CDS’ experience from banking practice

CDS’ experience from banking practice Loan Portfolio Fair Value Assessment Istanbul - May 2013

AGENDA Background Life time default estimation • Analytical framework • Main Results Expected cash flow estimation • Analytical framework • Discount rates • Early repayment rate • Portfolio pricing • Benchmark analysis • Conclusions

Banks are using these operations as a vehicle get funds from the ECB: the securitized portfolio is eligible as collateral from ECB. Internal securitization WHY CONSIDER LOAN PORTFOLIO VALUATION? • The complementary application of Life time cash flow estimation is the provision for credit losses estimation. Provisions can be set on the basis of the current portfolio status (default, not default) although many banks are starting to determine provisions also on the basis of estimation. Provision estimation • This is a classical method that Banks and Financial houses use to get funds. The crisis has created a very interesting disposal market fed by companies specialized in debt collection. Asset Disposal Due diligence • Expected cash flows can be used to review the credit portfolio governance and management, and get preliminary estimates of pricing and profitability of the portfolio, in accordance with Group portfolio management strategy (i.e. run-off) Benchmarking • Banks, especially at the Headquarter level, need to benchmark a loan portfolio’s lifetime estimation versus the system, aiming to verify the robustness of the estimation, or to highlight particular characteristics of the managed portfolio. 3

The Bank requested to CRIF to support them in a value assessment of their consumer loan portfolio, prior to an internal securitization operation. Internal securitization CASE STUDY: BUSINESS NEEDS • The complementary application of Life time cash flow estimation is the provision for credit losses estimation. Provisions can be set on the basis of the current portfolio status (default, not default) although many banks are starting to determine provisions also on the basis of estimation. Provision estimation • This is a classical method that Banks and Financial houses use to get funds. The crisis has created a very interesting disposal market fed by companies specialized in debt collection. Asset Disposal Due diligence • Expected cash flows can be used to review the credit portfolio governance and management, and get preliminary estimates of pricing and profitability of the portfolio, in accordance with Group portfolio management strategy (i.e. run-off) Benchmarking • Banks, especially at the Headquarter level, need to benchmark a loan portfolio’s lifetime estimation versus the system, aiming to verify the robustness of the estimation, or to highlight particular characteristics of the managed portfolio. 4

PROJECT APPROACH • In order to estimate the life time risk on the securitized portfolio, CRIF defined a framework that, through the Markov chain approach, makes possible the LTD estimation on a time window of 10 years. 1 • Customer: Italian subsidiary of leading international banking group • Main goal: • Calculating portfolio’s expected value & pricing before undertaking the decision if running or not the securitization (by the HQ) • Assessing the impact of the cost of funding of the bank into the expected value of the portfolio • Identifying high/low performing tranches of the portfolio, leveraging even on external data (CRIF Credit Bureau) Life time default (LTD) estimation • In order to estimate the securitized portfolio value we estimated the future cash flows splitting the portfolio by product (personal loans, consumer loans and auto loans) and level of risk (tranching). • The specific LTD, LGD and early repayment rate estimations together with the discount rates have contributed to determine the future cash flow estimation 2 Expected cash flow estimation • To build similar portfolios based on generic samples retrieved from the Italian Credit Bureau. On the basis of these samples we computed the benchmarking LTD curves. 3 Benchmarking parameter estimation 5

LIFE TIME DEFAULT ESTIMATION: ANALYTICAL FRAMEWORK The Life Time Default (LTD) estimation is part of the multiperiod estimation problems, in which the evolution from an initial state to a final state passes through "n" intermediate observable states, precisely, over several periods. The technique used by CRIF is based on "Markov chains". Credit scoring uses snapshots of historical information, observations and outcomes, to develop a risk-ranking tool. For behavioral-risk scoring, most models will use a one-year outcome. Then to determine the probability of an account going bad, or defaulting, over any given period, historical information is again used to work out the rates. But what if percentages are needed within, or beyond, the one-year period, or whatever period which was used for the scorecard? Markov chain allows the business to predict the future distribution (default/non default) using only the current distribution (without default) and a transition matrix indicating the expected movements between states. Multi period frame Risk 7

LIFE TIME DEFAULT ESTIMATION-ANALITICAL FRAMEWORK 1 Objective • Starting from the reference date of December 2012 the objective was of estimating LTD on the outstanding loans present at that time in the “customer” portfolio. 2 • The base of Markov chain estimation consists of building transition matrices that, on the basis of the past experience, can be used in order to extrapolate the future risk • The life time risk extrapolation is based on two simple assumptions: • When a contract is classified as defaulted do not change the status any more. • The probability to change status along the life time is constant and the transition matrix represents these probabilities. • Therefore the simple iteration of the transition matrix per the number of periods that cover the whole residual life of the securitized loan portfolio is able to estimate the LTD. • For the “customer” were developed two transition processes able to estimate the marginal default probabilities over a time frame of 10 years (120 months) both for the personal loans and the auto loan portfolios. Methodological foundations Data sources • The analysis was realized on the loan portfolios provided by the “customer” , who provided the payment history of each loan useful to set the transition matrices on the basis of an internal default definition. • CRIF has integrated the “customer” historical loan portfolios with Credit Bureau Score in order to define a robust risk tranchingand coherent with the benchmarking activity. • The definition of default has been established on the basis of the following two variables: • Risk Class (number of insolvency in each period) • Contract Status (issued, closed, early pay off, write off, into areas, DBT) 3

LIFE TIME DEFAULT ESTIMATION-ANALYSIS AND DELIVERABLES Performed Analysis Deliverables Not defaulted personal loan portfolio at 06/2012 with performance at 12/2012 • Initially, it is analyzed the distribution of Perform with respect the default rate defined asthe target variable • The default status and the Credit Bureau Score tranches represented the possible moving statutes of each contract Default rate • The transition matrix of the performing personal loans portfolio is reported in the table. The matrix represents the transition probabilities of a contract on score tranches or the default status between two periods: • a tranche “A” contract in June 2012 has a 25.7% probability of going after six months in default, has a 55.9% probability of staying in tranche “A” Personal loan portfolio transition matrix (06/2012 – 12/2012) Trantion matrix • The two graphs show the evolution of marginal probabilities of default for each risk class, • Each point represents the percentage of additional loans at that time reaches the default status. • The curve shows a decreasing trend with an initial peaks more pronounced for riskier classes. LTD curves Personal Loans AutoLoans Rsik measurement

EXPECTED CASH FLOWS ESTIMATION-ANALITICAL FRAMEWORK • To estimate the expected cash flowsis necessary to build the amortization schedule of the loans from the evaluation date until the date of expiration, based on the amount of the installment, the residual debt and the annual rate (APR) provided by the bank on individual basis. • Once it were obtained the values of the residual capital and of the interest at any time t, it has been developed a binomial tree where at each subsequent time t (t = 1,2... until expiration), the probability of being current or default status is derived from the Markov process. • In case of default at the time t, the cash flows are represented by the installment collected up to t-1, with the addition of the recovered residual debt plus the installment gained in the period, all multiplied by the recovery rate: Revenuest = Installments+ (1-LGD) * (Installmentt +Residual Debtt) the amount of the installments already paid from 0 to s =t-1 current installment+the recovered residual debt at the default event. • In case of not default at the time t, revenues are represented by the sum of installments paid up to s = t-1, • with in addition the installment of the period t: Revenuest = Installments + Installmentt Expected cash flows is determined by the sum of the discounted expected revenues from the evaluation date until the date of expiration

Early repayment rates Discounts rates DISCOUNT RATES AND EARLY REPAYMENT RATE • For the evaluation of the expected cash flows, it was necessary to define the early repayment rate of a credit line,. • The repayment rate was measured, on annual basis, in the year 2012. The ratio is measured as the number of contracts that expire in advance during 2012, with respect to the number of contracts alive at the beginning of that year. • The early repayment rates are introduced into the cash flows analysis: grater is the repayment rate lower will be the expected cash flows. • Here we can see the early repayment rates chose for the Personal Loans portfolio. • Revenues resulting from the application of the algorithm are then discounted at each time t, In order to give an expected value of the portfolio that takes into account the cost of funding of the bank. • Liquidity curve = risk free rate + liquidity spread • Greater is the cost of funding lower will be the expected value of the portfolio Upper Bound Lower Bound Personal Loans Early repayment rate grouped by Perform tranches:

Portfolio pricing is determined by the ratio of the expected cash flows to the total balance. • As for bonds, portfolio priced over 100 are more profitable meanwhile portfolio priced under 100 are less profitable for the originators. • The tranching of the portfolios is defined on the basis of the CRIF Credit Bureau Score. The price is mainly determined by the risk profile and the structure of the amortization plan PORTFOLIO PRICING Pricing = Expected C.F./Total Balance A 98,2% B 103,1% >100 OK C 105,8% D 108,5% =100 E 108,9% F 109,6% KO <100 G 106,3% Total 108,0%

To compare and validate the results obtained on specific “customer” portfolios, CDS has developed a benchmarking analysis extracting two random samples, from Italian Credit Bureau, that has similar features to those of “customer” . The main purpose of the benchmarking analysis was to compare the risk detected in the Credit Bureau samples, to verify if the behavior and the structure of the “customer” portfolios are aligned or not with what is observed in the total market. As can be seen from the graphs, the risk curves by Credit Bureau Score bands that are obtained from the samples of the system show comparable trends to those defined on the portfolios of the customer BENCHMARKING ANALYSIS Comparison of Default Rate

BENCHMARKING ANALYSIS Customer and Credit Bureaus LTD curves comparison Credit Bureau sample Customer sample Personal loan Auto & consumer loans

CONCLUSIONS • CRIF can support banks in a preliminary assessment phase in order to identify the best strategy for securitization. Internal securitization • CRIF can help banks define the amount of provisions based on the expected lifetime risk or limited to fixed periods (e.g. 36 months), as required by the new IAS Regulation. Provision estimation • CRIFcan support banks in an evaluation phase of the portfolio or specific segments to handle the negotiations with potential buyers. Asset Disposal Due diligence • CRIF, on the basis of the expected cash flows estimation, can support Banks in reviewing the credit portfolio governance and management, and get preliminary estimates of pricing and profitability of the portfolio. Benchmarking • CRIF, where the Credit Bureau information is available, is able to compare the expected risk (life time or multi-period) of the Bank with those of the system, providing evidence of benchmarking useful in all areas listed above but, more generally, to assess whether the value of its portfolio is determined by a systemic context or from a specific credit management approach. 18

CDS’ experience from banking practice