成果讲稿3404之家week2-credit risk analysis

1、Lecture 2:Part one: Credit scoring techniques Reading: Sathye: Chapter Three1Learning objectives1. Explain the practical use of a credit scorecard2. Summarise the history of credit scoring3. Explain the growth in consumer credit4. Discuss why retail credit scoring has e so important5. Explain the pr

2、ocess of building a credit application scorecard6. Discuss the 4 Rs of credit scoring7. Discuss how credit scoring might evolve in the future2Introduction Credit scoring uses statistical analysis to determine the probable repayments of debts by consumers. A score is assigned to an individual based o

3、n information processed from a number of sources (summarise the risk in single number) It has revolutionised the granting of credit generally and consumer credit in particular An example of a credit application scorecard is covered And also how to build a credit application scorecard3Advantages of c

4、redit scoring Decision making process is consistent Mass of data can be condensed into a credit figure Can be easily shown on computer files Little need to keep re-examining paper workAn example of a credit application scorecard A credit scorecard from the Home Loan Experts website: score-home-loan/

5、credit-score-calculator/ Suggested by HLE for use by prospective home loan borrowers to generate a realistic credit score 12 questions in total The higher the score the lower the creditworthiness of the prospective borrower Refer to “Industry Insight” section for a detailed coverage of the scorecard

6、5 The key scoring factors in the credit scorecard Bankrupt = 100 points Discharged bankrupt = 50 points Loan-to-valuation ratio greater than 95% = 50 points Liabilities more than assets = 40 points More than 6 credit enquiries = 30 points Missed debt repayments in last 6 months = 25 points Borrowing

7、 more than $1m = 25 points No genuine savings = 20 points6Using the credit scorecard The worst possible borrower score = 328 points The best possible borrower score = -10 points Scores up to 35 points are classified as “low risk” Scores between 35 and 55 points are “medium risk” Scores above 55 poin

8、ts are classified as “high risk” These cut-off scores can be changed by the lender7History of credit scoring The average adult in the US is currently being credit scored once per week either on new or existing accounts Three phases of in the history of credit scoring: 1935 1959 Pioneers 1960 1979 Th

9、e Age of Automation 1980 + The Age of Expansion How does your occupation fit in with growth or soon to be obsolete areas in the economy?8How to build a credit application scorecard Credit application scorecards are the main type of scorecard Other types: Behavioural scoring Collections scoring Custo

10、mer score Bureau score9Growth in consumer credit The link between the Global Financial Crisis (GFC) and the use of credit scoring in consumer credit 58% of bank lending in Australia is consumer credit Probably all credit card applications in Australia are credit scored Probably most housing loan app

11、lications are also credit scored Credit providers in Australia (since July 2012) can no longer send unsolicited invitations to customers (Unless you are already a bank customer)10Why has retail credit scoring e so important? The choice between judgemental credit decisions versus credit scoring The m

12、ove from relationship management to transactional lending The desire of banks to reduce costs and increase accuracy of lending decisions11The overall goal Collect historical information on all individuals who applied for credit over a 1 year period Usually start with 50 explanatory variables These a

13、re usually reduced to around 10 variables The dependent variable is the borrowers performance in the first year (“Good” less than 3 months of missed payments; “Bad” more than 3 months of missed payments Note that 3 months corresponds with APRAs impaired asset definition Unsuccessful applicants are u

14、sually included in this analysis Usually 1m+ applicants in a data set for a larger bank12Other considerations Data sample Data validation and cleaning Data segmentation Development sample and validation sample Reducing the number of variables in the model Coarse classifying characteristics (eg, reli

15、gious background) Regression analysis13Moving beyond credit application scorecards The 4 Rs of credit scoring Risk Response Retention Revenue The apparent link between insurance fraud and credit default14The future of credit scoring Building better credit scoring models (very dynamic over economic c

16、ycles and volatile if rich/poor gap widens) Incorporating economics and market conditions into credit scoring models Credit scoring models for Basel II (incorporation of minimum cash reserves to cover risk)15Part two: Credit risk analysis Reading: Sathye: Chapter four16Learning objectives1. Define c

17、redit risk2. Analyse various approaches to credit risk analysis3. Explain expert systems4. Carry out a five Cs analysis5. Ascertain credit risk from market-based spreads6. Describe various econometric processes7. Carry out a basic Altman analysis8. Describe hybrid systems of credit risk analysis9. L

18、ook at company data and carry out a basic credit analysis17Introduction The focus on credit risk has emerged over the last 1015 years Two key questions: What is credit risk? How do we analyse it? The vast range of new credit analysis tools should help minimise the dangers of institutional collapses1

19、8What is credit risk? Subtle difference between credit risk and default risk Credit risk: The risk of loss through the default on financial obligations Default risk: The risk that the issuer (borrower) will not fulfill its financial obligations to the investor/creditor in accordance with the terms o

20、f the obligation19To define credit risk properly, the obligations must be clear in the contract including:Defining the obligations of the borrowerDefining the obligations of the lenderDefining the payment dates for interest and principalIndicating the maturity date20 The lender is faced with three c

21、redit scenarios with any loan: The credit risk analysis occurring at the time the loan is made The assessment of the credit risk profile during the term of the loan The credit risk profile should the loan e a problem. For any borrower, the credit risk often changes over time (credit migration)21How

22、do we analyze credit risk? The tools used in credit risk analysis can vary considerably in complexity Credit risk analysis tools can be grouped in four main areas: 1 Expert Systems 2 Risk premium analysis (cost of risk) 3 Econometric Methods 4 Hybrid Systems22(1)Expert systems Despite the grandiose

23、title, expert systems tend to provide relatively simple computerised support to the decision- making process Often manually based and used to perform simple financial calculations including financial ratios (quite subjective, based on assessors opinion) Generally place much of the credit decision-ma

24、king on the individual lending officer Often used in smaller lending firms23Expert systems commonly used to support 5 Cs analysis1.Character: Is the borrower the type of person who would direct efforts to repayment or avoiding obligations?2.Capacity: Does the borrower have the capacity to enter into

25、 loan (e.g. minor)?3.Cash: Does the borrower have sufficient cashflows to support the loan repayments?4.Collateral: Can the assets supporting the loan be sold at a fair price in the event of default5.Conditions: Where are we in the economic cycle?24One alternative to 5 Cs is PARSER Personal characte

26、ristics of borrower Amount required and why Repayment capacity Security Expedience of future profitable opportunities Return from the loan25(2) Risk premium analysis Measuring credit risk can be inferred from the risk premium provided by the market on traded securities or ratings agency Risk premium

27、 measured as26irp1)1 (wherep = Probability of repaymentr= Interest rate on a corporate bondi= Risk-free rate Probability of repayment is then:27r1i1p The previous equations do not incorporate the proportion of the loan that may be recovered in the event of default, e, via collateral for example. As

28、the recoverable amount increases, the risk premium falls:i1)r1(p)p1(x) r1(e An alternative way of expressing the risk premium, pr, with recoverable assets is:28i)(pe)p(ei)(pr11 The previous equations have assumed a single period default horizon, e.g. one year. To assess the cumulative default probab

29、ility for multi-period loans (e.g. a three year loan) we can apply:np x p x p-1 y ProbabilitDefault Cumulative21(3)Econometric analysisRegression AnalysisApplied in three forms:Regression AnalysisAdvanced Regression AnalysisDiscriminant Analysis (separation into multiple groups)29Multiple regression

30、 Aims to forecast into the future by identifying significant variables that have previously had explanatory power Two types of data used. Firstly, the dependent variable, that is the one the model is attempting to forecast. Secondly, the independent (or explanatory variables) that provide explanator

31、y power for the dependent variable. Some technical econometric issues, such as variable selection, limit the value of strictly applying the multiple regression approach (usually where independent variables are similar)30Linear probability model (Probit) Variation on multiple regression analysis that

32、 divides variables into two samples, e.g. default and non-default, and assigning them values of 1 if the loan has defaulted and 0 if non-default. The division can then be regressed to determine significant variables as:31error1niiiiXPwhere pi=Probability of default = The estimate of importance of va

33、riable XiDiscriminant analysis This method highlights/discriminates against two groups, e.g. financially sound or financially distressed Applied in lending by Altman (1968) where ratio analysis applied to determine which firms in distress and which were not Became known as Altmans Z Score (Zeta Scor

34、e) which provided cut-off levels Z 1.81 Firm likely to default (DISTRESS ZONE) 1.81 Z 2.99 May or may not default (GREY ZONE) Z 2.99 Firm unlikely to default (SAFE ZONE)32Altmans Z Score Altmans Z Score given as33543210 . 16 . 03 . 34 . 12 . 1XXXXXZwhereX1 = Working Capital / Total AssetsX2 = Retain

35、ed Earnings / Total AssetsX3 = EBIT / Total AssetsX4 = Market Value Equity / Book Value of LiabilitiesX5 = Sales / Total Assets(4) Hybrid systems Despite the apparent rigour, the econometric methods discussed above provide little more than the statistical relationships between variables More recent

36、developments have drawn on broader sources of information and theory in assessing credit risk34 Expected Default Frequency Drawing on option theory, the borrowers payoff resembles a call option and the risk premium can be determined accordingly Mortality Models Examines real bond default data to est

37、ablish the proportion of bonds for a credit rating that actually did default35RatingCredit that in Issued Loans of Amount TotalDefaultsthat RatingCredit a in Loans of Amount TotalMMRtPutting it all together-ExampleText book,p.136 Facts: Hypothetical Retailer $10m loan application Listed on ASX, shar

38、e price $3.75 A rated with bonds trading at 7.5%, while comparable Government bonds trading at 5.9% EBIT was $156,298,000 on sales of $521,566,000 Inventory currently $61,001,0003637Item$Total Current Assets588,015,000Total Noncurrent Assets569,749,000Total Assets1,157,764,000Total Current Liabilities403,930,000Total Noncurrent Liabilities203,608,000Total Liabilities607,538,000Shareholders Equity550,326,000Retained Earnings278,893,000Number of Shares on Issue142,869,000 5 Cs: Cha