风控模型-风险预警模型

最近一个朋友在面试一家银行的算法岗，第一轮是take home test，也就是公司发了个测试题，要求候选人回家做完，面试讲解。看来今年市场行情的确不太好，很少高级算法岗会有这么一轮面试。笔者看了下数据，还蛮有意思，正好顺一下建模的pipeline，讲解下WOE和LR的结合应用。

WOE 的应用价值

  • 处理缺失值：利用分箱讲null单独处理，可以讲有效覆盖率只有30%的数据利用起来
  • 处理异常值：利用分箱讲异常值单独处理，增加变量的鲁棒性。例如，年龄由于用户手动填写的，可能存在200这种异常值，可以将这种情况划入age>60的分箱。
  • 业务解释性：业务习惯于线性判断变量的作用，当x越来越大，y就越来越大。但实际x与y之间经常存在着非线性关系，可经过WOE进行变换

建模pipeline

建模的pipeline:

加载数据->EDA->Feature Generation->Model Establishment-> Release online

EDA

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import json
from scipy.stats import chi2_contingency
pd.set_option('display.max_columns', None)
sns.set_theme(style="darkgrid")
sns.set(rc = {'figure.figsize':(20,15)})

dataset.head()
accountNumber customerId creditLimit availableMoney transactionDateTime transactionAmount merchantName acqCountry merchantCountryCode posEntryMode posConditionCode merchantCategoryCode currentExpDate accountOpenDate dateOfLastAddressChange cardCVV enteredCVV cardLast4Digits transactionType echoBuffer currentBalance merchantCity merchantState merchantZip cardPresent posOnPremises recurringAuthInd expirationDateKeyInMatch isFraud transactionDate transactionHour transactionMonth
0 737265056 737265056 5000.0 5000.0 2016-08-13 14:27:32 98.55 Uber US US 02 01 rideshare 06/2023 2015-03-14 2015-03-14 414 414 1803 PURCHASE NaN 0.0 NaN NaN NaN False NaN NaN False False 2016-08-13 14 8
1 737265056 737265056 5000.0 5000.0 2016-10-11 05:05:54 74.51 AMC #191138 US US 09 01 entertainment 02/2024 2015-03-14 2015-03-14 486 486 767 PURCHASE NaN 0.0 NaN NaN NaN True NaN NaN False False 2016-10-11 5 10
2 737265056 737265056 5000.0 5000.0 2016-11-08 09:18:39 7.47 Play Store US US 09 01 mobileapps 08/2025 2015-03-14 2015-03-14 486 486 767 PURCHASE NaN 0.0 NaN NaN NaN False NaN NaN False False 2016-11-08 9 11
3 737265056 737265056 5000.0 5000.0 2016-12-10 02:14:50 7.47 Play Store US US 09 01 mobileapps 08/2025 2015-03-14 2015-03-14 486 486 767 PURCHASE NaN 0.0 NaN NaN NaN False NaN NaN False False 2016-12-10 2 12
4 830329091 830329091 5000.0 5000.0 2016-03-24 21:04:46 71.18 Tim Hortons #947751 US US 02 01 fastfood 10/2029 2015-08-06 2015-08-06 885 885 3143 PURCHASE NaN 0.0 NaN NaN NaN True NaN NaN False False 2016-03-24 21 3 
fraud = dataset['isFraud'].value_counts().to_frame()
fraud['pct'] = fraud['isFraud']/fraud['isFraud'].sum()
display(fraud)
isFraud pct
False 773946 0.98421
True 12417 0.01579

这是个极度不平衡的数据集,对于unbalanced的数据集，两个思路，一个是upsampling，一个是downsampling.这个放在其他文章里讲吧，本篇就不涉及了

信用卡刷卡，有时候会因为网络等原因存在多次扣款的情况，数据集里就会存在多条重复记录，需要将这些重复记录删除

dataset = dataset[~(dataset['transactionType'].isin(['REVERSAL']))]
mult_swipe = dataset[dataset.duplicated(keep='first',subset=['customerId','transactionDate','transactionAmount','cardLast4Digits','transactionHour'])]
print('multi-swipe transaction number:{0},amount:{1}'.format(len(mult_swipe),sum(mult_swipe['transactionAmount'])))
dataset = dataset[~(dataset.index.isin(mult_swipe.index))]

multi-swipe transaction number:7565,amount:1076660.0299999956

先看一眼随时间的分布

sns.set_theme(style="darkgrid")
sns.set(rc = {'figure.figsize':(20,15)})
plt.figure(figsize = (15,8))
sns.barplot(data = dataset, x='transactionMonth',y='transactionAmount',estimator=sum)


sns.set_theme(style="darkgrid")
sns.set(rc = {'figure.figsize':(20,15)})
plt.figure(figsize = (15,8))
sns.barplot(data = dataset, x='transactionMonth',y='transactionAmount',estimator=len)

可以看出每个月份的交易金额和交易笔数相差不大

plt.figure(figsize = (15,8))
sns.boxplot(data = dataset,x='transactionMonth',y='transactionAmount',notch=True,showcaps=True,flierprops={'marker':'x'},medianprops={'color':'coral'})

boxplot反映异常值主要是较高金额

处理下null较多的字段

data_null
null_num total pct
accountNumber 0 758495 0.000000
customerId 0 758495 0.000000
creditLimit 0 758495 0.000000
availableMoney 0 758495 0.000000
transactionDateTime 0 758495 0.000000
transactionAmount 0 758495 0.000000
merchantName 0 758495 0.000000
acqCountry 4401 758495 0.005802
merchantCountryCode 703 758495 0.000927
posEntryMode 3904 758495 0.005147
posConditionCode 396 758495 0.000522
merchantCategoryCode 0 758495 0.000000
currentExpDate 0 758495 0.000000
accountOpenDate 0 758495 0.000000
dateOfLastAddressChange 0 758495 0.000000
cardCVV 0 758495 0.000000
enteredCVV 0 758495 0.000000
cardLast4Digits 0 758495 0.000000
transactionType 690 758495 0.000910
echoBuffer 758495 758495 1.000000
currentBalance 0 758495 0.000000
merchantCity 758495 758495 1.000000
merchantState 758495 758495 1.000000
merchantZip 758495 758495 1.000000
cardPresent 0 758495 0.000000
posOnPremises 758495 758495 1.000000
recurringAuthInd 758495 758495 1.000000
expirationDateKeyInMatch 0 758495 0.000000
isFraud 0 758495 0.000000
transactionDate 0 758495 0.000000
transactionHour 0 758495 0.000000
transactionMonth 0 758495 0.000000

remove columns which null_pct >= 0.5

data_df = dataset[data_null[data_null['pct']<0.5].index.tolist()]

data_df = data_df[['customerId', 'creditLimit', 'availableMoney','transactionDateTime','transactionAmount', 'merchantName','acqCountry', 'merchantCountryCode', 'posEntryMode', 'posConditionCode','merchantCategoryCode','accountOpenDate','dateOfLastAddressChange', 'cardCVV', 'enteredCVV', 'cardLast4Digits','transactionType', 'currentBalance','expirationDateKeyInMatch', 'isFraud', 'transactionDate','transactionHour','transactionMonth']]

进一步看下fraud交易和正常交易随时间分布的差异

fig, axes = plt.subplots(3,4)
sns.histplot(ax=axes[0,0],data = data_df[(data_df['transactionMonth']==1)&(data_df['isFraud']==True)], x='transactionAmount',binwidth = 100,stat='probability').set(title='1')
sns.histplot(ax=axes[0,1],data = data_df[(data_df['transactionMonth']==2)&(data_df['isFraud']==True)], x='transactionAmount',binwidth = 100,stat='probability').set(title='2')
sns.histplot(ax=axes[0,2],data = data_df[(data_df['transactionMonth']==3)&(data_df['isFraud']==True)], x='transactionAmount',binwidth = 100,stat='probability').set(title='3')
sns.histplot(ax=axes[0,3],data = data_df[(data_df['transactionMonth']==4)&(data_df['isFraud']==True)], x='transactionAmount',binwidth = 100,stat='probability').set(title='4')
sns.histplot(ax=axes[1,0],data = data_df[(data_df['transactionMonth']==5)&(data_df['isFraud']==True)], x='transactionAmount',binwidth = 100,stat='probability').set(title='5')
sns.histplot(ax=axes[1,1],data = data_df[(data_df['transactionMonth']==6)&(data_df['isFraud']==True)], x='transactionAmount',binwidth = 100,stat='probability').set(title='6')
sns.histplot(ax=axes[1,2],data = data_df[(data_df['transactionMonth']==7)&(data_df['isFraud']==True)], x='transactionAmount',binwidth = 100,stat='probability').set(title='7')
sns.histplot(ax=axes[1,3],data = data_df[(data_df['transactionMonth']==8)&(data_df['isFraud']==True)], x='transactionAmount',binwidth = 100,stat='probability').set(title='8')
sns.histplot(ax=axes[2,0],data = data_df[(data_df['transactionMonth']==9)&(data_df['isFraud']==True)], x='transactionAmount',binwidth = 100,stat='probability').set(title='9')
sns.histplot(ax=axes[2,1],data = data_df[(data_df['transactionMonth']==10)&(data_df['isFraud']==True)], x='transactionAmount',binwidth = 100,stat='probability').set(title='10')
sns.histplot(ax=axes[2,2],data = data_df[(data_df['transactionMonth']==11)&(data_df['isFraud']==True)], x='transactionAmount',binwidth = 100,stat='probability').set(title='11')
sns.histplot(ax=axes[2,3],data = data_df[(data_df['transactionMonth']==12)&(data_df['isFraud']==True)], x='transactionAmount',binwidth = 100,stat='probability').set(title='12')
plt.tight_layout()


fig, axes = plt.subplots(3,4)
sns.histplot(ax=axes[0,0],data = data_df[(data_df['transactionMonth']==1)&(data_df['isFraud']==False)], x='transactionAmount',binwidth = 100,stat='probability').set(title='1')
sns.histplot(ax=axes[0,1],data = data_df[(data_df['transactionMonth']==2)&(data_df['isFraud']==False)], x='transactionAmount',binwidth = 100,stat='probability').set(title='2')
sns.histplot(ax=axes[0,2],data = data_df[(data_df['transactionMonth']==3)&(data_df['isFraud']==False)], x='transactionAmount',binwidth = 100,stat='probability').set(title='3')
sns.histplot(ax=axes[0,3],data = data_df[(data_df['transactionMonth']==4)&(data_df['isFraud']==False)], x='transactionAmount',binwidth = 100,stat='probability').set(title='4')
sns.histplot(ax=axes[1,0],data = data_df[(data_df['transactionMonth']==5)&(data_df['isFraud']==False)], x='transactionAmount',binwidth = 100,stat='probability').set(title='5')
sns.histplot(ax=axes[1,1],data = data_df[(data_df['transactionMonth']==6)&(data_df['isFraud']==False)], x='transactionAmount',binwidth = 100,stat='probability').set(title='6')
sns.histplot(ax=axes[1,2],data = data_df[(data_df['transactionMonth']==7)&(data_df['isFraud']==False)], x='transactionAmount',binwidth = 100,stat='probability').set(title='7')
sns.histplot(ax=axes[1,3],data = data_df[(data_df['transactionMonth']==8)&(data_df['isFraud']==False)], x='transactionAmount',binwidth = 100,stat='probability').set(title='8')
sns.histplot(ax=axes[2,0],data = data_df[(data_df['transactionMonth']==9)&(data_df['isFraud']==False)], x='transactionAmount',binwidth = 100,stat='probability').set(title='9')
sns.histplot(ax=axes[2,1],data = data_df[(data_df['transactionMonth']==10)&(data_df['isFraud']==False)], x='transactionAmount',binwidth = 100,stat='probability').set(title='10')
sns.histplot(ax=axes[2,2],data = data_df[(data_df['transactionMonth']==11)&(data_df['isFraud']==False)], x='transactionAmount',binwidth = 100,stat='probability').set(title='11')
sns.histplot(ax=axes[2,3],data = data_df[(data_df['transactionMonth']==12)&(data_df['isFraud']==False)], x='transactionAmount',binwidth = 100,stat='probability').set(title='12')
plt.tight_layout()

fraud类型交易0-100和100-200区间交易占比较高。正常交易0-100占比明显高于其他区间，且第四季度占比略高于其他季度

plt.figure(figsize = (15,8))
g = sns.FacetGrid(data_df,col='transactionTime_',row='isFraud',margin_titles=True)
g.map(sns.histplot,'transactionAmount',stat='probability',binwidth = 50)
plt.tight_layout()

看着这个比例分布，正常交易对时间段不敏感，无差异。fraud类型的交易下午时间段分布和其他时间段不同，100-200占比最高

cus_df.head()
customerId transactionAmount transactionDate cumpct
0 380680241 4589985.93 31554 0.044210
1 882815134 1842601.52 12665 0.061958
2 570884863 1514931.43 10452 0.076549
3 246251253 1425588.84 9806 0.090280
4 369308035 1012414.42 6928 0.100032 
sns.lineplot(x='cus_pct',y='cumpct',data=cus_df )

80%的交易金额由20%的客户贡献

sns.histplot(data = cus_df, x='TA',stat='count',binwidth=10).set(title='TA')

客单价集中在150左右

df_ttl = df[['customerId', 'creditLimit','transactionAmount','acqCountry','merchantCategoryCode','transactionType','currentBalance','transactionMonth','transactionTime_','days_tranz_open','merchantName_', 'CVV', 'days_address', 'isFraud']]

from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn import metrics
from sklearn.metrics import roc_curve
from sklearn.model_selection import GridSearchCV
trained_cols = ['customerId', 'creditLimit','transactionAmount','acqCountry','merchantCategoryCode','transactionType','currentBalance','transactionMonth','transactionTime_','days_tranz_open','merchantName_', 'CVV', 'days_address','label']s_train_X,s_test_X,s_train_y,s_test_y = train_test_split(df_ttl[trained_cols],\df_ttl['label'],train_size=0.8,random_state=123)

cus_df = s_train_X.groupby('customerId',as_index=False).agg({'transactionAmount':'sum','acqCountry':'count'})
cus_df = cus_df.rename(columns = {'acqCountry':'Frequency'})
cus_df['TA'] = cus_df['transactionAmount']/cus_df['Frequency']

s_train_X = s_train_X.merge(cus_df[['customerId','Frequency','TA']],on='customerId',how='left')
s_test_X = s_test_X.merge(cus_df[['customerId','Frequency','TA']],on='customerId',how='left')

s_train_X['TA_Tranz'] = s_train_X['transactionAmount'] - s_train_X['TA']
s_test_X['TA_Tranz'] = s_test_X['transactionAmount'] - s_test_X['TA']

feature generation

class WOE(Analysis):@staticmethoddef __perc_share(df,group_name):return df[group_name]/df[group_name].sum()def __calculate_perc_share(self,feat):df = self.group_by_feature(feat)df['perc_good'] = self.__perc_share(df,'good')df['perc_bad'] = self.__perc_share(df,'bad')df['perc_diff'] = df['perc_good'] - df['perc_bad']return dfdef calculate_woe(self,feat):df = self.__calculate_perc_share(feat)df['woe'] = np.log(df['perc_good']/df['perc_bad'])df['woe'] = df['woe'].replace([np.inf,-np.inf],np.nan).fillna(0)return df
class CategoricalFeature():def __init__(self,df,feature):self.df = dfself.feature = feature@propertydef _df_woe(self):df_woe = self.df.copy()df_woe['bin'] = df_woe[self.feature].fillna('missing')return df_woe[['bin','label']]
def draw_woe(woe_df):fig, ax = plt.subplots(figsize=(10,6))sns.barplot(x=woe_df.columns[0], y=woe_df.columns[-2], data=woe_df, palette=sns.cubehelix_palette(len(woe_df),start=0.5,rot=0.75,reverse=True))ax.set_title('WOE visualization for: ' + feature)plt.xticks(rotation=30)plt.show()

def print_iv(woe_df):iv = woe_df['iv'].sum()if iv < 0.02:interpre = 'useless'elif iv < 0.1:interpre = 'weak'elif iv < 0.3:interpre = 'medium'elif iv < 0.5:interpre = 'strong'else:interpre = 'toogood'return iv,interpre

category feature

feature_cat = ['creditLimit','acqCountry', 'transactionType', 'transactionMonth', 'transactionTime_', 'CVV','merchantName_','merchantCategoryCode']
# feature_cat = ['creditLimit']
iv_dic = {}
iv_dic['feature'] = []
iv_dic['iv'] = []
iv_dic['interpretation'] = []
iv_df = pd.DataFrame(iv_dic)
display(iv_df)
feature iv interpretation
0 creditLimit 0.019578 useless
1 acqCountry 0.000674 useless
2 transactionType 0.016641 useless
3 transactionMonth 0.003295 useless
4 transactionTime_ 0.000690 useless
5 CVV 0.004968 useless
6 merchantName_ 0.766754 toogood
7 merchantCategoryCode 0.222249 medium

continuous feature

import scipy.stats as stats
feature_conti = ['transactionAmount','currentBalance','days_tranz_open','days_address', 'Frequency', 'TA','TA_Tranz']
# feature_conti = ['Frequency']
iv_con_dic = {}
iv_con_dic['feature'] = []
iv_con_dic['iv'] = []
iv_con_dic['interpretation'] = []
iv_con_df = pd.DataFrame(iv_con_dic)
iv_con_df
feature iv interpretation
0 transactionAmount 0.377574 strong
1 currentBalance 0.003375 useless
2 days_tranz_open 0.000420 useless
3 days_address 0.003841 useless
4 Frequency 0.023392 weak
5 TA 0.022107 weak
6 TA_Tranz 0.322500 strong 
draw_woe(woe_df)

这里用单笔交易与相应客户的平均比单价的差举例，可以看出当差大于10时，相应交易更倾向于fraud

col_model = iv_df[~(iv_df['interpretation'].isin(['useless']))]['feature'].values.tolist()
col_model.extend(iv_con_df[~(iv_con_df['interpretation'].isin(['useless']))]['feature'].values.tolist())
trained_col = []
for col in col_model:trained_col.append('woe'+col)
trained_col

['woemerchantName_','woemerchantCategoryCode','woetransactionAmount','woeFrequency','woeTA','woeTA_Tranz']

后面就使用这6个feature进行建模

Model

利用GridsearchCV对超参数进行遍历寻优

lr_paras = [{'penalty':['l2'],'C':[0.01,0.05,0.1,0.5,1,5,10,50,100],'class_weight':[{0:0.1,1:0.9},{0:0.2,1:0.8},{0:0.3,1:0.7},{0:0.4,1:0.6},{0:0.5,1:0.5},{0:0.6,1:0.4},{0:0.7,1:0.3},{0:0.8,1:0.2},{0:0.9,1:0.1}],'solver':['liblinear'],'multi_class':['ovr']}]modelLR = GridSearchCV(LogisticRegression(tol=1e-6),lr_paras,cv=5,verbose=1)
modelLR.fit(s_train_X,s_train_y)

Fitting 5 folds for each of 81 candidates, totalling 405 fits[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done 405 out of 405 | elapsed:  7.0min finishedGridSearchCV(cv=5, estimator=LogisticRegression(tol=1e-06),param_grid=[{'C': [0.01, 0.05, 0.1, 0.5, 1, 5, 10, 50, 100],'class_weight': [{0: 0.1, 1: 0.9}, {0: 0.2, 1: 0.8},{0: 0.3, 1: 0.7}, {0: 0.4, 1: 0.6},{0: 0.5, 1: 0.5}, {0: 0.6, 1: 0.4},{0: 0.7, 1: 0.3}, {0: 0.8, 1: 0.2},{0: 0.9, 1: 0.1}],'multi_class': ['ovr'], 'penalty': ['l2'],'solver': ['liblinear']}],verbose=1)

coef_,intercept_ = LR_(modelLR.best_estimator_,s_train_X,s_train_y)
s_test_X.drop(columns='pred',inplace=True)
s_train_X.drop(columns='pred',inplace=True)

s_y_pred_prob = lr_score(s_test_X,coef_,intercept_)s_y_pred_prob_train = lr_score(s_train_X,coef_,intercept_)

LR可以输出probability，遍历下步长，确定最优的threshold

thre = np.linspace(0,0.5,50)

score_dic = {}
score_dic['thre'] = []
score_dic['score'] = []
for item in thre:s_y_pred_train = [1 if i >= item else 0 for i in s_y_pred_prob_train]score_f1 = f1_score(s_train_y,s_y_pred_train,average='macro')score_dic['thre'].append(item)score_dic['score'].append(score_f1)

s_y_pred_train = [1 if i >= thresh else 0 for i in s_y_pred_prob_train]
s_y_pred = [1 if i >= thresh else 0 for i in s_y_pred_prob]

print('Training set performance')
print(metrics.confusion_matrix(s_train_y, s_y_pred_train))
print(metrics.classification_report(s_train_y, s_y_pred_train))print('Test set performance')
print(metrics.confusion_matrix(s_test_y, s_y_pred))
print(metrics.classification_report(s_test_y, s_y_pred))

Training set performance
[[576091  14082][  7969   1162]]precision    recall  f1-score   support0       0.99      0.98      0.98    5901731       0.08      0.13      0.10      9131accuracy                           0.96    599304macro avg       0.53      0.55      0.54    599304
weighted avg       0.97      0.96      0.97    599304Test set performance
[[144053   3385][  2091    297]]precision    recall  f1-score   support0       0.99      0.98      0.98    1474381       0.08      0.12      0.10      2388accuracy                           0.96    149826macro avg       0.53      0.55      0.54    149826
weighted avg       0.97      0.96      0.97    149826

fpr, tpr, _ = metrics.roc_curve(s_test_y, s_test_X_.pred)
auc = metrics.roc_auc_score(s_test_y, s_test_X_.pred)plt.figure(figsize=(8,6))
sns.lineplot(fpr,tpr,label='Model AUC %0.2f' % auc, color='palevioletred', lw = 2)
plt.plot([0, 1], [0, 1], color='lightgrey', lw=1.5, linestyle='--')
plt.xticks(fontsize=12)
plt.yticks(fontsize=12)
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate',fontsize=12)
plt.ylabel('True Positive Rate',fontsize=12)
plt.title('ROC - Test Set',fontsize=13)
plt.legend(loc="lower right",fontsize=12)
plt.rc_context({'axes.edgecolor':'darkgrey','xtick.color':'black','ytick.color':'black','figure.facecolor':'white'})
plt.show()

AUC虽然有0.75，但是F1score只有0.54，模型效果其实是很差的。这主要是极端不平衡数据集带来的影响。同时也说明，对于这种极端不平衡数据集挑选合适的metrics很重要

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