes'
dem_date = gen_data.create_date(past=True)
if (max((randrange(0, 101, 1) - 99), 0) == 1):
PEP = 'Yes'
row.extend([
addr[0], addr[1], zip, 'US', addr2[0], addr2[1], zip2, 'US',
gen_data.create_birthday(min_age=2, max_age=85), PEP, SAR, Clsd
])
'9223':'Bail and Bond Payments',
'9311':'Tax Payments Government Agencies',
'9399':'Government Services (Not Elsewhere Classified)',
'9402':'Postal Services Government Only',
'9405':'U.S. Federal Government Agencies or Departments',
'9950':'Intra Company Purchases'
};
with open('CreditCard_Transaction_MerchantCredits.csv','w') as f1:
writer=csv.writer(f1, delimiter=',',lineterminator='\n',)
writer.writerow(['rownum'] +['Account_Number'] + ['Merchant_Name']+['Merchant_Category_Code']+['Merchant_Category_Desc'] +\
['Post_Date'] + ['Transaction_Date'] + ['Transaction_Type'] +['Merchant_Country']+['Credit_Limit']+['Amount'])
for i in range(10):
dt=datetime.now().strftime("%Y-%m-%d %H:%M:%S")
row = [str(i)+'_'+dt]+[random.choice(python_account_ID.accountid)] +[gen_data.create_company_name()]
cat=random.choice(Merchant_Category)
row.append(cat)
row.append(All_Merchant_Cat[cat])
date1= gen_data.create_date(past=True)
date2=date1-timedelta(days=1)
row.append(date1)
row.append(date2)
#Set customer credit limit - skew to clients with $1000-$25000 and 10% with $25K - $50K
limit = max(max((randrange(1,101,1)-99),0)* randrange(25000,50000,1000),randrange(1000,25000,1000))
tmpAmt = randrange(1,limit,100)
row.extend([random.choice(Transaction_Type_Credits),random.choice(Country),limit,tmpAmt])
writer.writerow(row)
'Thriller record': (8, 20),
'Harry Potter book': (5, 35),
'iPhone': (400, 900),
'Rubik’s Cube': (15, 19),
'banana': (10, 10), # how much is a banana, 10 dollars?
}
salesdata = []
for i in range(2):
for i in range(130045):
sel = random.sample(humans, 1)[0]
product = random.sample(products.keys(), 1)[0]
units = random.randint(1, 50) if product != 'Corolla' else random.randint(1, 10)
unitprice = round(random.uniform(products[product][0], products[product][1]), 2)
salesdata.append(sel + [
str(gen_data.create_date(past=True, max_years_future=1, max_years_past=3)),
product,
units,
unitprice,
])
try:
del products['Corolla']
del products['Thriller record']
except:
pass
df = pd.DataFrame(salesdata)
columns = ['name', 'birthday', 'customer', 'orderdate', 'product', 'units', 'unitprice']
df.columns = columns
'6733 Trusts, Except Educational, Religious, and Charitable',\
'8999 Services, NEC',\
]
Stock_Market_Listing=['Australian Stock Exchange',\
'Brussels Stock Exchange',\
'Montreal Stock Exchange',\
'Not Found','Not Found','Not Found','Not Found','Not Found','Not Found','Not Found','Not Found','Not Found','Not Found','Not Found','Not Found','Not Found','Not Found','Not Found','Not Found','Not Found','Not Found','Not Found','Not Found','Not Found','Not Found','Not Found','Not Found','Not Found','Not Found','Not Found','Not Found','Not Found','Not Found',\
'Tiers 1 and 2 of the TSX Venture Exchange (also known as Tiers 1 and 2 of the Canadian Venture Exchange)',\
'Toronto Stock Exchange',\
]
Third_Party_Payment_Processor=Embassy_Consulate
Transacting_Provider=Embassy_Consulate
fake = Faker()
gen_data.create_date(past=True)
with open('large.csv','r') as csvinput:
with open('large_extended.csv','w') as csvoutput:
writer=csv.writer(csvoutput, delimiter=',',lineterminator='\n',)
reader = csv.reader(csvinput)
row = next(reader)
row.append('RELATED_ACCT')
row.append('RELATED_TYPE')
row.append('PARTY_TYPE')
row.append('PARTY_RELATION')
row.append('PARTY_STARTDATE')
#Creates Current Address
zip=random.choice(zips.zip)
addr=geo_data.create_city_state_zip[zip]
#Creates Previous address
zip2=random.choice(zips.zip)
addr2=geo_data.create_city_state_zip[zip2]
#Add additional data elements to current csv row
lrg_cash_ex=random.choice(Yes_No)
#Condition for SARs and Demarketed Clients
if(Clsd=='Yes'):
#1% of closed accounts are demarketed but never had a SAR filed
if (max((randrange(0,101,1)-99),0)==1 and SAR=='No'):
demarket='Yes'
dem_date=gen_data.create_date(past=True)
if (max((randrange(0,11,1)-9),0)==1 and demarket=='No'):
#10% of closed accounts have SARs
SAR='Yes'
#90% of closed accounts with SARs are demarketed
if(max((randrange(0,11,1)-9),0)==0):
demarket='Yes'
dem_date=gen_data.create_date(past=True)
if (max((randrange(0,101,1)-99),0)==1):
PEP='Yes'
row.extend([addr[0],addr[1],zip,'US',addr2[0],addr2[1],zip2,'US',gen_data.create_birthday(min_age=2, max_age=85),PEP,SAR,Clsd])
#Start Generating related accounts from account list once 10,000 accounts are generated
if i > 10000:
rel = int(random.choice(acct_list))*max((randrange(0,10001,1)-9999),0)
'Harry Potter book': (5, 35),
'iPhone': (400, 900),
'Rubik’s Cube': (15, 19),
'banana': (10, 10), # how much is a banana, 10 dollars?
}
salesdata = []
for i in range(2345):
sel = random.sample(humans, 1)[0]
units = random.randint(1, 50)
product = random.sample(products.keys(), 1)[0]
unitprice = round(
random.uniform(products[product][0], products[product][1]), 2)
salesdata.append(sel + [
str(
gen_data.create_date(
past=True, max_years_future=0, max_years_past=1)),
product,
units,
unitprice,
])
df = pd.DataFrame(salesdata)
columns = [
'name', 'birthday', 'customer', 'orderdate', 'product', 'units',
'unitprice'
]
df.columns = columns
# df.to_json('blooth_sales_data_".json', orient='records')
df.to_json('blooth_sales_data_2.json',
orient='records',
with open('CreditCard_Transaction_Red_NonAccountHolders.csv', 'w') as f1:
writer = csv.writer(
f1,
delimiter=',',
lineterminator='\n',
)
writer.writerow(['rownum'] +['Account_Number'] + ['Merchant_Name']+['Merchant_Category_Code']+['Merchant_Category_Desc'] +\
['Post_Date'] + ['Transaction_Date'] + ['Transaction_Type'] +['Merchant_Country']+['Credit_Limit']+['Amount'])
for i in range(10):
dt = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
#generate external account that is not in ref acct file
acct = randrange(100000, 100000000, 1)
row = [str(i) + '_' + dt] + [acct] + [gen_data.create_company_name()]
cat = random.choice(Merchant_Category)
row.append(cat)
row.append(All_Merchant_Cat[cat])
date1 = gen_data.create_date(past=True)
date2 = date1 - timedelta(days=1)
row.append(date1)
row.append(date2)
#7)Set customer credit limit - skew to clients with $1000-$25000 and 10% with $25K - $50K
limit = max(
max((randrange(1, 101, 1) - 99), 0) *
randrange(25000, 50000, 1000), randrange(1000, 25000, 1000))
tmpAmt = randrange(1, limit, 100)
row.extend([
random.choice(Transaction_Type),
random.choice(Country), limit, tmpAmt
])
writer.writerow(row)
def createCusts(N):
#List for client whose net worth is over $500K
HighNetWorth = ['Yes'] + ['No'] * 30
#List for type of account
Related_Type = ['Primary','Secondary','Joint']
#List for how the account was opened
Party_Type = ['Person','Non-Person']
#List for a BMO customer
Party_Relation = ['Customer','Non-Customer']
#List for random Yes/No Flag
Yes_No = ['Yes'] + ['No'] * 12
#List for random Yes/No Consent
Yes_No_Consent = ['Yes'] + ['No'] * 4
#List for equal Yes/No Flag
Yes_No_50 = ['Yes','No']
#List for official language
Official_Lang = ['English'] * 3 + ['French']
#List for method of communication
Preffered_Channel = ['Direct Mail','Telemarketing','Email','SMS']
#List for status of customer
#Customer_Status = ['Prospect','Inactive Customer','Past Customer'] + ['Active Customer'] * 56
#List for LOB Segment Type
Seg_Model_Type = ['LOB Specific','Profitability','Geographical','Behavioral','Risk Tolerance']
#List for Model ID
Model_ID = ['01','02','03','04','05']
#List for Model Name
Seg_Model_Name = ['IRRI', 'CRS Risk Score','Geo Risk','Financial Behavior Risk','CM Risk']
#List for Model Score
Seg_Model_Score = ['200','300','400','100','500']
#List for Model Group
Seg_Model_Group = ['Group 1'] * 2 + ['Group 2','Group 3','Group 4']
#List for Model Description
Seg_Model_Description = ['High Risk Tier','Mid Risk Tier','Low Risk Tier','Vertical Risk','Geographical Risk']
#List for random Arms Dealer flag
Arms_Manufacturer=['Yes'] + ['No'] * 2 + [''] * 392
#List for random auction flag
Auction=['Yes'] + ['No'] * 2 + [''] * 392
#List for random Cash Intensive flag
CashIntensive_Business=['Yes'] + ['No'] * 2 + [''] * 392
#List for random Casino?Gaming flag
Casino_Gambling=['Yes'] + ['No'] * 2 + [''] * 392
#List for random Client Onboarding flag
Channel_Onboarding=['E-mail','In Person','In person - In Branch/Bank Office','In person - Offsite/Client Location','Mail','Online','Phone','Request for Proposal (RFP)'] + ['Not Applicable'] * 10
#List for random Transaction flag
Channel_Ongoing_Transactions=['ATM','E-mail','Fax','Mail','Not Applicable','OTC Communication System','Phone'] + ['Online'] * 4 + ['In Person'] * 31
#List for random HI_Vehicle flag
Complex_HI_Vehicle=['Yes'] + ['No'] * 2 + [''] * 392
#List for random Metals flag
Dealer_Precious_Metal=['Yes'] + ['No'] * 2 + [''] * 392
#List for random Arms Dealer flag
Digital_PM_Operator=['Yes'] + ['No'] * 2 + [''] * 392
#List for random Embassy flag
Embassy_Consulate=['Yes'] + ['No'] * 2 + [''] * 392
#Sets variable to Embassy flag
Exchange_Currency=Embassy_Consulate
#Sets variable to Embassy flag
Foreign_Financial_Institution=Embassy_Consulate
#Sets variable to Embassy flag
Foreign_Government=Embassy_Consulate
#Sets variable to Embassy flag
Foreign_NonBank_Financial_Institution=Embassy_Consulate
#Sets variable to Embassy flag
Internet_Gambling=Embassy_Consulate
#Sets variable to Embassy flag
Medical_Marijuana_Dispensary=Embassy_Consulate
#Sets variable to Embassy flag
Money_Service_Business=Embassy_Consulate
#Sets variable to Embassy flag
NonRegulated_Financial_Institution=Embassy_Consulate
#Sets variable to Embassy flag
Not_Profit=Embassy_Consulate
#List for random occupation
Occupation=['11-1011 Chief Executives',\
'11-3011 Administrative Services Managers',\
'11-3031 Financial Managers',\
'11-3061 Purchasing Managers',\
'13-1011 Agents and Business Managers of Artists Performers and Athletes',\
'13-1031 Claims Adjusters Examiners, and Investigators',\
'13-1199 Business Operations Specialists, All Other',\
'13-2099 Financial Specialists All Other',\
'17-1011 Architects Except Landscape and Naval',\
'23-1011 Lawyers',\
'23-1023 Judges, Magistrate Judges and Magistrates',\
'25-2012 Kindergarten Teachers Except Special Education',\
'25-2021 Elementary School Teachers Except Special Education',\
'29-1041 Optometrists',\
'29-2054 Respiratory Therapy Technicians',\
'33-2011 Firefighters',\
'37-1012 First-Line Supervisors of Landscaping Lawn Service and Groundskeeping Workers',\
'39-1011 Gaming Supervisors',\
'39-2011 Animal Trainers',\
'41-1011 First-Line Supervisors of Retail Sales Workers',\
'41-1012 First-Line Supervisors of Non-Retail Sales Workers',\
'41-2011 Cashiers',\
'41-2031 Retail Salespersons',\
'43-3021 Billing and Posting Clerks',\
'45-1011 First-Line Supervisors of Farming, Fishing, and Forestry Workers',\
'49-2011 Computer Automated Teller and Office Machine Repairers',\
'53-3021 Bus Drivers Transit and Intercity',\
'53-4031 Railroad Conductors and Yardmasters',\
'55-1011 Air Crew Officers',\
'55-1012 Aircraft Launch and Recovery Officers',\
'55-1013 Armored Assault Vehicle Officers',\
]
#Sets variable to Embassy flag
Privately_ATM_Operator=Embassy_Consulate
#List for random products
Products=['Certificate of Deposit',\
'Checking Account',\
'Credit Card',\
'Custodial and Investment Agency - Institutional',\
'Custodial and Investment Agency - Personal',\
'Custodial/Trust Outsourcing Services (BTOS)',\
'Custody Accounts (PTIM)',\
'Custody Accounts (RSTC)',\
'DTF (BHFA)',\
'Investment Agency - Personal',\
'Investment Management Account (PTIM)',\
'Lease',\
'Loan / Letter of Credit',\
'Money Market',\
'Mortgage / Bond / Debentures',\
'None',\
'Savings Account',\
'Trust Administration - Irrevocable and Revocable (PTIM)',\
'Trust Administration - Irrevocable and Revocable Trusts (BDTC)',\
] + ['Nondeposit Investment Products'] * 14 + ['Investment Agency - Institutional'] * 5
#Sets variable to Embassy flag
Sales_Used_Vehicles=Embassy_Consulate
#Dictionary for random Services
Services=['Benefit Payment Services',\
'Domestic Wires and Direct Deposit / ACH',\
'Family Office Services (FOS)',\
'Fiduciary Services',\
'International Wires and IAT',\
'Investment Advisory Services (IAS)',\
'Investment Services',\
'None',\
'Online / Mobile Banking',\
'Payroll',\
'Short Term Cash Management',\
'Trust Services',\
'Trustee Services',\
'Vault Cash Services',\
] + ['Financial Planning'] * 6 + ['Retirement Plans'] * 19
#Dictionary for random SIC_Code
SIC_Code=['6021 National Commercial Banks',\
'6211 Security Brokers Dealers and Flotation Companies',\
'6282 Investment Advice',\
'6311 Life Insurance',\
'6733 Trusts Except Educational Religious and Charitable',\
'8999 Services NEC',\
] + ['6722 Management Investment Offices Open-End'] * 12
#Dictionary for random Market Listing
Stock_Market_Listing=['Australian Stock Exchange',\
'Brussels Stock Exchange',\
'Montreal Stock Exchange',\
'Tiers 1 and 2 of the TSX Venture Exchange (also known as Tiers 1 and 2 of the Canadian Venture Exchange)',\
'Toronto Stock Exchange',\
] + ['Not Found'] * 30
#Sets variable to Embassy flag
Third_Party_Payment_Processor=Embassy_Consulate
#Sets variable to Embassy flag
Transacting_Provider=Embassy_Consulate
#Dictionary for random Low Net Worth
LowNet=[1,2] + [0] * 5
#Dictionary for Consumer vs Business
Acct_Type = ['B'] + ['C'] * 5
#Dictionary for random number of credits cards per account
Number_CC = [1] * 7 + [2] * 11 + [3] * 3 + [4]
#Dictionary for Account list set to blank
acct_list=[]
#Dictionary for CreditCard list set to blank
CC_list = []
#Dictionary for random Wolfsberg scenario
Use_Case = [1,4,7,10,13,16,19,22,25,28,31,34,39] * 4 + [2,5,8,11,14,17,20,23,26,29,32,35,38] * 7 + [3,6,9,12,15,18,21,24,27,30,33,36] * 65 + [37] * 73 + [40,41] * 2
refrating = ['1','1','1','2','3','4','2','4','5','5','5','5','5','5','5','5','5','5','5','5']
fake = Faker()
global liSSNMaster
start=10786147
acct_list=[]
liCSV = []
for i in xrange(N):
#Initiate High Risk Flags
#Politically Exposed Person
PEP='No'
#Customer with a Suspicous Activity Report
SAR='No'
#Customer with a closed account
Clsd='No'
#High risk customer flag
high_risk='No'
#High Risk Rating
hr_rating=''
#Customer that was demarketed by the bank
demarket='No'
dem_date=''
#generate closed acct flag
if (max((randrange(0,98,1)-96),0)==1):
Clsd='Yes'
#Random choice for number of credit card users per account number
No_CCs = random.choice(Number_CC)
#Generate account number
acct=start+1+randrange(1,10,1)
start=acct
#Randomly generate customer name + middle name in tmp
name = fake.name()
tmp=gen_data.create_name()
#Adds account number to account dictionary
acct_list.extend([acct])
#Creates a new row and adds data elements
row = [i]+[acct]+[random.choice(Acct_Type)]+[No_CCs]+[name]+[tmp[0]]+[liSSNMaster[i]]
#Dictionary for names list set to blank
names=[]
#Dictionary for Social Security Number list set to blank
ssn=[]
#Middle Name to reduce name dups
mdl=[]
for j in range(No_CCs-1):
names.insert(j,fake.name())
tmp2=gen_data.create_name()
mdl.insert(j,tmp2[0])
##Pull from SSN Master list
randInt = randrange(1,len(liSSNMaster),1)
if randInt != i:
ssn.insert(j,liSSNMaster[randInt])
else:
ssn.insert(j,liSSNMaster[randInt - 1])
#Name and SSN is set to blank if less than 4 customers on an account
for k in range(4-No_CCs):
names.insert(No_CCs+k,'')
ssn.insert(No_CCs+k,'')
mdl.insert(No_CCs,'')
#Sets CC_NO to a random credit card number
CC_NO=gen_data.cc_number()
#Extract CC_Number from the tuple returned by CC_Number then scramble to ensure uniqueness...Tuple contains CC Number and Type
CC_TRANS=CC_NO[1][0]
dt = str(datetime.now())
clean=re.sub('\W','',dt)
printCC=str(CC_TRANS[-4:])+str(clean[-12:-3])+str(randrange(1111,9999,randrange(1,10,1)))
#Add data elements to current csv row
row.extend([names[0],mdl[0],ssn[0],names[1],mdl[1],ssn[1],names[2],mdl[2],ssn[2],printCC,CC_NO[0],gen_data.create_company_name()+' '+tmp[1],\
gen_data.create_email(),gen_data.create_job_title()])
#Create Current Address
zip=random.choice(zips.zip)
addr=geo_data.create_city_state_zip[zip]
#Create Previous address
zip2=random.choice(zips.zip)
addr2=geo_data.create_city_state_zip[zip2]
#Add additional data elements to current csv row
lrg_cash_ex=random.choice(Yes_No)
#Condition for SARs and Demarketed Clients
if(Clsd=='Yes'):
#1% of closed accounts are demarketed but never had a SAR filed
if (max((randrange(0,101,1)-99),0)==1 and SAR=='No'):
demarket='Yes'
dem_date=gen_data.create_date(past=True)
if (max((randrange(0,11,1)-9),0)==1 and demarket=='No'):
#10% of closed accounts have SARs
SAR='Yes'
#90% of closed accounts with SARs are demarketed
if(max((randrange(0,11,1)-9),0)==0):
demarket='Yes'
dem_date=gen_data.create_date(past=True)
if (max((randrange(0,101,1)-99),0)==1):
PEP='Yes'
row.extend([addr[0],addr[1],zip,'US',addr2[0],addr2[1],zip2,'US',gen_data.create_birthday(min_age=2, max_age=85),PEP,SAR,Clsd])
#Start Generating related accounts from account list once 10,000 accounts are generated - to avoid duplicating accounts in the beginning
if i > 10000:
rel = int(random.choice(acct_list))*max((randrange(0,10001,1)-9999),0)
if rel <> 0:
row.append(rel)
row.append(random.choice(Related_Type))
else:
row.append('')
row.append('')
else:
row.append('')
row.append('')
#Randomly generates account start date
party_start=gen_data.create_date(past=True)
#Randomly selects consent option for sharing info
Consent_Share = random.choice(Yes_No_Consent)
#Add additional data elements to current csv row
row.extend([random.choice(Party_Type),random.choice(Party_Relation),party_start,gen_data.create_date(past=True),\
lrg_cash_ex,demarket,dem_date,randrange(0,100,1),random.choice(Official_Lang)])
#Add data element preferred methond of contact for yes to share info...if not then blank to current row
if Consent_Share == 'Yes':
row.extend(['Yes',random.choice(Preffered_Channel)])
else:
row.extend(['No',''])
row.extend([zip,randrange(0,5,1)])
#Generate Segment ID then add additional Segment data based on the selection to the current csv row
Segment_ID = randrange(0,5,1)%5
if Segment_ID == 0:
row.extend([Model_ID[0],Seg_Model_Type[0],Seg_Model_Name[0],Seg_Model_Group[0],Seg_Model_Description[0],Seg_Model_Score[0]])
if Segment_ID == 1:
row.extend([Model_ID[1],Seg_Model_Type[1],Seg_Model_Name[1],Seg_Model_Group[1],Seg_Model_Description[1],Seg_Model_Score[1]])
if Segment_ID == 2:
row.extend([Model_ID[2],Seg_Model_Type[2],Seg_Model_Name[2],Seg_Model_Group[2],Seg_Model_Description[2],Seg_Model_Score[2]])
if Segment_ID == 3:
row.extend([Model_ID[3],Seg_Model_Type[3],Seg_Model_Name[3],Seg_Model_Group[3],Seg_Model_Description[3],Seg_Model_Score[3]])
if Segment_ID == 4:
row.extend([Model_ID[4],Seg_Model_Type[4],Seg_Model_Name[4],Seg_Model_Group[4],Seg_Model_Description[4],Seg_Model_Score[4]])
#Add additional data elements to current csv row
hr0=random.choice(Arms_Manufacturer)
hr01=random.choice(Auction)
hr02=random.choice(CashIntensive_Business)
hr03=random.choice(Casino_Gambling)
hr04=random.choice(Channel_Onboarding)
hr05=random.choice(Channel_Ongoing_Transactions)
row.extend([hr0,hr01,hr02,hr03,hr04,hr05])
#Randomly select whether customer has a High Net Worth
HighNetWorthFlag = random.choice(HighNetWorth)
#Randomly Generate customer net worth based on the above flag
if HighNetWorthFlag == 'Yes':
row.append(max(max((randrange(0,101,1)-99),0)*randrange(1000000,25000000,1),randrange(1000000,5000000,1)))
else:
flag=random.choice(LowNet)
if flag==0:
row.append(randrange(-250000,600000,1))
else:
if flag==1:
row.append(randrange(149000,151000,1))
else:
row.append(randrange(40000,50000,1))
#Add data elements to current csv row
hr1=random.choice(Complex_HI_Vehicle)
hr2=random.choice(Dealer_Precious_Metal)
hr3=random.choice(Digital_PM_Operator)
hr4=random.choice(Embassy_Consulate)
hr5=random.choice(Exchange_Currency)
hr6=random.choice(Foreign_Financial_Institution)
hr7=random.choice(Foreign_Government)
hr8=random.choice(Foreign_NonBank_Financial_Institution)
hr9=random.choice(Internet_Gambling)
hr10=random.choice(Medical_Marijuana_Dispensary)
hr11=random.choice(Money_Service_Business)
hr12=random.choice(NAICS.NAICS_Code)
hr13=random.choice(NonRegulated_Financial_Institution)
hr14=random.choice(Not_Profit)
#hr15=random.choice(Occupation) - added before through gen_data
hr16=random.choice(Privately_ATM_Operator)
hr17=random.choice(Products)
hr18=random.choice(Sales_Used_Vehicles)
hr19=random.choice(Services)
hr20=random.choice(SIC_Code)
hr21=random.choice(Stock_Market_Listing)
hr22=random.choice(Third_Party_Payment_Processor)
hr23=random.choice(Transacting_Provider)
if(PEP=='Yes' or SAR=='Yes' or lrg_cash_ex=='Yes' or demarket=='Yes' or hr0=='Yes'
or hr01=='Yes' or hr02=='Yes' or hr03=='Yes' or hr1=='Yes' or hr2=='Yes' or hr3=='Yes' or hr4=='Yes' or
hr5=='Yes' or hr6=='Yes' or hr7=='Yes' or hr8=='Yes' or hr9=='Yes' or hr10=='Yes' or hr11=='Yes' or hr13=='Yes' or hr14=='Yes' or
hr16=='Yes' or hr17=='Yes' or hr18=='Yes' or hr22=='Yes' or hr23=='Yes' or HighNetWorthFlag=='Yes'):
high_risk='Yes'
hr_rating=random.choice(refrating)
if(SAR=='No' and high_risk=='No'):
if(max((randrange(0,101,1)-99),0)==1):
high_risk='Yes'
hr_rating=random.choice(refrating)
if(PEP=='No' and high_risk=='No'):
if(max((randrange(0,101,1)-99),0)==1):
high_risk='Yes'
hr_rating=random.choice(refrating)
if(high_risk=='No'):
if(max((randrange(0,101,1)-99),0)==1):
high_risk='Yes'
hr_rating=random.choice(refrating)
row.extend([hr1,hr2,hr3,hr4,hr5,hr6,hr7,hr8,hr9,hr10,hr11,hr12,hr13,hr14,hr16,hr17,hr18,hr19,hr20,hr21,hr22,hr23,
HighNetWorthFlag,high_risk,hr_rating,random.choice(Use_Case)])
liCSV.append(row)
return liCSV
def __init__(self, i, acct, liSSNMaster, acct_list):
self.ROWNUM = i
self.ACCOUNTID = acct
self.SSN = liSSNMaster[i]
self.ACCT_TYPE = choice(Acct_Type)
self.NUM_CCS = choice(Number_CC)
self.NAME = fake.name()
self.CUSTEMAIL = gen_data.create_email()
self.OCCUPATION = gen_data.create_job_title()
self.COUNTRY = 'US'
self.PREVIOUS_COUNTRY = 'US'
self.DOB = gen_data.create_birthday(min_age=2, max_age=85)
self.PARTY_ENDDATE = gen_data.create_date(past=True)
self.CONSENT_SHARING = choice(Yes_No_Consent)
self.LARGE_CASH_EXEMPT = choice(Yes_No)
self.PARTY_TYPE = choice(Party_Type)
self.PARTY_RELATION = choice(Party_Relation)
self.PROB_DEFAULT_RISKR = randrange(0, 100, 1)
self.OFFICIAL_LANG_PREF = choice(Official_Lang)
self.DEPENDANTS_COUNT = randrange(0, 5, 1)
self.USE_CASE_SCENARIO = choice(Use_Case)
self.CLOSEDACCOUNT = choice(Clsd_flag)
self.HIGH_NET_WORTH = choice(HighNetWorth)
self.PARTY_STARTDATE = gen_data.create_date(past=True)
self.ARMS_MANUFACTURER = choice(Yes_No_Cust_Flag)
self.AUCTION = choice(Yes_No_Cust_Flag)
self.CASHINTENSIVE_BUSINESS = choice(Yes_No_Cust_Flag)
self.CASINO_GAMBLING = choice(Yes_No_Cust_Flag)
self.CHANNEL_ONBOARDING = choice(Channel_Onboarding)
self.CHANNEL_ONGOING_TRANSACTIONS = choice(
Channel_Ongoing_Transactions)
self.COMPLEX_HI_VEHICLE = choice(Yes_No_Cust_Flag)
self.DEALER_PRECIOUS_METAL = choice(Yes_No_Cust_Flag)
self.DIGITAL_PM_OPERATOR = choice(Yes_No_Cust_Flag)
self.EMBASSY_CONSULATE = choice(Yes_No_Cust_Flag)
self.EXCHANGE_CURRENCY = choice(Yes_No_Cust_Flag)
self.FOREIGN_FINANCIAL_INSTITUTION = choice(Yes_No_Cust_Flag)
self.FOREIGN_GOVERNMENT = choice(Yes_No_Cust_Flag)
self.FOREIGN_NONBANK_FINANCIAL_INSTITUTION = choice(Yes_No_Cust_Flag)
self.INTERNET_GAMBLING = choice(Yes_No_Cust_Flag)
self.MEDICAL_MARIJUANA_DISPENSARY = choice(Yes_No_Cust_Flag)
self.MONEY_SERVICE_BUSINESS = choice(Yes_No_Cust_Flag)
self.NAICS_CODE = choice(NAICS.NAICS_Code)
self.NONREGULATED_FINANCIAL_INSTITUTION = choice(Yes_No_Cust_Flag)
self.NOT_PROFIT = choice(Yes_No_Cust_Flag)
self.PRIVATELY_ATM_OPERATOR = choice(Yes_No_Cust_Flag)
self.PRODUCTS = choice(Products)
self.SALES_USED_VEHICLES = choice(Yes_No_Cust_Flag)
self.SERVICES = choice(Services)
self.SIC_CODE = choice(SIC_Code)
self.STOCK_MARKET_LISTING = choice(Stock_Market_Listing)
self.THIRD_PARTY_PAYMENT_PROCESSOR = choice(Yes_No_Cust_Flag)
self.TRANSACTING_PROVIDER = choice(Yes_No_Cust_Flag)
self.ZIP = choice(zips.zip)
self.PREVIOUS_ZIP = choice(zips.zip)
addr = geo_data.create_city_state_zip[self.ZIP]
addr2 = geo_data.create_city_state_zip[self.PREVIOUS_ZIP]
self.CITY = addr[0]
self.STATE = addr[1]
self.PREVIOUS_CITY = addr2[0]
self.PREVIOUS_STATE = addr2[1]
self.PRIMARY_BRANCH_NO = self.ZIP
tmp = gen_data.create_name()
self.M_NAME = tmp[0]
self.EMPLOYER = gen_data.create_company_name() + ' ' + tmp[1]
No_CCs = choice(Number_CC)
#Dictionary for names list set to blank
names = []
#Dictionary for Social Security Number list set to blank
ssn = []
#Middle Name to reduce name dups
mdl = []
#Generates Name and SSN for Credit Users
for j in range(4):
if No_CCs > j:
names.insert(j, fake.name())
tmp2 = gen_data.create_name()
mdl.insert(j, tmp2[0])
randInt = randrange(1, len(liSSNMaster), 1)
if randInt != i:
ssn.insert(j, liSSNMaster[randInt])
else:
ssn.insert(j, liSSNMaster[randInt - 1])
#Name and SSN is set to blank if less than 4 customers on an account
else:
names.insert(No_CCs + j, '')
ssn.insert(No_CCs + j, '')
mdl.insert(No_CCs + j, '')
self.AUTHORIZED_NAME2 = names[0]
self.M_NAME2 = mdl[0]
self.SSN2 = ssn[0]
self.AUTHORIZED_NAME3 = names[1]
self.M_NAME3 = mdl[1]
self.SSN3 = ssn[1]
self.AUTHORIZED_NAME4 = names[2]
self.M_NAME4 = mdl[2]
self.SSN4 = ssn[2]
#Sets CC_NO to a random credit card number
CC_NO = gen_data.create_cc_number()
CC_TRANS = CC_NO[1][0]
dt = str(datetime.now())
clean = re.sub('\W', '', dt)
self.CREDITCARDNUMBER = str(CC_TRANS[-4:]) + str(clean[-12:-3]) + str(
randrange(1111, 9999, randrange(1, 10, 1)))
self.CREDITCARDTYPE = CC_NO[0]
self.RELATED_ACCT = ''
self.RELATED_TYPE = ''
if i > 10000:
rel = int(choice(acct_list)) * max(
(randrange(0, 10001, 1) - 9999), 0)
if rel <> 0:
self.RELATED_ACCT = rel
self.RELATED_TYPE = choice(Related_Type)
self.PREFERRED_CHANNEL = ''
if self.CONSENT_SHARING == 'Yes':
self.PREFERRED_CHANNEL = choice(Prefered_Channel)
## #Generates Segment ID then adds additional Segment data based on the selection to the current csv row
Segment_ID = randrange(0, 5, 1)
if Segment_ID == 0:
self.SEG_MODEL_ID = '01'
self.SEG_MODEL_TYPE = 'LOB Specific'
self.SEG_MODEL_NAME = 'IRRI'
self.SEG_MODEL_GROUP = 'Group 1'
self.SEG_M_GRP_DESC = 'High Risk Tier'
self.SEG_MODEL_SCORE = '200'
if Segment_ID == 1:
self.SEG_MODEL_ID = '02'
self.SEG_MODEL_TYPE = 'Profitability'
self.SEG_MODEL_NAME = 'CRS Risk Score'
self.SEG_MODEL_GROUP = 'Group 1'
self.SEG_M_GRP_DESC = 'Mid Risk Tier'
self.SEG_MODEL_SCORE = '300'
if Segment_ID == 2:
self.SEG_MODEL_ID = '03'
self.SEG_MODEL_TYPE = 'Geographical'
self.SEG_MODEL_NAME = 'Geo Risk'
self.SEG_MODEL_GROUP = 'Group 2'
self.SEG_M_GRP_DESC = 'Low Risk Tier'
self.SEG_MODEL_SCORE = '400'
if Segment_ID == 3:
self.SEG_MODEL_ID = '04'
self.SEG_MODEL_TYPE = 'Behavioral'
self.SEG_MODEL_NAME = 'Financial Behavior Risk'
self.SEG_MODEL_GROUP = 'Group 3'
self.SEG_M_GRP_DESC = 'Vertical Risk'
self.SEG_MODEL_SCORE = '100'
if Segment_ID == 4:
self.SEG_MODEL_ID = '05'
self.SEG_MODEL_TYPE = 'Risk Tolerance'
self.SEG_MODEL_NAME = 'CM Risk'
self.SEG_MODEL_GROUP = 'Group 4'
self.SEG_M_GRP_DESC = 'Geographical Risk'
self.SEG_MODEL_SCORE = '500'
self.CLIENT_NET_WORTH = ''
if self.HIGH_NET_WORTH == 'Yes':
self.CLIENT_NET_WORTH = max(
max((randrange(0, 101, 1) - 99), 0) *
randrange(5000000, 25000000, 1),
randrange(1000000, 5000000, 1))
else:
flag = choice(LowNet)
if flag == 0:
self.CLIENT_NET_WORTH = randrange(-250000, 600000, 1)
else:
if flag == 1:
self.CLIENT_NET_WORTH = randrange(149000, 151000, 1)
else:
self.CLIENT_NET_WORTH = randrange(40000, 50000, 1)
#Politically Exposed Person
self.PEP = 'No'
#1% of accounts are PEP
if (max((randrange(0, 101, 1) - 99), 0) == 1):
self.PEP = 'Yes'
#Customer that was demarketed by the bank
self.DEMARKET_FLAG = 'No'
self.DEMARKET_DATE = ''
#Customer with a Suspicous Activity Report
self.SAR = 'No'
#Customer with a closed account
#generate closed acct flag
#Condition for SARs and Demarketed Clients
if (self.CLOSEDACCOUNT == 'Yes'):
#1% of closed accounts are demarketed but never had a SAR filed
if (max((randrange(0, 101, 1) - 99), 0) == 1):
self.DEMARKET_FLAG = 'Yes'
self.DEMARKET_DATE = gen_data.create_date(past=True)
if (self.DEMARKET_FLAG == 'No' and max(
(randrange(0, 11, 1) - 9), 0) == 1):
#10% of closed accounts have SARs
self.SAR = 'Yes'
#90% of closed accounts with SARs are demarketed
if (max((randrange(0, 11, 1) - 9), 0) == 0):
self.DEMARKET_FLAG = 'Yes'
self.DEMARKET_DATE = gen_data.create_date(past=True)
self.HIGH_RISK = 'No'
self.RISK_RATING = ''
if (self.PEP == 'Yes' or self.SAR == 'Yes'
or self.LARGE_CASH_EXEMPT == 'Yes'
or self.DEMARKET_FLAG == 'Yes'
or self.ARMS_MANUFACTURER == 'Yes' or self.AUCTION == 'Yes'
or self.CASHINTENSIVE_BUSINESS == 'Yes'
or self.CASINO_GAMBLING == 'Yes'
or self.COMPLEX_HI_VEHICLE == 'Yes'
or self.DEALER_PRECIOUS_METAL == 'Yes'
or self.DIGITAL_PM_OPERATOR == 'Yes'
or self.EMBASSY_CONSULATE == 'Yes'
or self.EXCHANGE_CURRENCY == 'Yes'
or self.FOREIGN_FINANCIAL_INSTITUTION == 'Yes'
or self.FOREIGN_GOVERNMENT == 'Yes'
or self.FOREIGN_NONBANK_FINANCIAL_INSTITUTION == 'Yes'
or self.INTERNET_GAMBLING == 'Yes'
or self.MEDICAL_MARIJUANA_DISPENSARY == 'Yes'
or self.MONEY_SERVICE_BUSINESS == 'Yes'
or self.NONREGULATED_FINANCIAL_INSTITUTION == 'Yes'
or self.NOT_PROFIT == 'Yes'
or self.PRIVATELY_ATM_OPERATOR == 'Yes'
or self.SALES_USED_VEHICLES == 'Yes'
or self.THIRD_PARTY_PAYMENT_PROCESSOR == 'Yes'
or self.TRANSACTING_PROVIDER == 'Yes'
or self.HIGH_NET_WORTH == 'Yes'):
self.HIGH_RISK = 'Yes'
self.RISK_RATING = choice(refrating)
elif (max((randrange(0, 101, 1) - 99), 0) == 1):
self.HIGH_RISK = 'Yes'
self.RISK_RATING = choice(refrating)
def generate_customers():
with get_file('uber_cust.csv', 'w') as f1:
# Writer for CSV...Pipe delimited...Return for a new line
writer = csv.writer(
f1,
delimiter='|',
lineterminator='\n',
)
# Header Row
writer.writerow(
['ROWNUM'] + ['accountNumber'] + ['accountCategory'] + ['accountType'] + ['NUM_CCS'] + ['NAME'] + [
'M_NAME'] + [
'SSN'] + [
'AUTHORIZED_NAME2'] + ['M_NAME2'] + ['SSN2'] + \
['AUTHORIZED_NAME3'] + ['M_NAME3'] + ['SSN3'] + ['AUTHORIZED_NAME4'] + ['M_NAME4'] + ['SSN4'] + [
'CREDITCARDNUMBER'] + ['CREDITCARDTYPE'] + ['EMPLOYER'] + ['CUSTEMAIL'] + \
['OCCUPATION'] + ['CITY'] + ['STATE'] + ['ZIP'] + ['COUNTRY'] + ['PREVIOUS_CITY'] + [
'PREVIOUS_STATE'] + \
['PREVIOUS_ZIP'] + ['PREVIOUS_COUNTRY'] + ['DOB'] + ['politically_exposed_person'] + [
'suspicious_activity_report'] + ['CLOSEDACCOUNT'] + [
'RELATED_ACCT'] + ['RELATED_TYPE'] + ['PARTY_TYPE'] + ['PARTY_RELATION'] + [
'PARTY_STARTDATE'] + ['PARTY_ENDDATE'] + \
['LARGE_CASH_EXEMPT'] + ['DEMARKET_FLAG'] + ['DEMARKET_DATE'] + ['PROB_DEFAULT_RISKR'] + [
'OFFICIAL_LANG_PREF'] + ['CONSENT_SHARING'] + \
['PREFERRED_CHANNEL'] + ['PRIMARY_BRANCH_NO'] + ['DEPENDANTS_COUNT'] + ['SEG_MODEL_ID'] + [
'SEG_MODEL_TYPE'] + \
['SEG_MODEL_NAME'] + ['SEG_MODEL_GROUP'] + ['SEG_M_GRP_DESC'] + ['SEG_MODEL_SCORE'] + [
'ARMS_MANUFACTURER'] + ['AUCTION'] + \
['CASHINTENSIVE_BUSINESS'] + ['CASINO_GAMBLING'] + ['CHANNEL_ONBOARDING'] + [
'CHANNEL_ONGOING_TRANSACTIONS'] + ['CLIENT_NET_WORTH'] + \
['COMPLEX_HI_VEHICLE'] + ['DEALER_PRECIOUS_METAL'] + ['DIGITAL_PM_OPERATOR'] + [
'EMBASSY_CONSULATE'] + ['EXCHANGE_CURRENCY'] + \
['FOREIGN_FINANCIAL_INSTITUTION'] + ['FOREIGN_GOVERNMENT'] + [
'FOREIGN_NONBANK_FINANCIAL_INSTITUTION'] + ['INTERNET_GAMBLING'] + \
['MEDICAL_MARIJUANA_DISPENSARY'] + ['MONEY_SERVICE_BUSINESS'] + ['NAICS_CODE'] + [
'NONREGULATED_FINANCIAL_INSTITUTION'] + \
['NOT_PROFIT'] + ['PRIVATELY_ATM_OPERATOR'] + ['PRODUCTS'] + ['SALES_USED_VEHICLES'] + [
'SERVICES'] + \
['SIC_CODE'] + ['STOCK_MARKET_LISTING'] + ['THIRD_PARTY_PAYMENT_PROCESSOR'] + [
'TRANSACTING_PROVIDER'] + ['HIGH_NET_WORTH'] + ['HIGH_RISK'] + ['RISK_RATING'] + [
'USE_CASE_SCENARIO'])
# Loop for number of accounts to generate
start = 10
acct_list = []
li_ssn_master = list(
set([
''.join(str(random.randint(0, 9)) for _ in xrange(9))
for i in xrange(30)
]))
if len(li_ssn_master) < 30:
li_ssn_master = list(
set([
''.join(str(random.randint(0, 9)) for _ in xrange(9))
for i in xrange(30)
]))
for i in xrange(30):
# Initiate High Risk Flags
politically_exposed_person = 'No'
suspicious_activity_report = 'No'
closed_cust_acct = 'No'
# High risk customer flag
high_risk = 'No'
# High Risk Rating
hr_rating = ''
# Customer that was demarketed by the bank
demarket = 'No'
dem_date = ''
# generate closed acct flag
if max((randrange(0, 98, 1) - 96), 0) == 1:
closed_cust_acct = 'Yes'
# Random number generator for account number
# acct = randrange(100000,100000000,1)
# Random choice for number of credit cards per account number
no_ccs = weighted_options('number_cc')
# while acct_list.count(acct) > 0:
# acct = randrange(100000,100000000,1)
# dt = str(datetime.now())
# acct=str(i)++re.sub('\W','',dt)
acct = start + 1 + randrange(1, 10, 1)
start = acct
name = fake.name()
tmp = gen_data.create_name()
# Adds account number to account dictionary
acct_list.extend([acct])
# Creates a new row and adds data elements
## JS - Main Account Holder SSN as current index in master SSN list
## row = [i]+[acct]+[random.choice(acct_type)]+[No_CCs]+[name]+[tmp[0]]+[(str(randrange(101,1000,1))+str(randrange(10,100,1))+str(randrange(1000,10000,1)))]
row = [i] + [acct] + [weighted_options('acct_type')] + [no_ccs] + [
name
] + [tmp[0]] + [li_ssn_master[i]]
# Dictionary for names list set to blank
names = []
# Dictionary for Social Security Number list set to blank
ssn = []
# Generates Name and SSN for Credit Users
# Middle Name to reduce name dups
mdl = []
for j in range(no_ccs - 1):
names.insert(j, fake.name())
tmp2 = gen_data.create_name()
mdl.insert(j, tmp2[0])
## JS - Pull from SSN Master list
# ssn.insert(j,(str(randrange(101,1000,1))+str(randrange(10,100,1))+str(randrange(1000,10000,1))))
randInt = randrange(1, len(li_ssn_master), 1)
if randInt != i:
ssn.insert(j, li_ssn_master[randInt])
else:
ssn.insert(j, li_ssn_master[randInt - 1])
# Name and SSN is set to blank if less than 4 customers on an account
for k in range(4 - no_ccs):
names.insert(no_ccs + k, '')
ssn.insert(no_ccs + k, '')
mdl.insert(no_ccs, '')
# Sets CC_NO to a random credit card number
CC_NO = gen_data.create_cc_number()
# Extract CC_Number from the tuple returned by CC_Number...Tuple contains CC Number and Type
# while credit_cards.count(CC_NO[1][0]) > 0:
CC_TRANS = CC_NO[1][0]
dt = str(datetime.now())
clean = re.sub('\W', '', dt)
printCC = str(CC_TRANS[-4:]) + str(clean[-12:-3]) + str(
randrange(1111, 9999, randrange(1, 10, 1)))
# str(CC_TRANS[-4:])+str(clean[-12:-2])+str(randrange(1111,9999,randrange(1,10,1)))
# Add CC_Number to control list to prevent duplicates
# Add data elements to current csv row
row.extend([
names[0], mdl[0], ssn[0], names[1], mdl[1], ssn[1], names[2],
mdl[2], ssn[2], printCC, CC_NO[0],
gen_data.create_company_name() + ' ' + tmp[1],
gen_data.create_email(),
gen_data.create_job_title()
])
# Creates Current Address
zip = random.choice(zips.zip)
addr = geo_data.create_city_state_zip[zip]
# Creates Previous address
zip2 = random.choice(zips.zip)
addr2 = geo_data.create_city_state_zip[zip2]
# Add additional data elements to current csv row
lrg_cash_ex = weighted_options('yes_no')
# Condition for SARs and Demarketed Clients
if closed_cust_acct == 'Yes':
# 1% of closed accounts are demarketed but never had a suspicious_activity_report filed
if risk_range() and suspicious_activity_report == 'No':
demarket = 'Yes'
dem_date = gen_data.create_date(past=True)
if risk_range() and demarket == 'No':
# 10% of closed accounts have SARs
suspicious_activity_report = 'Yes'
# 90% of closed accounts with SARs are demarketed
if max((randrange(0, 11, 1) - 9), 0) == 0:
demarket = 'Yes'
dem_date = gen_data.create_date(past=True)
if risk_range():
politically_exposed_person = 'Yes'
row.extend([
addr[0], addr[1], zip, 'US', addr2[0], addr2[1], zip2, 'US',
gen_data.create_birthday(min_age=2, max_age=85),
politically_exposed_person, suspicious_activity_report,
closed_cust_acct
])
# Start Generating related accounts from account list once 10,000 accounts are generated
if i > 10000:
rel = int(random.choice(acct_list)) * max(
(randrange(0, 10001, 1) - 9999), 0)
if rel <> 0:
row.append(rel)
row.append(weighted_options('related_type'))
else:
row.append('')
row.append('')
else:
row.append('')
row.append('')
# Randomly generates account start date
party_start = gen_data.create_date(past=True)
# Randomly selects consent option for sharing info
consent_share = weighted_options('yes_no')
# Add additional data elements to current csv row
row.extend([
weighted_options('party_type'),
weighted_options('party_relation'), party_start,
gen_data.create_date(past=True), lrg_cash_ex, demarket,
dem_date,
randrange(0, 100, 1),
weighted_options('official_lang')
])
# Add data element preferred methond of contact for yes to share info...if not then blank to current row
if consent_share == 'Yes':
row.extend(['Yes', weighted_options('preferred_channel')])
else:
row.extend(['No', ''])
# DO NOT USE CUST STATUS BELOW - NOT INTEGRATED WITH CLOSED STATUS! Add additional data elements to current csv row
row.extend([zip, randrange(0, 5, 1)])
# Generates Segment ID then adds additional Segment data based on the selection to the current csv row
Segment_ID = randrange(0, 5, 1) % 5
if Segment_ID == 0:
row.extend([
MODEL_ID[0], SEG_MODEL_TYPE[0], SEG_MODEL_NAME[0],
SEG_MODEL_GROUP[0], SEG_MODEL_DESCRIPTION[0],
SEG_MODEL_SCORE[0]
])
if Segment_ID == 1:
row.extend([
MODEL_ID[1], SEG_MODEL_TYPE[1], SEG_MODEL_NAME[1],
SEG_MODEL_GROUP[1], SEG_MODEL_DESCRIPTION[1],
SEG_MODEL_SCORE[1]
])
if Segment_ID == 2:
row.extend([
MODEL_ID[2], SEG_MODEL_TYPE[2], SEG_MODEL_NAME[2],
SEG_MODEL_GROUP[2], SEG_MODEL_DESCRIPTION[2],
SEG_MODEL_SCORE[2]
])
if Segment_ID == 3:
row.extend([
MODEL_ID[3], SEG_MODEL_TYPE[3], SEG_MODEL_NAME[3],
SEG_MODEL_GROUP[3], SEG_MODEL_DESCRIPTION[3],
SEG_MODEL_SCORE[3]
])
if Segment_ID == 4:
row.extend([
MODEL_ID[4], SEG_MODEL_TYPE[4], SEG_MODEL_NAME[4],
SEG_MODEL_GROUP[4], SEG_MODEL_DESCRIPTION[4],
SEG_MODEL_SCORE[4]
])
# Add additional data elements to current csv row
arms_manufacturer = weighted_options('arms_manufacturers')
auction = weighted_options('auction')
cash_intensive_business = weighted_options(
'cash_intensive_business')
casino_gambling = weighted_options('casino_gambling')
chan_ob = weighted_options('channel_onboarding')
chan_txn = weighted_options('channel_ongoing_txn')
row.extend([
arms_manufacturer, auction, cash_intensive_business,
casino_gambling, chan_ob, chan_txn
])
# Randomly select whether customer has a High Net Worth
high_net_worth_flag = weighted_options('high_net_worth')
# Randomly Generates customer net worth based on the above flag
if high_net_worth_flag == 'Yes':
row.append(
max(
max((randrange(0, 101, 1) - 99), 0) *
randrange(1000000, 25000000, 1),
randrange(1000000, 5000000, 1)))
else:
flag = weighted_options('low_net')
if flag == 0:
row.append(randrange(-250000, 600000, 1))
else:
if flag == 1:
row.append(randrange(149000, 151000, 1))
else:
row.append(randrange(40000, 50000, 1))
# Add data elements to current csv row
hr1 = weighted_options('complex_hi_vehicle')
hr2 = weighted_options('dealer_precious_metal')
hr3 = weighted_options('digital_pm_operator')
hr4 = weighted_options(EMBASSY_CONSULATE)
hr5 = weighted_options(EXCHANGE_CURRENCY)
hr6 = weighted_options(FOREIGN_FINANCIAL_INSTITUTION)
hr7 = weighted_options(FOREIGN_GOVT)
hr8 = weighted_options(FOREIGN_NONBANK_FINANCIAL_INSTITUTION)
hr9 = weighted_options(INTERNET_GAMBLING)
hr10 = weighted_options(MEDICAL_MARIJUANA_DISPENSARY)
hr11 = weighted_options(MONEY_SERVICE_BUSINESS)
hr12 = random.choice(NAICS.NAICS_Code)
hr13 = weighted_options(NONREGULATED_FINANCIAL_INSTITUTION)
hr14 = weighted_options(NOT_PROFIT)
# hr15=random.choice(occupation)
hr16 = weighted_options(PRIVATE_ATM_OPERATOR)
hr17 = weighted_options('products')
hr18 = weighted_options(SALES_USED_VEHICLES)
hr19 = weighted_options('services')
hr20 = weighted_options('sic_code')
hr21 = weighted_options('stock_market_listing')
hr22 = weighted_options(THIRD_PARTY_PAYMENT_PROCESSOR)
hr23 = weighted_options(TRANSACTING_PROVIDER)
if 'Yes' in (politically_exposed_person,
suspicious_activity_report, lrg_cash_ex, demarket,
arms_manufacturer, auction, cash_intensive_business,
casino_gambling, hr1, hr2, hr3, hr4, hr5, hr6, hr7,
hr8, hr9, hr10, hr11, hr13, hr14, hr16, hr17, hr18,
hr22, hr23, high_net_worth_flag):
high_risk = 'Yes'
hr_rating = weighted_options('refrating')
if suspicious_activity_report == 'No' and high_risk == 'No':
if risk_range():
high_risk = 'Yes'
hr_rating = weighted_options('refrating')
if politically_exposed_person == 'No' and high_risk == 'No':
if risk_range():
high_risk = 'Yes'
hr_rating = weighted_options('refrating')
if high_risk == 'No':
if risk_range():
high_risk = 'Yes'
hr_rating = weighted_options('refrating')
row.extend([
hr1, hr2, hr3, hr4, hr5, hr6, hr7, hr8, hr9, hr10, hr11, hr12,
hr13, hr14, hr16, hr17, hr18, hr19, hr20, hr21, hr22, hr23,
high_net_worth_flag, high_risk, hr_rating,
random.choice(USE_CASE)
])
# End the current row
writer.writerow(row)
def createCusts(N):
#List for client whose net worth is over $500K
HighNetWorth = ['Yes'] + ['No'] * 30
#List for type of account
Related_Type = ['Primary', 'Secondary', 'Joint']
#List for how the account was opened
Party_Type = ['Person', 'Non-Person']
#List for a BMO customer
Party_Relation = ['Customer', 'Non-Customer']
#List for random Yes/No Flag
Yes_No = ['Yes'] + ['No'] * 12
#List for random Yes/No Consent
Yes_No_Consent = ['Yes'] + ['No'] * 4
#List for equal Yes/No Flag
Yes_No_50 = ['Yes', 'No']
#List for official language
Official_Lang = ['English'] * 3 + ['French']
#List for method of communication
Preffered_Channel = ['Direct Mail', 'Telemarketing', 'Email', 'SMS']
#List for status of customer
#Customer_Status = ['Prospect','Inactive Customer','Past Customer'] + ['Active Customer'] * 56
#List for LOB Segment Type
Seg_Model_Type = [
'LOB Specific', 'Profitability', 'Geographical', 'Behavioral',
'Risk Tolerance'
]
#List for Model ID
Model_ID = ['01', '02', '03', '04', '05']
#List for Model Name
Seg_Model_Name = [
'IRRI', 'CRS Risk Score', 'Geo Risk', 'Financial Behavior Risk',
'CM Risk'
]
#List for Model Score
Seg_Model_Score = ['200', '300', '400', '100', '500']
#List for Model Group
Seg_Model_Group = ['Group 1'] * 2 + ['Group 2', 'Group 3', 'Group 4']
#List for Model Description
Seg_Model_Description = [
'High Risk Tier', 'Mid Risk Tier', 'Low Risk Tier', 'Vertical Risk',
'Geographical Risk'
]
#List for random Arms Dealer flag
Arms_Manufacturer = ['Yes'] + ['No'] * 2 + [''] * 392
#List for random auction flag
Auction = ['Yes'] + ['No'] * 2 + [''] * 392
#List for random Cash Intensive flag
CashIntensive_Business = ['Yes'] + ['No'] * 2 + [''] * 392
#List for random Casino?Gaming flag
Casino_Gambling = ['Yes'] + ['No'] * 2 + [''] * 392
#List for random Client Onboarding flag
Channel_Onboarding = [
'E-mail', 'In Person', 'In person - In Branch/Bank Office',
'In person - Offsite/Client Location', 'Mail', 'Online', 'Phone',
'Request for Proposal (RFP)'
] + ['Not Applicable'] * 10
#List for random Transaction flag
Channel_Ongoing_Transactions = [
'ATM', 'E-mail', 'Fax', 'Mail', 'Not Applicable',
'OTC Communication System', 'Phone'
] + ['Online'] * 4 + ['In Person'] * 31
#List for random HI_Vehicle flag
Complex_HI_Vehicle = ['Yes'] + ['No'] * 2 + [''] * 392
#List for random Metals flag
Dealer_Precious_Metal = ['Yes'] + ['No'] * 2 + [''] * 392
#List for random Arms Dealer flag
Digital_PM_Operator = ['Yes'] + ['No'] * 2 + [''] * 392
#List for random Embassy flag
Embassy_Consulate = ['Yes'] + ['No'] * 2 + [''] * 392
#Sets variable to Embassy flag
Exchange_Currency = Embassy_Consulate
#Sets variable to Embassy flag
Foreign_Financial_Institution = Embassy_Consulate
#Sets variable to Embassy flag
Foreign_Government = Embassy_Consulate
#Sets variable to Embassy flag
Foreign_NonBank_Financial_Institution = Embassy_Consulate
#Sets variable to Embassy flag
Internet_Gambling = Embassy_Consulate
#Sets variable to Embassy flag
Medical_Marijuana_Dispensary = Embassy_Consulate
#Sets variable to Embassy flag
Money_Service_Business = Embassy_Consulate
#Sets variable to Embassy flag
NonRegulated_Financial_Institution = Embassy_Consulate
#Sets variable to Embassy flag
Not_Profit = Embassy_Consulate
#List for random occupation
Occupation=['11-1011 Chief Executives',\
'11-3011 Administrative Services Managers',\
'11-3031 Financial Managers',\
'11-3061 Purchasing Managers',\
'13-1011 Agents and Business Managers of Artists Performers and Athletes',\
'13-1031 Claims Adjusters Examiners, and Investigators',\
'13-1199 Business Operations Specialists, All Other',\
'13-2099 Financial Specialists All Other',\
'17-1011 Architects Except Landscape and Naval',\
'23-1011 Lawyers',\
'23-1023 Judges, Magistrate Judges and Magistrates',\
'25-2012 Kindergarten Teachers Except Special Education',\
'25-2021 Elementary School Teachers Except Special Education',\
'29-1041 Optometrists',\
'29-2054 Respiratory Therapy Technicians',\
'33-2011 Firefighters',\
'37-1012 First-Line Supervisors of Landscaping Lawn Service and Groundskeeping Workers',\
'39-1011 Gaming Supervisors',\
'39-2011 Animal Trainers',\
'41-1011 First-Line Supervisors of Retail Sales Workers',\
'41-1012 First-Line Supervisors of Non-Retail Sales Workers',\
'41-2011 Cashiers',\
'41-2031 Retail Salespersons',\
'43-3021 Billing and Posting Clerks',\
'45-1011 First-Line Supervisors of Farming, Fishing, and Forestry Workers',\
'49-2011 Computer Automated Teller and Office Machine Repairers',\
'53-3021 Bus Drivers Transit and Intercity',\
'53-4031 Railroad Conductors and Yardmasters',\
'55-1011 Air Crew Officers',\
'55-1012 Aircraft Launch and Recovery Officers',\
'55-1013 Armored Assault Vehicle Officers',\
]
#Sets variable to Embassy flag
Privately_ATM_Operator = Embassy_Consulate
#List for random products
Products=['Certificate of Deposit',\
'Checking Account',\
'Credit Card',\
'Custodial and Investment Agency - Institutional',\
'Custodial and Investment Agency - Personal',\
'Custodial/Trust Outsourcing Services (BTOS)',\
'Custody Accounts (PTIM)',\
'Custody Accounts (RSTC)',\
'DTF (BHFA)',\
'Investment Agency - Personal',\
'Investment Management Account (PTIM)',\
'Lease',\
'Loan / Letter of Credit',\
'Money Market',\
'Mortgage / Bond / Debentures',\
'None',\
'Savings Account',\
'Trust Administration - Irrevocable and Revocable (PTIM)',\
'Trust Administration - Irrevocable and Revocable Trusts (BDTC)',\
] + ['Nondeposit Investment Products'] * 14 + ['Investment Agency - Institutional'] * 5
#Sets variable to Embassy flag
Sales_Used_Vehicles = Embassy_Consulate
#Dictionary for random Services
Services=['Benefit Payment Services',\
'Domestic Wires and Direct Deposit / ACH',\
'Family Office Services (FOS)',\
'Fiduciary Services',\
'International Wires and IAT',\
'Investment Advisory Services (IAS)',\
'Investment Services',\
'None',\
'Online / Mobile Banking',\
'Payroll',\
'Short Term Cash Management',\
'Trust Services',\
'Trustee Services',\
'Vault Cash Services',\
] + ['Financial Planning'] * 6 + ['Retirement Plans'] * 19
#Dictionary for random SIC_Code
SIC_Code=['6021 National Commercial Banks',\
'6211 Security Brokers Dealers and Flotation Companies',\
'6282 Investment Advice',\
'6311 Life Insurance',\
'6733 Trusts Except Educational Religious and Charitable',\
'8999 Services NEC',\
] + ['6722 Management Investment Offices Open-End'] * 12
#Dictionary for random Market Listing
Stock_Market_Listing=['Australian Stock Exchange',\
'Brussels Stock Exchange',\
'Montreal Stock Exchange',\
'Tiers 1 and 2 of the TSX Venture Exchange (also known as Tiers 1 and 2 of the Canadian Venture Exchange)',\
'Toronto Stock Exchange',\
] + ['Not Found'] * 30
#Sets variable to Embassy flag
Third_Party_Payment_Processor = Embassy_Consulate
#Sets variable to Embassy flag
Transacting_Provider = Embassy_Consulate
#Dictionary for random Low Net Worth
LowNet = [1, 2] + [0] * 5
#Dictionary for Consumer vs Business
Acct_Type = ['B'] + ['C'] * 5
#Dictionary for random number of credits cards per account
Number_CC = [1] * 7 + [2] * 11 + [3] * 3 + [4]
#Dictionary for Account list set to blank
acct_list = []
#Dictionary for CreditCard list set to blank
CC_list = []
#Dictionary for random Wolfsberg scenario
Use_Case = [1, 4, 7, 10, 13, 16, 19, 22, 25, 28, 31, 34, 39] * 4 + [
2, 5, 8, 11, 14, 17, 20, 23, 26, 29, 32, 35, 38
] * 7 + [3, 6, 9, 12, 15, 18, 21, 24, 27, 30, 33, 36
] * 65 + [37] * 73 + [40, 41] * 2
refrating = [
'1', '1', '1', '2', '3', '4', '2', '4', '5', '5', '5', '5', '5', '5',
'5', '5', '5', '5', '5', '5'
]
fake = Faker()
global liSSNMaster
start = 10786147
acct_list = []
liCSV = []
for i in xrange(N):
#Initiate High Risk Flags
#Politically Exposed Person
PEP = 'No'
#Customer with a Suspicous Activity Report
SAR = 'No'
#Customer with a closed account
Clsd = 'No'
#High risk customer flag
high_risk = 'No'
#High Risk Rating
hr_rating = ''
#Customer that was demarketed by the bank
demarket = 'No'
dem_date = ''
#generate closed acct flag
if (max((randrange(0, 98, 1) - 96), 0) == 1):
Clsd = 'Yes'
#Random choice for number of credit card users per account number
No_CCs = random.choice(Number_CC)
#Generate account number
acct = start + 1 + randrange(1, 10, 1)
start = acct
#Randomly generate customer name + middle name in tmp
name = fake.name()
tmp = gen_data.create_name()
#Adds account number to account dictionary
acct_list.extend([acct])
#Creates a new row and adds data elements
row = [i] + [acct] + [random.choice(Acct_Type)] + [No_CCs] + [name] + [
tmp[0]
] + [liSSNMaster[i]]
#Dictionary for names list set to blank
names = []
#Dictionary for Social Security Number list set to blank
ssn = []
#Middle Name to reduce name dups
mdl = []
for j in range(No_CCs - 1):
names.insert(j, fake.name())
tmp2 = gen_data.create_name()
mdl.insert(j, tmp2[0])
##Pull from SSN Master list
randInt = randrange(1, len(liSSNMaster), 1)
if randInt != i:
ssn.insert(j, liSSNMaster[randInt])
else:
ssn.insert(j, liSSNMaster[randInt - 1])
#Name and SSN is set to blank if less than 4 customers on an account
for k in range(4 - No_CCs):
names.insert(No_CCs + k, '')
ssn.insert(No_CCs + k, '')
mdl.insert(No_CCs, '')
#Sets CC_NO to a random credit card number
CC_NO = gen_data.cc_number()
#Extract CC_Number from the tuple returned by CC_Number then scramble to ensure uniqueness...Tuple contains CC Number and Type
CC_TRANS = CC_NO[1][0]
dt = str(datetime.now())
clean = re.sub('\W', '', dt)
printCC = str(CC_TRANS[-4:]) + str(clean[-12:-3]) + str(
randrange(1111, 9999, randrange(1, 10, 1)))
#Add data elements to current csv row
row.extend([names[0],mdl[0],ssn[0],names[1],mdl[1],ssn[1],names[2],mdl[2],ssn[2],printCC,CC_NO[0],gen_data.create_company_name()+' '+tmp[1],\
gen_data.create_email(),gen_data.create_job_title()])
#Create Current Address
zip = random.choice(zips.zip)
addr = geo_data.create_city_state_zip[zip]
#Create Previous address
zip2 = random.choice(zips.zip)
addr2 = geo_data.create_city_state_zip[zip2]
#Add additional data elements to current csv row
lrg_cash_ex = random.choice(Yes_No)
#Condition for SARs and Demarketed Clients
if (Clsd == 'Yes'):
#1% of closed accounts are demarketed but never had a SAR filed
if (max((randrange(0, 101, 1) - 99), 0) == 1 and SAR == 'No'):
demarket = 'Yes'
dem_date = gen_data.create_date(past=True)
if (max((randrange(0, 11, 1) - 9), 0) == 1 and demarket == 'No'):
#10% of closed accounts have SARs
SAR = 'Yes'
#90% of closed accounts with SARs are demarketed
if (max((randrange(0, 11, 1) - 9), 0) == 0):
demarket = 'Yes'
dem_date = gen_data.create_date(past=True)
if (max((randrange(0, 101, 1) - 99), 0) == 1):
PEP = 'Yes'
row.extend([
addr[0], addr[1], zip, 'US', addr2[0], addr2[1], zip2, 'US',
gen_data.create_birthday(min_age=2, max_age=85), PEP, SAR, Clsd
])
#Start Generating related accounts from account list once 10,000 accounts are generated - to avoid duplicating accounts in the beginning
if i > 10000:
rel = int(random.choice(acct_list)) * max(
(randrange(0, 10001, 1) - 9999), 0)
if rel <> 0:
row.append(rel)
row.append(random.choice(Related_Type))
else:
row.append('')
row.append('')
else:
row.append('')
row.append('')
#Randomly generates account start date
party_start = gen_data.create_date(past=True)
#Randomly selects consent option for sharing info
Consent_Share = random.choice(Yes_No_Consent)
#Add additional data elements to current csv row
row.extend([random.choice(Party_Type),random.choice(Party_Relation),party_start,gen_data.create_date(past=True),\
lrg_cash_ex,demarket,dem_date,randrange(0,100,1),random.choice(Official_Lang)])
#Add data element preferred methond of contact for yes to share info...if not then blank to current row
if Consent_Share == 'Yes':
row.extend(['Yes', random.choice(Preffered_Channel)])
else:
row.extend(['No', ''])
row.extend([zip, randrange(0, 5, 1)])
#Generate Segment ID then add additional Segment data based on the selection to the current csv row
Segment_ID = randrange(0, 5, 1) % 5
if Segment_ID == 0:
row.extend([
Model_ID[0], Seg_Model_Type[0], Seg_Model_Name[0],
Seg_Model_Group[0], Seg_Model_Description[0],
Seg_Model_Score[0]
])
if Segment_ID == 1:
row.extend([
Model_ID[1], Seg_Model_Type[1], Seg_Model_Name[1],
Seg_Model_Group[1], Seg_Model_Description[1],
Seg_Model_Score[1]
])
if Segment_ID == 2:
row.extend([
Model_ID[2], Seg_Model_Type[2], Seg_Model_Name[2],
Seg_Model_Group[2], Seg_Model_Description[2],
Seg_Model_Score[2]
])
if Segment_ID == 3:
row.extend([
Model_ID[3], Seg_Model_Type[3], Seg_Model_Name[3],
Seg_Model_Group[3], Seg_Model_Description[3],
Seg_Model_Score[3]
])
if Segment_ID == 4:
row.extend([
Model_ID[4], Seg_Model_Type[4], Seg_Model_Name[4],
Seg_Model_Group[4], Seg_Model_Description[4],
Seg_Model_Score[4]
])
#Add additional data elements to current csv row
hr0 = random.choice(Arms_Manufacturer)
hr01 = random.choice(Auction)
hr02 = random.choice(CashIntensive_Business)
hr03 = random.choice(Casino_Gambling)
hr04 = random.choice(Channel_Onboarding)
hr05 = random.choice(Channel_Ongoing_Transactions)
row.extend([hr0, hr01, hr02, hr03, hr04, hr05])
#Randomly select whether customer has a High Net Worth
HighNetWorthFlag = random.choice(HighNetWorth)
#Randomly Generate customer net worth based on the above flag
if HighNetWorthFlag == 'Yes':
row.append(
max(
max((randrange(0, 101, 1) - 99), 0) *
randrange(1000000, 25000000, 1),
randrange(1000000, 5000000, 1)))
else:
flag = random.choice(LowNet)
if flag == 0:
row.append(randrange(-250000, 600000, 1))
else:
if flag == 1:
row.append(randrange(149000, 151000, 1))
else:
row.append(randrange(40000, 50000, 1))
#Add data elements to current csv row
hr1 = random.choice(Complex_HI_Vehicle)
hr2 = random.choice(Dealer_Precious_Metal)
hr3 = random.choice(Digital_PM_Operator)
hr4 = random.choice(Embassy_Consulate)
hr5 = random.choice(Exchange_Currency)
hr6 = random.choice(Foreign_Financial_Institution)
hr7 = random.choice(Foreign_Government)
hr8 = random.choice(Foreign_NonBank_Financial_Institution)
hr9 = random.choice(Internet_Gambling)
hr10 = random.choice(Medical_Marijuana_Dispensary)
hr11 = random.choice(Money_Service_Business)
hr12 = random.choice(NAICS.NAICS_Code)
hr13 = random.choice(NonRegulated_Financial_Institution)
hr14 = random.choice(Not_Profit)
#hr15=random.choice(Occupation) - added before through gen_data
hr16 = random.choice(Privately_ATM_Operator)
hr17 = random.choice(Products)
hr18 = random.choice(Sales_Used_Vehicles)
hr19 = random.choice(Services)
hr20 = random.choice(SIC_Code)
hr21 = random.choice(Stock_Market_Listing)
hr22 = random.choice(Third_Party_Payment_Processor)
hr23 = random.choice(Transacting_Provider)
if (PEP == 'Yes' or SAR == 'Yes' or lrg_cash_ex == 'Yes'
or demarket == 'Yes' or hr0 == 'Yes' or hr01 == 'Yes'
or hr02 == 'Yes' or hr03 == 'Yes' or hr1 == 'Yes'
or hr2 == 'Yes' or hr3 == 'Yes' or hr4 == 'Yes' or hr5 == 'Yes'
or hr6 == 'Yes' or hr7 == 'Yes' or hr8 == 'Yes' or hr9 == 'Yes'
or hr10 == 'Yes' or hr11 == 'Yes' or hr13 == 'Yes'
or hr14 == 'Yes' or hr16 == 'Yes' or hr17 == 'Yes'
or hr18 == 'Yes' or hr22 == 'Yes' or hr23 == 'Yes'
or HighNetWorthFlag == 'Yes'):
high_risk = 'Yes'
hr_rating = random.choice(refrating)
if (SAR == 'No' and high_risk == 'No'):
if (max((randrange(0, 101, 1) - 99), 0) == 1):
high_risk = 'Yes'
hr_rating = random.choice(refrating)
if (PEP == 'No' and high_risk == 'No'):
if (max((randrange(0, 101, 1) - 99), 0) == 1):
high_risk = 'Yes'
hr_rating = random.choice(refrating)
if (high_risk == 'No'):
if (max((randrange(0, 101, 1) - 99), 0) == 1):
high_risk = 'Yes'
hr_rating = random.choice(refrating)
row.extend([
hr1, hr2, hr3, hr4, hr5, hr6, hr7, hr8, hr9, hr10, hr11, hr12,
hr13, hr14, hr16, hr17, hr18, hr19, hr20, hr21, hr22, hr23,
HighNetWorthFlag, high_risk, hr_rating,
random.choice(Use_Case)
])
liCSV.append(row)
return liCSV
def gen_cust(liSSNMaster, acct_list, i):
fake = Faker()
#Initiate High Risk Flags
#Politically Exposed Person
PEP = 'No'
#Customer with a Suspicous Activity Report
SAR = 'No'
#Customer with a closed account
#generate closed acct flag
Clsd = choice(Clsd_flag)
#High risk customer flag
high_risk = 'No'
#High Risk Rating
hr_rating = ''
#Customer that was demarketed by the bank
demarket = 'No'
dem_date = ''
#Random choice for number of credit cards per account number
No_CCs = choice(Number_CC)
acct = start + 1 + randrange(1, 10, 1)
start = acct
#Randomly generates customer name
name = fake.name()
tmp = gen_data.create_name()
#Adds account number to account dictionary
acct_list.extend([acct])
#Creates a new row and adds data elements
## JS - Main Account Holder SSN as current index in master SSN list
row = [i] + [acct] + [choice(Acct_Type)
] + [No_CCs] + [name] + [tmp[0]] + [liSSNMaster[i]]
#Dictionary for names list set to blank
names = []
#Dictionary for Social Security Number list set to blank
ssn = []
#Generates Name and SSN for Credit Users
#Middle Name to reduce name dups
mdl = []
for j in range(No_CCs - 1):
names.insert(j, fake.name())
tmp2 = gen_data.create_name()
mdl.insert(j, tmp2[0])
## JS - Pull from SSN Master list
randInt = randrange(1, len(liSSNMaster), 1)
if randInt != i:
ssn.insert(j, liSSNMaster[randInt])
else:
ssn.insert(j, liSSNMaster[randInt - 1])
#Name and SSN is set to blank if less than 4 customers on an account
for k in range(4 - No_CCs):
names.insert(No_CCs + k, '')
ssn.insert(No_CCs + k, '')
mdl.insert(No_CCs, '')
#Sets CC_NO to a random credit card number
CC_NO = gen_data.create_cc_number()
CC_TRANS = CC_NO[1][0]
dt = str(datetime.now())
clean = re.sub('\W', '', dt)
printCC = str(CC_TRANS[-4:]) + str(clean[-12:-3]) + str(
randrange(1111, 9999, randrange(1, 10, 1)))
#Add data elements to current csv row
row.extend([names[0],mdl[0],ssn[0],names[1],mdl[1],ssn[1],names[2],mdl[2],ssn[2],printCC,CC_NO[0],gen_data.create_company_name()+' '+tmp[1],\
gen_data.create_email(),gen_data.create_job_title()])
#Creates Current Address
zip = choice(zips.zip)
addr = geo_data.create_city_state_zip[zip]
#Creates Previous address
zip2 = choice(zips.zip)
addr2 = geo_data.create_city_state_zip[zip2]
#Add additional data elements to current csv row
lrg_cash_ex = choice(Yes_No)
#Condition for SARs and Demarketed Clients
if (Clsd == 'Yes'):
#1% of closed accounts are demarketed but never had a SAR filed
if (max((randrange(0, 101, 1) - 99), 0) == 1 and SAR == 'No'):
demarket = 'Yes'
dem_date = gen_data.create_date(past=True)
if (max((randrange(0, 11, 1) - 9), 0) == 1 and demarket == 'No'):
#10% of closed accounts have SARs
SAR = 'Yes'
#90% of closed accounts with SARs are demarketed
if (max((randrange(0, 11, 1) - 9), 0) == 0):
demarket = 'Yes'
dem_date = gen_data.create_date(past=True)
#1% of accounts are PEP
if (max((randrange(0, 101, 1) - 99), 0) == 1):
PEP = 'Yes'
row.extend([
addr[0], addr[1], zip, 'US', addr2[0], addr2[1], zip2, 'US',
gen_data.create_birthday(min_age=2, max_age=85), PEP, SAR, Clsd
])
#Start Generating related accounts from account list once 10,000 accounts are generated
if i > 10000:
rel = int(choice(acct_list)) * max((randrange(0, 10001, 1) - 9999), 0)
if rel <> 0:
row.append(rel)
row.append(choice(Related_Type))
else:
row.append('')
row.append('')
else:
row.append('')
row.append('')
#Randomly generates account start date
party_start = gen_data.create_date(past=True)
#Randomly selects consent option for sharing info
Consent_Share = choice(Yes_No_Consent)
#Add additional data elements to current csv row
row.extend([choice(Party_Type),choice(Party_Relation),party_start,gen_data.create_date(past=True),\
lrg_cash_ex,demarket,dem_date,randrange(0,100,1),choice(Official_Lang)])
#Add data element preferred methond of contact for yes to share info...if not then blank to current row
if Consent_Share == 'Yes':
row.extend(['Yes', choice(Preffered_Channel)])
else:
row.extend(['No', ''])
#DO NOT USE CUST STATUS BELOW - NOT INTEGRATED WITH CLOSED STATUS! Add additional data elements to current csv row
row.extend([zip, randrange(0, 5, 1)])
#Generates Segment ID then adds additional Segment data based on the selection to the current csv row
Segment_ID = randrange(0, 5, 1)
if Segment_ID == 0:
row.extend(
['01', 'LOB Specific', 'IRRI', 'Group 1', 'High Risk Tier', '200'])
if Segment_ID == 1:
row.extend([
'02', 'Profitability', 'CRS Risk Score', 'Group 1',
'Mid Risk Tier', '300'
])
if Segment_ID == 2:
row.extend([
'03', 'Geographical', 'Geo Risk', 'Group 2', 'Low Risk Tier', '400'
])
if Segment_ID == 3:
row.extend([
'04', 'Behavioral', 'Financial Behavior Risk', 'Group 3',
'Vertical Risk', '100'
])
if Segment_ID == 4:
row.extend([
'05', 'Risk Tolerance', 'CM Risk', 'Group 4', 'Geographical Risk',
'500'
])
#Arms Manufacturer random choice
hr0 = choice(Yes_No_Cust_Flag)
#Auction random choice
hr01 = choice(Yes_No_Cust_Flag)
#Cash Intensive Business random choice
hr02 = choice(Yes_No_Cust_Flag)
#Casino Gambling random choice
hr03 = choice(Yes_No_Cust_Flag)
#Channel Onboarding random choice
hr04 = choice(Channel_Onboarding)
#Channel Ongoing Transactions random choice
hr05 = choice(Channel_Ongoing_Transactions)
#Add additional data elements to current csv row
row.extend([hr0, hr01, hr02, hr03, hr04, hr05])
#Randomly select whther customer has a High Net Worth
HighNetWorthFlag = choice(HighNetWorth)
#Randomly Generates customer net worth based on the above flag
if HighNetWorthFlag == 'Yes':
row.append(
max(
max((randrange(0, 101, 1) - 99), 0) *
randrange(5000000, 25000000, 1),
randrange(1000000, 5000000, 1)))
else:
flag = choice(LowNet)
if flag == 0:
row.append(randrange(-250000, 600000, 1))
else:
if flag == 1:
row.append(randrange(149000, 151000, 1))
else:
row.append(randrange(40000, 50000, 1))
#Add data elements to current csv row
#Complex_HI_Vehicle random choice
hr1 = choice(Yes_No_Cust_Flag)
#Dealer_Precious_Metal random choice
hr2 = choice(Yes_No_Cust_Flag)
#Digital_PM_Operator random choice
hr3 = choice(Yes_No_Cust_Flag)
#Embassy_Consulate random choice
hr4 = choice(Yes_No_Cust_Flag)
#Exchange_Currency random choice
hr5 = choice(Yes_No_Cust_Flag)
#Foreign_Financial_Institution random choice
hr6 = choice(Yes_No_Cust_Flag)
#Foreign_Government random choice
hr7 = choice(Yes_No_Cust_Flag)
#Foreign_NonBank_Financial_Institution random choice
hr8 = choice(Yes_No_Cust_Flag)
#Internet_Gambling random choice
hr9 = choice(Yes_No_Cust_Flag)
#Medical_Marijuana_Dispensary random choice
hr10 = choice(Yes_No_Cust_Flag)
#Money_Service_Business random choice
hr11 = choice(Yes_No_Cust_Flag)
hr12 = choice(NAICS.NAICS_Code)
#NonRegulated_Financial_Institution random choice
hr13 = choice(Yes_No_Cust_Flag)
#Not_Profit random choice
hr14 = choice(Yes_No_Cust_Flag)
#Occupation random choice
#hr15=choice(Occupation)
#Privately_ATM_Operator random choice
hr16 = choice(Yes_No_Cust_Flag)
#Products random choice
hr17 = choice(Products)
#Sales_Used_Vehicles random choice
hr18 = choice(Yes_No_Cust_Flag)
#Services random choice
hr19 = choice(Services)
#SIC_Code random choice
hr20 = choice(SIC_Code)
#Stock_Market_Listing random choice
hr21 = choice(Stock_Market_Listing)
#Third_Party_Payment_Processor random choice
hr22 = choice(Yes_No_Cust_Flag)
#Transacting_Provider random choice
hr23 = choice(Yes_No_Cust_Flag)
refrating = ['1'] * 3 + ['2', '4'] * 2 + ['3'] + ['5'] * 12
if (PEP == 'Yes' or SAR == 'Yes' or lrg_cash_ex == 'Yes'
or demarket == 'Yes' or hr0 == 'Yes' or hr01 == 'Yes'
or hr02 == 'Yes' or hr03 == 'Yes' or hr1 == 'Yes' or hr2 == 'Yes'
or hr3 == 'Yes' or hr4 == 'Yes' or hr5 == 'Yes' or hr6 == 'Yes'
or hr7 == 'Yes' or hr8 == 'Yes' or hr9 == 'Yes' or hr10 == 'Yes'
or hr11 == 'Yes' or hr13 == 'Yes' or hr14 == 'Yes' or hr16 == 'Yes'
or hr17 == 'Yes' or hr18 == 'Yes' or hr22 == 'Yes' or hr23 == 'Yes'
or HighNetWorthFlag == 'Yes'):
high_risk = 'Yes'
hr_rating = choice(refrating)
if (high_risk == 'No'):
if (max((randrange(0, 101, 1) - 99), 0) == 1):
high_risk = 'Yes'
hr_rating = choice(refrating)
row.extend([
hr1, hr2, hr3, hr4, hr5, hr6, hr7, hr8, hr9, hr10, hr11, hr12, hr13,
hr14, hr16, hr17, hr18, hr19, hr20, hr21, hr22, hr23, HighNetWorthFlag,
high_risk, hr_rating,
choice(Use_Case)
])
#End the current row
return row
