# 信贷风控
> Design an end-to-end machine learning system for a real-time loan approval/rejection model, such as credit cards. Discuss the infrastructure, features, model, training and evaluation aspects of the system.
## 1. requirements
**functional**
* stage: 获客、贷前(Loan origination)、贷中(Loan maintenance /servicing)、贷后(Delinquency management/recovery)
* types of loans it will support
* types of risk it will support -> 决定了是一个什么样的机器学习任务, 影响难点
* 目标: 识别优劣(风险，需求，价值)
* 业务指标
**non-functional**
* compliance requirements
* scalability goals
* reliability, security
## 2. ML task & pipeline & keys
信贷风控决策流 黑白名单 rule + model binary classification, multi class classification or multi label classification
## 3. data
* user (关系型数据库)
* credit 信用
* fraud 欺诈
* log (分布式文件系统)
* label
* 滚动率(Roll Rate)、vintage
* 从数据指标中发现风险点
* 信贷: 逾期不放贷款的就是黑样本
需求类: 履约风险: 履约能力:
外部数据
* 因合规要求，很多互联网大数据只能从征信平台以评分的形式引入
## 4. feature
> feature engineering is the art
> 
**user**
* ID/Address Proof: Voter ID, Aadhaar, PAN Card
* Employment Information, including salary slips
* Credit Score
* Bank Statements and Previous Loan Statements
* 反欺诈重要feature，如ip，device\_id，idfv，phone number
***
* graph
* 行为序列
* compliance -> buy some user feature
**稳定性**
* SHAP 是一种解释模型输出的方法，能展示某个变量对预测值的贡献。检查：变量的值是否与模型输出呈合理的趋势；是否存在“非预期拐点”或“跳跃”（可能是过拟合的信号）。
* 某些变量和风险标签的关系应该是单调的（如负债率↑，违约概率↑），否则可能存在逻辑问题、数据问题，或模型过拟合。建模阶段通过分析变量与 label 的WOE 图（Weight of Evidence）或 KS 图判断是否具有单调关系
## 5.model
> 可解释性和稳定性（stability）。模型不仅要在当前样本上表现好，还要对未来数据、不同时间段、不同人群具有鲁棒性，也就是稳定性。
* Credit Scoring Models 评分卡
* LR
* GBDT
* NN
* Probabilistic Calibration
## 6. evaluation
* offline
* 准确率、AUC、Log Loss、Precision、Recall
* Kolmogorov-Smirnov，风控常用指标
## 7. deploy & serving
* feature service
* prediction service
## 8. monitoring & maintenance
* Approval Rate
## 9. QA & optimization
* cold start
* profit/revenue come from which (credit score) part of customers, risk come from which part of customers
## reference
* [外部数据在信贷风控和经营中的应用](https://mp.weixin.qq.com/s/bJGFsIuNVNfg1866PQ9gYg)
* [Real Time Risk Management and Assessment](https://www.gigaspaces.com/blog/real-time-risk-management)
* [How Loan Management Software Improves Loan Risk Assessment For Lenders](https://lendfusion.com/blog/loan-risk-assessment/)
* [我现在来演示一下风控模型的架构 - 风控少侠的文章 - 知乎](https://zhuanlan.zhihu.com/p/717674909)
* [风控架构简单分享 - 回首乱山横的文章 - 知乎](https://zhuanlan.zhihu.com/p/683754431)
* [特征工程到底是什么？](https://www.zhihu.com/question/29316149)
* [风控模型—区分度评估指标(KS)深入理解应用 - 求是汪在路上的文章 - 知乎](https://zhuanlan.zhihu.com/p/79934510)
* [小样本学习在滴滴治理和安全场景应用](https://mp.weixin.qq.com/s/sf5B0hmhwIoYLuCU7Rdvug)
* [复杂风控场景下，如何打造一款高效的规则引擎](https://tech.meituan.com/2020/05/14/meituan-security-zeus.html)
* [Build and Deploy a Real Time ML System](https://paulabartabajo.substack.com/p/implementing-a-real-time-ml-system)
* [Let's build another Real Time ML System](https://www.realworldml.net/blog/let-s-build-a-real-time-ml-system-2)
*
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