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信用評(píng)分是指根據(jù)客戶的信用歷史資料贸弥，利用一定的信用評(píng)分模型，得到不同等級(jí)的信用分?jǐn)?shù)河咽。根據(jù)客戶的信用分?jǐn)?shù)胞皱， 授信者可以分析客戶按時(shí)還款的可能性。據(jù)此综芥， 授信者可以決定是否準(zhǔn)予授信以及授信的額度和利率
原理
A/B系數(shù)計(jì)算
Score= A - B * log(odds)
Score + pdo = A - B * log(odds/2)
B = pdo / log(2)
A = Score + B* log(odds)
odds - 壞好比 , 目標(biāo)變量值 1 的占比 除以 0 的占比
pdo - odds 變化時(shí) Score 得分的上下浮動(dòng)差
初始計(jì)算A/B值時(shí), odds 為整體數(shù)據(jù)的壞好比,即
odds = P_bad / (1-P_bad)
一般設(shè)定 Score 為600 ; Odds 減半時(shí)丽蝎，Score +20，假設(shè)初始 odds = (p/1-p)=0.2/0.8=0.25 則
B = 20 / log(2)=28.8539
A = 600 + 28.8539 * log(0.25) = 560邏輯回歸系數(shù)轉(zhuǎn)化
log(odds)=Beta_0+Beta_1 * x_1+Beta_2 * x_2+Beta_3 * x_3+...
Score = A - B * log(odds)
= A - B * (Beta_0+Beta_1 * x_1+Beta_2 * x_2+Beta_3 * x_3+...)
= A - B * Beta_0-B * Beta_1 * x_1-B * Beta_2 * x_2-B * Beta_3 * x_3...
=(A - B * Beta_0)-(B * Beta_1) * x_1-(B * Beta_2) * x_2-(B * Beta_3) * x_3...
注: x_i = woe_i
案例應(yīng)用
1. 數(shù)據(jù)讀取
library(openxlsx)
library(data.table)
# 讀取各網(wǎng)點(diǎn)的日運(yùn)營明細(xì)
data1<-read.xlsx("../data1.xlsx",sheet=1)
data2 <- data.table(data1)[,lapply(.SD,sum),
keyby=.(UID,month),
.SDcols=names(data1)[4:17]]
# 列名稱中不能帶'.',若有則需要修改,考慮到作圖名稱過長也改掉
colnames(data2)[3:7]=c('lin_zhi','lin_zhuan','kuasheng','G_G','G_M')
colnames(data2)[ncol(data2)-c(2,1)] <- c('pingtai','quantity')
2. 數(shù)據(jù)清洗
2.1 NA 處理
2.1.1 NA 的列處理方式
VIM::matrixplot(res1)
na1 <- as.data.frame(sapply(res1,function(x){sum(is.na(x))/length(x)}))
colnames(na1)='per1';na1$col1 <- rownames(na1)
na1[order(-na1$per1),]
per1 col1
lin_zhi 0.99480405 lin_zhi
M-G 0.90656099 M-G
M-M 0.83540291 M-M
lin_zhuan 0.09749009 lin_zhuan
G_G 0.07476882 G_G
G_M 0.04720387 G_M
... ...
res10 <- res1[,-c(1,6,7)]
2.1.2 NA 的行處理方式
na_cnt1 <- apply(res10,1,function(x)sum(is.na(x)))
table(na_cnt1)
na_cnt1
0 1 2 3 4 5 6 11
9416 1372 309 31 44 4 4 175
res101 <- res10[-which(na_cnt1>=round(ncol(res10)/3)),]
VIM::matrixplot(res101)
2.1.3 其他 NA 的處理方式
- 自動(dòng)填充
rcaret::knnImputation() - 均值替代
rx[is.na(x)] = mean(x, na.rm=TRUE) - 全部用 0 填充
本案例結(jié)合實(shí)際情況,采集 0 填充的方案
res11 <- res101[,lapply(.SD,function(x) ifelse(is.na(x),0,x))]
2.2 異常值處理
異常值檢測 可以用 rquantile(x,c(0.25,0.75)) +/- 1.5(or 3) * diff(quantile(x,c(0.25,0.75))) 的標(biāo)準(zhǔn)來完成
3. 共線性診斷處理和變量篩選
# --- 多重共線性診斷
corr1 <- cor(res101[,!'clus1'])
kappa(corr1,exact = TRUE)
# 條件數(shù)<100,則認(rèn)為多重共線性的程度很小,
# 若100<=條件數(shù)<=1000,則認(rèn)為存在中等程度的多重共線性,
# 若條件數(shù)>1000,則認(rèn)為存在嚴(yán)重的多重共線性.
# lasso變量篩選
# 如有疑問 請(qǐng)咨詢QQ群 174225475
head(res11)
4. WoE 計(jì)算
indx1 <- sample(c(0,1),nrow(res11),prob = c(0.3,0.7),replace = TRUE)
train1 <- res11[indx1==1,]
test1 <- res11[indx1==0,]
dat <- data.frame(train1) # train data
y='clus1' # target var
var_d <- c() # category var
var_c <- c() # numeric var
if(length(var_d1)==0){
var_d = NULL
}else{var_d = var_d1}
if(length(var_c1)==0){
var_c = NULL
}else{var_c = var_c1}
fit_data <- dat # testdata
source('./woe_repalce.R') # 調(diào)用 woe 計(jì)算函數(shù)
# woe_replace函數(shù), 請(qǐng)咨詢QQ群 174225475
library(smbinning)
woe_info1 <- woe.replace(dat,var_c,var_d,y,fit_data)
woe1 <- woe_info1[[1]] # woe 分值
(rules1 <- woe_info1[[2]]) # 各變量內(nèi)分組與woe分值對(duì)應(yīng)關(guān)系
Cutpoint WoE varIndex
1 <= 0.1139 -5.9691 jiesuanshouru
2 <= 0.356 -2.3566 jiesuanshouru
3 <= 0.5561 -1.0874 jiesuanshouru
4 <= 0.8008 0.2356 jiesuanshouru
5 <= 1.0811 1.6840 jiesuanshouru
6 <= 1.3079 2.7690 jiesuanshouru
... ...
5. 最終得分計(jì)算及評(píng)分規(guī)則整理
5.1 計(jì)算系數(shù) A/B
> (pdo <- 30)
[1] 30
> (B <- pdo/log(2))
[1] 43.28085
> (odds <- table(train1$clus1)[2]/table(train1$clus1)[1])
1
0.2579551
> (SC <- 600)
[1] 600
> (A <- SC + B*log(odds))
1
541.3558
5.2 利用 WoE 值重新建立 logistic 模型計(jì)算 各項(xiàng)系數(shù)
實(shí)際操作中, 因變量的分布是極度不平衡的, 因此在建模時(shí)就有了要不要人工設(shè)定 weights 參數(shù)對(duì)建模數(shù)據(jù)進(jìn)行樣本比例矯正, 以下是兩種方案(設(shè)置/不設(shè)置weights)的比較
> woe2 <- data.frame(cbind(woe1,clus1=train1$clus1))
> lgc3 <- glm(clus1~.,data=woe2,
family = binomial(link = 'logit'))
> lgc_res3 <- predict(lgc3,data.frame(woe1),type = 'response')
> table(ifelse(lgc_res3 >0.5,1,0),train1$clus1)
0 1
0 6100 90
1 91 1507
> library(ModelMetrics)
> auc(ifelse(lgc_res3 >0.5,1,0),train1$clus1)
[1] 0.9642571
# ------------------
> lgc3 <- glm(clus1~.,data=woe2,weights = ifelse(woe2$clus1>0,5,1),
+ family = binomial(link = 'logit'))
> lgc_res3 <- predict(lgc3,data.frame(woe1),type = 'response')
> table(ifelse(lgc_res3 >0.5,1,0),train1$clus1)
0 1
0 5991 18
1 200 1579
> library(ModelMetrics)
> auc(ifelse(lgc_res3 >0.5,1,0),train1$clus1)
[1] 0.9422909
> woe_info2 <- woe.replace(dat,var_c,var_d,y,
+ fit_data = data.frame(test1))
> woe3 <- woe_info2[[1]]
> lgc_res4 <- predict(lgc3,data.frame(woe3),type = 'response')
> table(ifelse(lgc_res4 >0.5,1,0),test1$clus1)
0 1
0 534 10
1 29 18
添加了 weights 參數(shù), 模型對(duì) 違約客戶 的預(yù)測準(zhǔn)確率會(huì)更高, 但整體AUC 會(huì)受影響, 因此在建模時(shí)可根據(jù) 業(yè)務(wù)的實(shí)際需求進(jìn)行 weights 參數(shù)的取舍
5.3 WoE 向?qū)嶋H得分轉(zhuǎn)化及其規(guī)則整理
# --- train1 樣本總得分計(jì)算
coef1 <- lgc3$coefficients
(BaseSC <- A-B*coef1[1])
Tsc1 <- BaseSC + woe1 %*% coef1[-1]
# ---得分轉(zhuǎn)化規(guī)則梳理
rules1$coef1 <- coef1[rules1$varIndex]
rules1$sc1 <- with(rules1,WoE*coef1)
# --- 分值分布情況
library(dplyr)
rules1 %>%
group_by(varIndex) %>%
summarise(max1=max(sc1),
min1=min(sc1),
mean1=mean(sc1))
# A tibble: 7 x 4
varIndex max1 min1 mean1
<chr> <dbl> <dbl> <dbl>
1 feiyongheji 2.4498204 -3.176858 -0.12697315
2 jianshu 2.9163435 -3.223899 0.18207080
3 jiesuanshouru 2.8838985 -3.500189 0.29030440
4 pingtai 3.4201266 -5.301336 -0.02456898
5 quantity 0.9789534 -1.492049 -0.11555953
6 tiji 2.8583949 -3.338300 -0.08009086
7 yunfei 2.4681047 -3.475437 -0.14391316
# --- 規(guī)則展示
> rules1[,.(varIndex,Cutpoint,sc1)]
varIndex Cutpoint sc1
1: jiesuanshouru <= 0.1139 -3.50018886
2: jiesuanshouru <= 0.356 -1.38187416
3: jiesuanshouru <= 0.5561 -0.63763471
4: jiesuanshouru <= 0.8008 0.13815223
5: jiesuanshouru <= 1.0811 0.98747182
6: jiesuanshouru <= 1.3079 1.62369921
7: jiesuanshouru <= 1.4988 2.20891113
8: jiesuanshouru > 1.4988 2.88389855
9: feiyongheji <= -0.449 -3.17685829
10: feiyongheji <= 0.2052 -1.85452835
11: feiyongheji <= 0.4717 -0.89196384
12: feiyongheji <= 0.7499 -0.19168770
13: feiyongheji <= 1.0887 0.92124707
14: feiyongheji <= 1.4839 1.85515871
15: feiyongheji > 1.4839 2.44982037
16: yunfei <= -0.5582 -3.47543677
17: yunfei <= 0.2745 -1.86611915
... ...
