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Page 1 MFF UK – March 9, 2012 Risk management in banks Leoš Souček, Komerční banka.

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Presentation on theme: "Page 1 MFF UK – March 9, 2012 Risk management in banks Leoš Souček, Komerční banka."— Presentation transcript:

1 page 1 MFF UK – March 9, 2012 Risk management in banks Leoš Souček, Komerční banka

2 page 2 MFF UK – March 9, 2012 Introduction / Czech economic specifics…1/2  Private sector indebtedness at the lower end of EU levels Indebtedness of corporate sector at 22% of GDP Indebtedness of households 30% of GDP Dynamic growth of mass retail exposure (above 30% yoy in 2003- 2008) slow-downed in 2010/2011 (6.6% yoy in 07/2011)  Also public sector indebtedness lower than EU peers CZ at 38.5% end of 2010 (FR: 82%, DE: 83%, HU: 80%, PL: 55%, SK: 41%, IT: 119%, GR: 143%) Budget deficit narrowed to ‑ 4.7% GDP in 2010 (SK: ‑ 7.9%, HU: ‑ 4.3%, GR: ‑ 10.5%, FR: ‑ 7.0%, DE: ‑ 3.3%, AT: ‑ 4.8%)  Despite fragile political stability some progress in reforms, which help to secure fiscal sustainability Health care and pension reforms under progress S&P raised the LTFC Czech Republic rating to 'AA-'  No concrete plans on EUR adoption  Limited dependence on external financing Relative low level of external debt in terms of GDP External financing needs covered by FDI and EU funds

3 page 3 MFF UK – March 9, 2012 Introduction / Czech economic specifics…2/2  Limited share of private foreign currency debt 13.0% for private sector (in 7/2011, both residents and non residents) 17.4% for corporate, 0.14% for households (residents only) export-oriented corporates as main users 150% coverage ratio by foreign currency deposits (res. only)  Healthy banking sector Confirmed by CNB stress tests Sustained banking sector profitability since early 2000s Favorable loan-to-deposit ratio at 78% July 2011 Strongly capitalized (in end-June, 15.9% average regulatory capital ratio)  Growth largely relying on external factors … Share of nominal exports on GDP at 80% in 2010 84% of exports concentrated on the EU-27 in 2010 (two predominant partners: Germany 32% and Slovakia 9%) Absence of major macroeconomic imbalances - current account worsened to 3.8% of GDP in 2010 Substantial foreign ownership following high FDI inflows ... and on the cyclical industry sectors Industry sectors represents 30% of GDP (vs. 22% in Germany and 12% in France) Highly cyclical (predominant car industry and machinery)

4 page 4 MFF UK – March 9, 2012 Zoom on KB:  SG Group member since 2002.  Third largest bank in ČR: about 7 800 employees. about 400 point of sales.  KB initially corporate bank.  Retail developed after 2000.  KB’s market share on credit lending: Mortgages: 23% Small Business: 20% Corporates: 30% Municipalities: 40% Introduction / Banking sector As of 12/2011 Three key banks (market share at about 70%) KB (SG Group) ČS (ERSTE Group) ČSOB (KBC Group) Net profit9,513,611,2 Deposits (Bn CZK)586,0783,3721,6 Loans (Bn CZK)441,4483,5440,5 Cost of Risk (Bn CZK)-7,3-5,5-5,0 o/w loans-2,0-5,5-1,8 o/w other risks (Gr)-5,30,0-3,2 Cost of Risk (bps)181bps (39bps)114bps36bps Loan to Deposits77,5%71,9%69,5% LUSR5,7%6,0%5,2% CIR41.2%41,8%41,8%44,8% CAR14,6%13,1%15,6% ROE12,3%18,2%17,3%

5 page 5 MFF UK – March 9, 2012 Risk Management / Functions & Missions  Credit Risk Management Retail: model based and statistical approach (PD, LGD, EL) Individual approach for non-retail (Corporate, Banks, Sovereign) Collateral Evaluation (independent on client or distribution channel, on-site visits)  Market Risk Management FX, IR, commodity, credit risk, …  Monitoring and reporting Quality of portfolio / Focus on sensitive sections / Distribution channels / Sensitivity to market (FX, IR,..) Back-testing of models  Recovery / Collection Pre-early collection (-5DPD - 5DPD), Soft collection (5DPD – 90DPD) Hard recovery (90DPD +)  Operational Risk Management Antifraud policy, Insurance, Business continuity plans, estimations of operational losses, …

6 page 6 MFF UK – March 9, 2012 Supervision and Measurement Scoring models Monitoring Risk Methodology Credit frauds Credit Risk Assessment Corporate deal-flow Capital Markets Risks Market risk Capital markets Assets Valuation & Recovery Hard recovery Collateral Evaluation Risk Information Systems Risk databasis SG RISQ Functional links with SG RISQ departments KB RISQ A. Viry (L. Souček) Risk Management / Zoom on KB organization  Universal Risk Management Function / OpRisk out of scope.  Matrix organization / One of largest Risk Management in the SG Group (330FTE).

7 page 7 MFF UK – March 9, 2012  In-house score-card development since 1998 (IND, SB, Corp, Muni).  SG models used for sovereign and banks since 2002.  KB historical view: 1990 – 1997: Score-card developed by analysts (very simple expert models). 1997 – 1998: Score-card developed by statisticians (consumer loans, mortgages, corp). 2002 – 2003: Models implemented to the central rating system. 2001 – 2002: Behavioural scoring model developed (IND, SB). 2002 – 2005: Review of models with SG after acquisition. 2002 – 2007: Progressive usage of credit bureaus for retail (CBCB, SOLUS). 2005 – 2007: Implementation of Ba2 standards in KB (advanced methods for all credit portfolios). 2008 – 2011: Development and implementation of credit fraud prevention. Risk Management / History of model development in KB

8 page 8 MFF UK – March 9, 2012 MONITORING / REPORTINGCREDIT RISKMARKET RISKOPERATIONAL RISKRECOVERY NOT INTEGRATED CYCLE LOW UNDERSTANDING MODEL RISK 3 DANGERS Risk Management / Key risks for the bank

9 page 9 MFF UK – March 9, 2012 CCF LGD Default, PD= 100% “90DPD or unlikely to pay” Off B/S On B/S Recovered cash Actual Exposure = On B/S + Off B/S Exposure at Default (EaD) = On B/S + Off B/S * CCF Recovery processNon-Default, PD < 100% Credit Risk / Key elements PD, LGD, EaD  Expected Loss (EL) = EaD * 1Y PD * LGD  Risk Weighted Assets (RWA) = RW * EaD  RW = Function (PD, LGD, Maturity, Regulatory correlation, Regulatory interval of conf. 99.9%)  1Y PD = Probability of Default during following 1Y  LGD = Loss Given Default  EaD = Exposure at Default  RW = Risk Weight  RWA = Risk Weighted Assets

10 page 10 MFF UK – March 9, 2012 Probability 99,9% Probability 0,1% Stress Testing SIZE OF LOSSES FREQUENCY OF LOSSES Expected Loss covered by revenues Unexpected Loss covered by the capital Extreme Loss !!! DEFAULT !!! Bank Capital Market Risk RWA + Credit Risk RWA + Operational Risk RWA > 8%CAR =  CAR = Capital Adequacy Ratio  Regulatory minimum at 8%  Unexpected Loss as a variation of Expected Loss Credit Risk / Ability to absorb a loss

11 page 11 MFF UK – March 9, 2012 Credit Risk / Credit portfolios per PD & LGD Expected Loss Client rate: 3,5% Net margin 1,0% Expected Loss 0,5% Cost of funds 2,0%  Expected Loss (given by PD and LGD) is reflected in pricing.

12 page 12 MFF UK – March 9, 2012 Economic Rating Financial Rating Behavioural Rating Model Rating Final Obligor Rating Credit analyst Credit Risk / Corporates / Rating Model Moody’sS&P1Y PDCountry AaaAAA0,01%DE, USA, FR (M) Aa1AA+0,01%FR (S&P) Aa2AA0,02%BE, SI Aa3AA-0,03%IT, JP A1A+0,03%CZ, SK, CN A2A0,04%PL A3A-0,06% Baa1BBB+0,13% Baa2BBB0,26%RU, BR Baa3BBB-0,50% Ba1BB+1,10%HU, RO Ba2BB2,12% Ba3BB-3,26%BUL B1B+4,61% B2B7,76%UA B3B-11,42% Caa1CCC+14,33% Caa2CCC20,44% Caa3CCC-27,25% Default100,00%GR Rating scales for non-retail  Individual assessment is prevailing. Financial assessment (financial data) Economic assessment (position in market, …)  Model rating revised by credit analyst.

13 page 13 MFF UK – March 9, 2012 SCORING MODEL (YES / NO) DATA COLLECTION (COMPLEX INFORMATION ABOUT CLIENT) APPLICATION FORM (DEMOGRAPHIC DATA) CREDIT REGISTERS (CREDIT HISTORY) INTERNAL BANK DATA (BEHAV. DATA) TRANSACTION PARAMETERS INSTALLMENT LIMIT (YES / NO) COLLATERAL EVALUATION (YES / NO) CREDIT ANTIFRAUD SYSTÉM (IDENTITY, EMPLOYER, DATA) COLLATERAL Credit Risk / Retail / Granting process  Maximally automated (95% of approvals) / Maximally parameterized / Maximally centralized.  Data collection / Independent verification / Assessment by statistical model.

14 page 14 MFF UK – March 9, 2012 Demographic Model (application form) Credit Register Model (data from the register) Behavioural Model (data in the bank) Application rating Behavioural Models (data in subsidiaries) MAIN DRIVER OF PREDICTING POWER Basic Behavioural SM (Bank) Complex Behavioural SM (Group) Advanced Application SM Advanced Behavioural SM 3 KEY ADVANTAGES High predicting power. Complex assessment. High flexibility (4 boxes). IN KB SINCE 2002 IN KB SINCE 2007 IN KB SINCE 2006 IN KB SINCE 1998 Credit Risk / Retail / Scoring Model

15 page 15 MFF UK – March 9, 2012 1 200 ths. clients EUR 6 400M 300 ths. clients EUR 1 200M 80 ths. clients EUR 32M 170 ths. clients EUR 400M 2002 2011 20062004 AO AO, CL, CC PRODUCTS MAX LIMIT EUR 400 EUR 10 000 EUR 6 000 EUR 2 400 CLIENTS KB KB GROUP KB 3 KEY ADVANTAGES Fast and easy process High volume of production Top quality of production 3 KEY RISKS  No view of expenditures  Limited assessment  Change of behaviour Credit Risk / Retail / Scoring Model

16 page 16 MFF UK – March 9, 2012 CONTACT RNDr. Ing. Leoš Souček Deputy Head of Risk Management Komerční banka, a.s. Tel: +420 222 435 141 Email: leos_soucek@kb.cz


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