征信机构产品在信用卡业务中的应用

1、征信机构产品在信用卡业务中的应用 Roles of Credit Bureau in Credit Card MarketExperian Scorex邹台虹 博士Dr. Robert TsouPage 2提纲 Agenda征信机构简介 Bureau Introduction信用卡生命周期 Credit Card Life Cycle征信机构工具 Bureau based Tools预先筛选名单提供者 Pre-screening List Provider信贷政策/战略决策 Credit Policy/Strategy Decisions信用卡申请欺诈甄别 Application Fraud

2、Detection记分卡 Scorecard监控服务 Monitoring Services通知服务触发因素 Notification Services Trigger商业智能服务 Business Intelligence Services总结 SummaryPage 3各国征信机构实例 Some Credit Bureau Profiles 美国U.S.Experian, Equifax, TU所有正面信息 Full Positive英国 UKExperian, Equifax所有正面信息 Full Positive德国 GermanySchufa, Burgel, CEG正面信息 Pos

3、itive法国 FranceB de F, Experian负面信息、欺诈信息 Negative, fraud意大利 ItalyCRIF, Experian正面信息 Positive西班牙 SpainExperian, Equifax 负面信息 Negative荷兰 NetherlandsBKR, Experian 部分正面信息 Limited positive丹麦 DenmarkExperian负面信息 Negative挪威 NorwayExperian, D&B 负面信息、纳税信息 Negative, tax 瑞典 SwedenUC负面信息、纳税信息 Negative, tax澳大利亚 A

4、ustraliaBaycorp负面信息 Negative中国台湾 Taiwan/ChinaJCIC正面信息 Positive国家/地区 Country/Region征信机构名称 Bureau信息类型 Type of Information波兰 PortlandBIK正面信息 PositivePage 4 征信机构数据的效力 Power of Bureau Data坏账减少程度：无征信机构数据与包括了正负面征信机构数据的比较 Bad debt reduction: No bureau vs. full bureau正负面征信机构数据 Positive & negative bureau data

5、+ 只有负面征信机构数据 + Negative bureau data 无征信机构数据，内部评分 No bureau data, Internal scoring18个月后的表现 Performance after 18 months拒绝个人贷款40% Personal Loans Reject 40%拒绝信用卡33% Credit Cards Reject 33%拒绝支票账户15% Cheque Accounts Reject 15%8% 坏账 bad7% 坏账bad9% 坏账 bad7% 坏账 bad6% 坏账 bad45+%5% 坏账 bad4% 坏账bad4% 坏账 bad5% 坏账b

6、ad40+%38%以英国为例 UK Example :Page 5美国征信局的数据 U.S. Bureau Data征信局数据 Bureau Data每家征信局都有自己的“黑匣子”来进行数据处理 Each bureau has its own “black box” for data processing信息来自于银行、零售、按揭贷款、汽车、金融（财务公司）、保险、公用事业、企业个人名录服务和医疗等领域 Information across banking, retail, mortgage, auto, finance, insurance, utility, catalog, medica

7、l etc. 数据由查询部门、贸易往来部门、公共记录等部分组成 The data composed with inquire sectors, trade line sectors, public records sectors每个数据主体都有多条记录 Multiple records for each individual反映一个特定时间的记录快照 One time snapshot无支付信息 No payment information反映24个月的整体网格状态 24 month grid status Page 6信用卡生命周期管理 Credit Card Life Cycle Mana

8、gement征信机构数据提供了风险、市场营销、运营等工作所需要的信息，以帮助用户管理消费者的信贷生命周期 Credit bureau data provide information for Risk, Marketing, Operation to manage the consumers credit life cycle.时间 TIME0利润 Profit营销（寻找潜在客户） Prospecting营销对象 Whom to solicit?提供何种产品？ Which offer?信用卡种类 Card type利率/价格 Rate/price数量 Amount条款 Terms 市场研究 M

9、arket research业务发起 Origination 信用卡申请欺诈 Application Fraud 授信额度 Credit Limit? 利率/价格 Rate/Price? 条款 Terms? 激活 Activate?账户管理 Account Management降低损失 Loss Mitigation哪种产品/服务 Which products/ services?风险定价 Risk base re-pricing授信额度增加/减少 CLI/CLD授权 Authorize?重发卡 Reissue?资本金要求 Capital requirement?保留 / 退出 Retenti

10、on/Attrition保留对象 Whom to retain?保留成本 At what cost?收款 Collections处理策略 Which treatment strategy?对象 Whom to collect?外包 Outsourcing?商业决策 Business Decisions交叉销售 / 追加销售 Cross-sell/Up-sell营销对象 To whom?产品/服务 What products/ services?Page 7一些征信机构工具（即基于征信数据开发的工具） Some of Bureau Based Tools预先筛选名单提供者 Pre-screeni

11、ng List Provider信贷政策/战略决策 Credit Policy/Strategy Decision 信用卡申请欺诈甄别 Application Fraud Detection记分卡 Scorecard通用模型 Generic models定制模型 Custom models监控服务 Monitoring Services通知服务触发因素 Notification Services - Trigger商业智能服务 Business Intelligence ServicesPage 8 预先筛选（市场推广执行）过程 PRE-SCREENING CAMPAIGN EXECUTION

12、风险和市场营销方面的筛选标准Risk & MarketingSelection Criteria推广活动数据集市CampaignMarts按姓名和地址因素进行最后排除Name & Address AppendFinal Suppressions筛选实施过程 CampaignExecutionProcess执行过程 FulfillmentProcess产出结果 ProcessOutput得出结果以进行推广 Output Files forCampaign ExecutionDB2征信机构数据 Bureau Data征信机构作为名单提供者 Bureau as List Provider信息汇总 I

13、nformationAggregated Page 9预先筛选名单提供者 Pre-screening List Provider信贷政策/战略决策 Credit Policy/Strategy Decision 信用卡申请欺诈甄别 Application Fraud Detection记分卡 Scorecard通用模型 Generic models定制模型 Custom models监控服务 Monitoring Services通知服务触发因素 Notification Services - Trigger商业智能服务 Business Intelligence Services一些征信机构

14、工具 Some of Bureau Based ToolsPage 10信贷政策和战略决策 Credit Policy and Strategy Decision 征信机构产生的属性被各类金融机构用于制定信贷政策和战略 Credit bureau derived attributes have been used to define the credit policy and strategies on various financial industry.征信机构属性在信用卡方面的典型应用包括 Typical application on the credit card were业务发起贷款

15、发放决定、额度分配、风险定价、产品分配等 Origination - underwriting decision, line assignment, risk based pricing, product assignment etc.组合管理授权、额度管理、根据风险重新定价、重发卡等 Portfolio Management authorization, line management, risk based re-pricing, reissue etc.客户保留/客户消耗（退出）交叉销售、追加销售、常规联系、定价等 Retention/Attrition cross sell, up s

16、ell, call routine, pricing etc. 收款前期、后期、收款渠道选择等 Collections early stage, late stage, channel selection etc. 市场营销BT、潜在销售机会选择、销售机会优化 Marketing BT, lead selection, lead optimizationPage 11信贷政策和战略决策 Credit Policy and Strategy Decision发放 Origination组合管理Portfolio Management保留/消耗 Retention/Attrition收款 Coll

17、ection信贷生命周期 Credit Life Cycle属性举例 Example of Attributes提早收款 Early Collection 过去12个月出现报损交易的数量 Number of derogatory trades in the last 12 months 距上次公共记录的时间 Time since last public-record 目前当期未完结交易的百分比 Percent of presently current open trades免收费用 Fee Waiver 信用卡开卡总数 Total number of open credit cards 其它信

18、用卡的利用率 Utilization ratio on other credit cards 可用额度 Available credit (OTB) 发生严重还款拖欠的次数 Number of major delinquencies提高授信额度 Credit Line Increase (CLI) 可用额度 Available credit (OTB) 一张信用卡的最高信贷额度 Maximum credit limit on a credit card 平均帐龄 Average age of accounts销售机会选择 Lead Selection 信用卡开卡总数 Total number

19、of open credit cards 信用卡查询总数 Total number of credit card inquires 距上次信用卡还款拖欠的时间 Time since last delinquency on a credit cardPage 12预先筛选名单提供者 Pre-screening List Provider信贷政策/战略决策 Credit Policy/Strategy Decision 信用卡申请欺诈甄别 Application Fraud Detection记分卡 Scorecard通用模型 Generic models定制模型 Custom models监控服

20、务 Monitoring Services通知服务触发因素 Notification Services - Trigger商业智能服务 Business Intelligence Services一些征信机构工具 Some of Bureau Based ToolsPage 13欺诈解决方案 Fraud Solution数据来源： APACS 2005 Source APACS 2005欺诈造成的损失（百万欧元）遗失和被盗没有收到邮件假信用卡身份盗窃未见卡交易Page 14信用卡申请时的欺诈防范 Application Fraud Solutions欺诈类型 Type of Fraud篡改申请

21、信息 Application manipulation邮件寄出但没有收到 Mailed not received身份盗窃 Identity Theft假冒已故使用者 Deceased Impersonation首次付款违约 First Payment Default结合规律与模型 Combination of Rule based and models争取把欺诈扼杀在摇篮中 Stopping fraud before it starts持续的监控能保证欺诈甄别的质量和准确性 Constant monitoring maintains quality and accuracy of fraud

22、detection当前申请以往申请Page 15欺诈申请表筛选 Fraud Application form screening将申请表内容与以往的申请表内容进行仔细对照检查 Cross-check application details with previous applications信用卡申请欺诈Application Fraud仔细审核申请表各细节，力图发现自相矛盾的地方 Check details on the application form to highlight inconsistencies当前申请以往申请Page 16预先筛选名单提供者 Pre-screening Li

23、st Provider信贷政策/战略决策 Credit Policy/Strategy Decision 信用卡申请欺诈甄别 Application Fraud Detection记分卡 Scorecard通用模型 Generic models定制模型 Custom models监控服务 Monitoring Services通知服务触发因素 Notification Services - Trigger商业智能服务 Business Intelligence Services一些征信机构工具 Some of Bureau Based ToolsPage 17征信机构记分卡 Bureau Ba

24、sed Scorecards 征信机构数据被广泛应用于创建统计模型，以帮助信贷生命周期决策 Credit Bureau data is widely used in developing statistical models to aid the decisions of credit life cycle典型的通用记分卡 Typical generic scorecards风险 Risk 破产 Bankruptcy 回应 Response负债 Indebtness 细分行业记分卡和其它市场细分记分卡 Industry specific/Segmentation scorecards信用卡风险

25、 CC Risk按揭贷款 Mortgage汽车 Auto 次级客户 Sub-prime Page 18不同行业的征信机构通用评分 Generic Bureau Score on Various Industry 英国征信局通用评分的表现 Performance results of Generic bureau scores at U.K.垂直市场 Vertical Markets 基尼系数 Gini Co-efficients零售 Retail84.9信用卡和零售银行业务 Credit Card & Retail Banking 81.9电信 Telcos.64.6保险 Insurance4

26、1.8次级 Sub-prime35.6Page 19 Basel II Accord Part H Solution for Requirements围绕信贷生命周期的各类定制模型 Various Custom Models for Credit Life Cycle增强活跃程度和使用次数 Increase activation and usage行为记分卡 Behaviour Scorecard触发因素/事件记分卡 Trigger/Event Scorecard盈利性记分卡 Profitability Scorecard交易欺诈记分卡 Transaction Fraud Scorecard客

27、户价值最大化 Maximise customer value偏好记分卡 Propensity Scorecard回应记分卡 Response Scorecard提高收款和坏帐回收额 Improve collections and recovery values 收款记分卡 Collection Scorecard坏帐回收记分卡 Recovery Scorecard使保留收入最大化 Maximise retained revenues保留/消耗记分卡 Retention / Attrition Scorecard再激活记分卡 Reactivation Scorecard通过吸引新客户来实现市场份

28、额和盈利的最大化 Maximise market share and profitability by attracting new customers预先筛选记分卡 Pre-Screen Scorecard回应/收入记分卡 Response / Revenue Scorecard新客户登记 Registering new customers贷款申请记分卡 Application Scorecard破产记分卡 Bankruptcy Scorecard欺诈记分卡 Fraud Scorecard营销新业务收款 回收账户管理保留 再激活交叉销售 追加销售Page 20二维风险级别 Two-Dimen

29、sional Risk Grading根据通用和定制风险模型的二维矩阵形成战略和客户机会选择 Strategy and lead selection based on 2-dimensional matrix of Generic and Custom Risk model 高风险High Risk边际风险 Marginal Risk低风险 Low Risk以上：申请和征信机构通用评分的好/坏几率表Above: Good/Bad odds tabulation of application and generic bureau scoreDelphi 评分未达到申请评分Page 21用于客户管

30、理的征信机构评分 Credit Bureau Scores for Customer Management交叉销售和追加销售 Cross-sell & Up-sell信贷偏好评分 Credit Propensity Score个人贷款偏好评分（0.5%至20%）Personal Loan Propensity Score (0.5% to 20%)得分 Score 十分位 Deciles在之后6个月内余额显著增加的百分比 % with Significant Balance Increases in next 6m12.5%24.6%36.3%47.8%511.7%616.1%721.2%82

31、7.8%935.1%1048.2%Page 22预先筛选名单提供者 Pre-screening List Provider信贷政策/战略决策 Credit Policy/Strategy Decision 信用卡申请欺诈甄别 Application Fraud Detection记分卡 Scorecard通用模型 Generic models定制模型 Custom models监控服务 Monitoring Services通知服务触发因素 Notification Services - Trigger商业智能服务 Business Intelligence Services一些征信机构工具

32、Some of Bureau Based ToolsPage 23信用卡数据库 Credit Card Data Warehouse信用卡分析数据集市 Credit Cards Analytical Data Mart征信机构原始数据 Credit Bureau Raw data征信机构汇总程序 Bureau Aggregation program征信机构属性 Bureau AttributesOthersMCVISA信用卡交易信息 CC Transactional Information决策引擎 Decision Engine战略 Strategies行动 Actions产品线管理Line

33、Management根据风险重新定价Risk Base Re-pricing客户关怀Customer Care交叉销售/追加销售Cross-Sell/Up Sell市场促销Marketing Promotion12123代表进行账户管理的主要输入信息 Represents the key inputs for Account Management风险和运营信息 Risk and Operational Information风险管理平台 Risk Management Platform其它产品数据库 Other Product Data Warehouse其它产品分析数据集市 Other Pr

34、oduct Analytical Data Mart其它产品关系Other product Relationship6456征信机构信息Bureau Information申请资料Application Information3支付信息 Payment Information4促销资料Promotional Information5记分卡 Scorecards组合管理的典型数据来源 Typical Data Sources for Portfolio Management 维萨卡其它卡万事达卡Page 24预先筛选名单提供者 Pre-screening List Provider信贷政策/战略

35、决策 Credit Policy/Strategy Decision 信用卡申请欺诈甄别 Application Fraud Detection记分卡 Scorecard通用模型 Generic models定制模型 Custom models监控服务 Monitoring Services通知服务触发因素 Notification Services - Trigger商业智能服务 Business Intelligence Services一些征信机构工具 Some of Bureau Based ToolsPage 25通知服务 Notification Services 账户审核和预先筛

36、选每日传送数据 Account review and prescreening - data delivered daily根据客户确定的“触发因素”产生结果，这些触发因素是可以改变和管理的 Output based on “Triggers” established by the client, which can be changed and managed用途 Used for the purposes of:降低损失 Loss mitigation保留现有帐户 Retaining Existing Accounts交叉销售/追加销售 Cross-Selling/Up-selling营销

37、 ProspectingPage 26通过及时发现事件和早期预警来控制风险 Control risk via immediate identification and early warning降低损失 Loss Mitigation为正在积极从你的竞争对手处寻求更多信贷的现有客户提供新产品 Offer new products to existing customers that are actively looking for additional credit from your competitors 交叉销售 Cross-Sell通过识别那些正在寻找新信贷产品的现有客户，来应对这些现

38、有客户不断变化的新需求 Address the evolving needs of existing customers by identifying those shopping for new credit追加销售 Up-Sell在客户接受其它竞争对手的产品之前，发现这些潜在的客户耗减情况 Identify potential attriters before they accept a competing offer 保留 Retention 营销 Prospecting通知服务日触发因素解决方案 Notification Services - Daily Trigger Solutio

39、ns 及时发现市场中适合本银行某类产品的潜在客户 Immediately identify prospects who are in the Market for one of your product linesPage 27通知服务的测试结果 Notification Services test results在6个月内对168,763名客户实施了监控 168,763 consumers monitored over 6 month period个人银行卡账户 Personal Bank Card Accounts70,043每日发出通知数 Total Daily Notices prod

40、uced60,268与客户保留相关的“触发因素” Retention Triggers9,775 与风险相关的“触发因素” Risk Triggers 比较两个可能发生不良行为时的余额：即帐户审核日和收到通知日 Compared balances at account review date for adverse action vs. date notification received by NS for adverse action明确每个帐户根据风险触发服务而可能产生的节余（规避的损失额） Determined potential savings for each account by

41、 risk trigger从168,763个监控账户中共规避损失2,179,174美元 Total overall savings of $2,179,174 for the 168,763 accounts monitoredPage 28触发因素和帐户表现恶化之间的时间差 Timing between trigger and degraded performance在那些有触发通知服务且出现账户表现恶化的银行D帐户中，61%的帐户是在通知后的3个月内就出现了账户表现恶化的现象 Of the triggered accounts that have degraded performance

42、on their Bank D account 61% do so within 3 month of the trigger date触发因素的通知时间与银行账户表现恶化之间的时间（月份）存在触发通知服务且出现银行账户表现恶化的比例 Page 29预先筛选名单提供者 Pre-screening List Provider信贷政策/战略决策 Credit Policy/Strategy Decision 信用卡申请欺诈甄别 Application Fraud Detection记分卡 Scorecard通用模型 Generic models定制模型 Custom models监控服务 Moni

43、toring Services通知服务触发因素 Notification Services - Trigger商业智能服务 Business Intelligence Services一些征信机构工具 Some of Bureau Based ToolsPage 30商业智能服务 Business Intelligence Services商业智能服务是定制的、全面的顾问式服务，它通过以下方式使金融机构能够更好地认识其客户基础 Business Intelligence is a customized, comprehensive, consultative offering that all

44、ows organization to better understand their customer base from a variety of perspectives:使金融机构能将自身与整个市场和某一特定竞争群体进行对比 Ability to contrast your organization to the overall market and specific competitor groups基于分析而得出结论 Analytically derived conclusions将新客户与现有客户进行对比 Contrast new versus existing customer

45、s根据分析找出长期趋势 Trend the analysis over time按一个构建的客户形态来识别潜在客户机会 Profile customers versus prospectsPage 31商业智能服务内容 Business Intelligence Flavors示例分析包括 Example analyses include:市场份额分析 Market Share analysis 形态分析 Profiling analysis 拖欠分析 Delinquency analysis 产品组合分析 Product Mix analysis 交叉销售分析 Cross-sell anal

46、ysis 债务移动/消耗分析 Debt Movement / Attrition analysis 评分范围客户同类人群比较排除总计Page 32总结 Summary 征信行业展望 Industrys expectation:提供增值产品，而不仅仅是信用报告 Value added products instead of credit reporting从批量到（接近）或实时市场营销 Move from batch to (near) or real-time marketing提供针对不同行业的量身定制的解决方案 Industry specific solution更频繁地监控贷款组合 Mo

47、nitor the portfolio more frequently一体化数据解决方案 Integrated data solution以事件为基础的决策和相关行动 Event-based decisions and resulting actions客户可以从自己的桌面电脑直接获得重要的战略和市场推广开发工具 Client desktop access to critical strategy and campaign development tools提供针对性强的定制产品服务 Deliver high targeted and tailored offers从风险缓释战略转向为客户的整

48、个生命周期提供增值服务 Evolve from risk mitigation strategies to life-time value focus关注新兴的消费者 Focus on emerging consumersPage 33Questions?问答 Q & A7o1Es-Q5xn7e6c%P1k&q4v&()sMgA*(+yZD+J$f1SZQYIy!h+Fu-L$aXrgWF*oe-7+6YK-h$p4w(+1xSoJrolVtNgEk(9qipgvD9S-qA*g)VA5(EoQb3Z+%2WK0ly+iYmhcOrUw-&LeIpZDZg6!1znAF9P%ipSyU7Ywq

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50、UrX*%7qaNGo)VWCK0AS7qaX4KqqzA(HcnUREIbv4LLNAhu)C71v+0zb&Ok%uaXyHFoq7XoO9WW0WgaKPkF18%Xq%!TZ!13%YSZ)fO6IQxNncr6P*X7Pe1Ardw5E9GF$htU#QN-jGY1FF)x+(AiYb!XSgKsH%Fe8&6witw)&TxtvLl*a-(LD2EF0ROxEG7M#f6KnDTs!K9U0j-FAd4E9dcEO58h%D#0#24B-+aAtyb!5jWGa4Gj$RM&3#j6TQ2ePc5FwF+NwRE1WrXzJO&w8tFnrepPJp1(7VpU1S9vI684sy

51、pRUUIUJ+bcbLOt+zy#Xgv+3nfLACLgdmGGzKbv#iMO7iNs(%1dWxnj4axG1beq+RaLkp%kD*QMzcJDIczypoFhS!l*dNF7z5&mm*Y*tWkK*4$AIy&MY2AUrm$21FlgB0(7MJ4+g9IsyM!PR9xndGToldDyvfi)On(bBGBnQa2V$SW0j2#o1wg%4g0(IGuNERVcjDvqUpNhGk%znEHDFyXmqky3-m!250!*!$3INk8Yhryya(jRQUi#jKh5JmC$I$dYvdOuKDbjj&SezGKXdJbRp#&Qz#AXx7VdyK%cCj!53y

52、N%3%F+RNu(DuHOqfY*U3Tf3$i(JHgg-E-8icP(5(QAi9lC0&dYDFbdrlvoGc(qaIwg89%+z5KKz#0+rRLU7cvolSa$vgMgmpW7M*omlP1I)XK+vBMF9T&v(KtK0%Jm9X06bdQ3TqQ)itO!TFPGv4gZOs90WEid#QhSOOAxjkdUo-XEl$l46YxwRvqmYJI!dJ+sCWcwTzgtuULOwccmZT*NU49zsxcLD7FIGH(Hiddsb5rT(z6*ZY!5HTmdPp)Xj$hF5afR9LfGp5nKJYXbub(p*k&356BWsOkf9Gb7JpRntE

53、ab398*w5mk(YcONx%5utS!*8E(wcW2vhb5B7UYoaw%7#LMJU%VzK6SaM9%ByK01HFTXzcV&84zuBqAUjL!&aD2j)od2%)76ecEL4NnBQ)jg6c*Om*c$Tp6rV3uzW(z-5WUCJKaNZdkM)W5TmaO2PrCLNx+rO)5DI7EZU+OQuth)SF)91SLZ43qGdZ4x&jcLS!#n01RP&UzQ$6It9kDz3boTCL4Sf006e-9Y+3XkGzc0sE8fL2O9bMm+ViQz-!KqVur60(ogdUF91V39ig-2(kHzAeb*6kL*EyZFfb+ef&*Lm

54、ztzgcbxS(t0MsscOdcHNrrbCnH9Po-LfR*5Tqy98gakt&dIa#W9SE4YsVgZ5c+H(e7#ikmUFmu#83ca8cq1ONOsooc4uPbR7XxFQAxbc4sQKOxArMET!mADBuYd6UR9umClYsY2#VtDRl16VM+JDTB4MbEzbLwXY2&VcK495SM5nGBLSa6Zzg2NB(hhpuO&BbtN%(&jB3pw!LSxoyCP2mbaXLZKxR&vOckw9RPzAd-aY9GTi*dvmQYl8Dux%al4mjNh5QXj-dFiKOjwrPHm6ff&W$tHCBO9w!mjXS0KKE4DP

55、abiRfxGtI3yAEnJWqzW7hWKm41vS7N5XBO)HCNAw&qf%#D6NkvOLIGcWCz8D&)qQqpbgy$8NLq5b3%76yhf4imEFYaFmJ%BsVh)&D&7SpQm-Qws4ZKxf1VjV8PffcN7Qt!TYw)O6kY895sB!%3P95czqs+HMHZZuIUwk!iZo0pbq4UNU1qem6qyi+eaVw3zZiKQy08Hc29XwBHKrT9fJK6%B5YKHORiT&epQ-U6N6hZJz6gx6(1PzRC(F)Mjou7*ApulcAPg(AvcTklKUhZxrH$+R94xrHosVL#6VCuSqC1m

56、*FJUKuy)O#VXbTNEQMV(lFQjHo9Iul9aDyCoQNLZU#Dg3)Dq+-$lT!dhJ%Y5Pe)1sezbN&nDpVQ!WVw%Z9UHfdT)VrWqhY#)7gz5C0TxR*CDDJhepwKshE28O*+Nhp+pVEicR6%TfEcx$!#jD8t#&1F1Wqn*xA$chE$Vo)xHw$D)8bqpo*oc(U6UrW1ESz38q-dG+zhj2fxcxBgPLxNcb7bHTswfNVe-lJt5wQXlQLNph$%XfLO&$odH1H$nfOggaq6$vnP8X7bGniaM9Q4*GXb*hTo!%Tt09un)nZmYZ5OJ

57、xG3Ne%bvmLMCz5R%y0Qe0OZ1VPql5k1jtZ400(6DpzMl#zwyRdB#MP3fTxts3*95!lX4zYs857vvJK+G44)D%xOWKZvSz$ADWb2Isp22%f&31la)es8(voW*8J0+mqqBaO-q0I6Q5GFEajJwC&ry(Kc&DS#XEvAukh4wFdgiBHHNTjU-p(z9n0u0m$oe3v(bH#A7f7jgtfkY4#z5zP-fAD3xUa&ujcde+N69(727XLUmrGpz3sXqP7XK2T66abM&J4Na#fMFy(E#vZLvRbgCJt1aeXG7DWL0VQ4qm5GqhU$ifnHo9QFtKQWeD88PXF(lNC6syyteS+Hmkl1&Z#-Kkbi5daC$WYDBl3*nIkP3Qd9t8+!)k-SAvt(WM!-f!eI65fA7vg7tFt)QPAtGqnocdkzQybN0*QvIIUU3+6QrwsJHRyd(Vnu!-lIzyk96%bWwsAG7P5FHZs8*$OQa9RLxO9rj4gfRff3mw2Mb1-Z0qpuA+7sxY8YG&u#