Macroeconomic-based Approach to Credit Loss Forecasting 基于宏观经济的信用损失预测方法Consumer Credit Card Portfolio 个人信用卡组合,Delinquency Migration, Macroeconomic Indicators & Applications to Portfolio Credit Risk Forecasting 逾期变动、宏观经济指标以及在组合信用风险预测中的应用,Credit Portfolio Analytics 信贷组合分析组,Overview 概述,Credit Portfolio Analytics Group Mission 信贷组合分析组的使命To provide management with an independent, top-down, macroeconomic-driven asset quality forecast range, including a baseline forecast based on most-likely assumption, that is accurate, comprehensive, nimble, and actionable.向管理层提供以宏观经济为基础、独立、自上而下的资产质量预测，包括根据“最可几”假设作出的、准确、全面、可提供行动依据的基准线预测。Assist in management of earnings volatility associated with risk in the credit portfolio.协助管理与信用组合风险有关的盈利波动。To mitigate significant potential losses due to event risk, and 减少因事件风险造成的重大潜在损失；以及 To design and implement proactive strategies for portfolio management. 设计和实施主动性的组合管理战略Old EnvironmentOne Forecast Opinion 旧的环境预测角度的看法Line-of-Business/Risk Management provided asset Quality forecast to Senior Management 由业务线/风险管理部门向高级管理层提供资产质量预测“Bottoms Up” approachaccount level data ”自下而上“的预测方法账户级数据- How many standard deviations from expected? 距离期望值有多少个标准差？ No Contrarian view 不采取反向观点 Not explicitly linked to future economic environment 没有与未来的经济环境建立明确的关联 One outcome provided to senior management 只向高级管理层提供一个结果- Conservative? Reasonable? Or a stretch? 保守？合理？还是太乐观？,Overview 概述,New EnvironmentOne Forecast Opinion 新的环境统一的预测观点Provide CEO, CFO, and Chief Risk Officer with an alternative to Line-of-Business forecasts 除业务线预测之外，向首席执行官、财务总监和风险总监提供另一个选择 Forecasting based on a single view of the economy 根据对经济的单一看法进行预测 Ability to run numerous simulation and sensitivity analyses 能够进行大量的模拟和敏感性分析 Synchronized loan and interest income forecast with the Corporate Treasury 贷款和利息收入预测与集团司库部同步 Objective 目标The main objective is to produce a forecast of the credit loss associated with the Consumer Credit Card taking into account the following factors: 主要目标是在考虑以下因素的前提下，提供与个人信用卡有关的信用损失预测Underlying macroeconomic indicators 相关的宏观经济指标 The banking industry credit environment 银行业的信用环境 Migration of banks loans along the delinquency bucket spectrum (including Bankruptcy/Deceased status and Charge-offs) 银行贷款沿逾期档自低向高的变动（包括破产/状况恶化和撇账）Models Produced for Commercial Products 开发用于商业产品的模型Macroeconomic-based loss forecast models have been developed (for combined Bank of America and Fleet legacy portfolios: 已经开发基于宏观经济的损失预测模型（针对美国银行和Fleet两家公司合并前的组合）：Legacy Bank of AmericaCredit Card (Total Managed) 美国银行合并前信用卡（管理的全部信用卡贷款） Legacy FleetCredit Card (Total Managed) Fleet 合并前信用卡（管理的全部信用卡贷款） Legacy FleetCredit Card (Master Trust) Fleet 合并前信用卡（主信托结构）Legacy MiBNAConsumer Card-US (Total Managed, Securitized) MiBNA合并前美国个人信用卡（管理的全部信用卡贷款，证券化） Legacy MiBNAConsumer Card-Europe (Total Managed, Securitized) MiBNA合并前欧洲个人信用卡（管理的全部信用卡贷款，证券化） Legacy MiBNAConsumer Card-Canada (Total Managed, Securitized) MiBNA合并前加拿大个人信用卡（管理的全部信用卡贷款，证券化）,Main Components of the Loss Model 损失模型的主要组成部分,Step 1 : Establishing the Future Credit Environment 第1步：确立远期信用环境Using forecasted economic data, estimate the future Consumer Credit Cycle Index (CCCI). 使用预测的经济数据，估算远期的消费者信用周期指数（CCCI）Step 2 : Applying the Credit Environment to the Banks Portfolio 第2步：对银行的信用组合应用信用环境Derive future delinquency migration matrices based on the forecasted CCCI. 根据预测的CCCI导出远期的逾期变动矩阵Step 3 : Forecasting the Banks Credit Quality, and Delinquencies 第3步：预测银行的信用质量和贷款逾期情况Multiply current delinquency bucket distribution by forecasted migration matrices to arrive at the future delinquency dollar distribution. 用预测的变动矩阵乘以当前的逾期档分布，得出远期的逾期金额分布。Account for new business, and pay-downs, and align with Corporate Treasury, Line of Business Finance, as well as Securitization Finance overall loan growth targets. 在考虑新业务和现金支付情况的前提下，就贷款增长的总体目标与集团司库部、业务线财务部以及证券化融资部取得一致。Step 4 : Forecast the Credit Loss 第4步：预测信用损失For “defaulted” loans, apply “Severity of Loss” assumptions (or one minus recovery rate). 对“违约”贷款，应用“损失严重度”假设（或1减去回收率）。Produce a projection of “potential credit loss” for future time-periods. 得出远期“潜在信用损失”的预测值。,Loss Forecast Process 损失预测流程,Step 1 : Establishing the Future Credit Environment 第1步：确立远期信用环境Step 2 : Applying the Credit Environment to the Banks Portfolio 第2步：对银行的信用组合应用信用环境Step 3 : Forecasting the Banks Credit Quality & Delinquencies 第3步：预测银行的信用质量和贷款逾期情况Step 4 : Forecast the Credit Loss 第4步：预测信用损失,Consumer Credit Cycle IndexDomestic US 消费者信用周期指数美国国内,CCCI indicates the credit state of the consumer credit card market as a whole CCCI 反映个人信用卡市场的总体信用状况Due to the differences noticeable between the credit environment of the US and other countries, a region-specific credit cycle index was constructed for the US, Canada, and Europe credit card portfolios. 由于美国和其他国家的信用环境有明显的差异，因此针对美国、加拿大和欧洲的信用卡组合分别构造可地区性信用周期指数。The Index is designed to be: 指数的设计原则： “Positive”, for good times, indicating lower levels of downgrading and defaults, and a higher upgrading probability, than average 在景气好的时期，指数为“正”，表明与平均水平相比，信用等级降低和违约处于较低水平，信用等级升高的可能性较高“Negative”, for bad times, implying higher levels of downgrading and defaults, and a lower upgrading probability, than average 在景气不好的时期，指数为“负”，表明与平均水平相比，信用等级降低和违约处于较高水平，信用等级升高的可能性较低,