Business Risk Management - A Practical Application

Transcription

1 Operatinal Risk Management: A Practical Apprach and its Regulatr Implicatins Federal Reserve Bank f Bstn Nvember 2001

2 Operatinal Risk: Is a change required? Cmmand and Cntrl Branch Operating Manual Mandated Cntrls Audit Mnitring reslutins f audit findings Operatinal Risk is managed thrugh better cntrls and better audit prcess Inspire and Lead Guiding Principles Bundaries Structured Self Assessments f Risk Mnitring f OP Risk Levels Operatinal Risk is managed thrugh better risk identificatin and transparenc f risk taken Managing Operatinal Risk Based n ding mre f the same nl better A Ne Paradigm fr Managing Operatinal Risk Based n $ at Risk

3 Under The Ne Paradigm T Effective Manage Op Risk Business Leaders need t be able t kn h much $ are at Risk? Mre precisel anser these questins What are m biggest peratinal risk? At Bank At SBU At BU at BL What hits can I epect m P&L t take frm m biggest peratinal risk? H bad can thse hits get? H bad can thse hits reall get? H ill changes t m business strateg r cntrl envirnment affect thse hits H d m ptential hits cmpare internall r eternall

4 H is peratinal risk measured

5 Objective f Measuring Operatinal Risk Prvide an accurate vie f the peratinal risk prfile f the business ver the net 12 mnths. 8 What is the epected lsses frm peratinal risk 8 What is the Wrst Case Lss frm peratinal risk Supprts the analsis f Operatinal Risk 8 What are the tp Op Risk 8 What is the Wrst case lss under stress cnditins 8 H ill changes t m business strateg r cntrl envirnment affect the ptential. 8 H des the ptential hit cmpare ith ther business units r ther banks

6 Measuring Operatinal Risk Fr better Management Based n analtic techniques idel used in the insurance industr t measure the financial impact f an peratinal failure The fundatin is 8 the histrical peratinal lss eperience 8 deep understanding f hat and h is at risk The edifice is business judgement 8 similar t putting tgether a business plan 8 judgement is used t supplement/ replace r enhance histrical lss eperience based inputs 8 flls the same rigrus prcess as if all the inputs ere histrical lss data The measure is called OP VaR used fr determining 8 the epected lss frm peratinal failures 8 the rst case lss at cnfidence level 8 the required ecnmic and regulatr peratinal risk capital 8 cncentratin f peratinal risk

7 First Step Recgnise Distinct Operatinal Risk Lsses Tpes 1. Legal Liabilit: inlcudes client, emplee and ther third part lasuits 2. Regulatr, Cmpliance and Taatin Penalties fines, r the cst f an ther penalties, such as license revcatins and assciated csts - ecludes lst / frgne revenue. 3. Lss f r Damage t Assets: reductin in value f the firm s nn-financial asset and prpert 4. Client Restitutin includes restitutin paments (principal and/r interest) r ther cmpensatin t clients. 5. Theft, Fraud and Unauthrized Activities includes rgue trading 6. Transactin Prcessing Risk includes failed r late settlement, rng amunt r rng cunterpart

8 Fr a line f business and lss tpe: The rst case lss (WCL) ver the net 12 mnths WCL = Epected Lsses γ = Epected n f Lsses Average Lss γ Epected n f lsses the average number f legal liabilit, r transactin errrs, r frauds etc ver the net 12 mnths. Average Lss the average amunt lst per legal liabilit, r per transactin errr, r per frauds etc ver the net 12 mnths γ Factr t cnvert the epected lss t rst case lss

9 WCL Epressed in terms f Cmpnents f Epected Lsses and Average Lss WCL = Εpected lsses γ Epected n f Lsses Average Lss γ = E f PE f E LGE γ s E f = Epsure fr n f lsses eg n transactins, n f accunts, n f emplees PE = Epected Prbabilit f an peratinal risk lss eg Epected number f lss / the number f transactins E s = Epsure fr lss amunt eg Avg transactins value, Avg accunts value, Avg emplee cmpensatin LGE = Average Lss Given Event Rate eg average lss / Avg transactins value γ = Factr t cnvert the epected lss t rst case lss

10 Op Risk Measurement Prcess Calculatin f the Frequenc ( PE) Frequenc (PE) N f lsses/ n f trades PE = 2.8 per 10,000 Trades can be desegregated fr different tpe f trades r trade prcessing sstems)

11 Op Risk Measurement Prcess Calculatin f the Severit (LGE) 80.0% Severit (LGE) 70.0% 60.0% 50.0% 40.0% 30.0% 20.0% 10.0% 0.0% Amunt f lss /average trade amunt LGE = 9.8 % f Avg. Trade Value

12 Statistical Distributins and Simulate Functin and Parameters 60% 50% 40% 30% 20% 10% PE mnthl Functin Pissn Mean PE 2.8 lsses per 10,000 transactins Std PE 2 events 10,000 transactins 0% Mre 40% 35% 30% 25% LR mnthl Simulatin 20% 15% 10% 5% 50% 45% 40% 35% 30% 25% 20% LGE mnthl Functin LgNrmal Mean LGE 9.8 % 0% $0 $200 $400 $600 $800 $1,000$1,200$1,400$1,600$1,800$2,000Mre 15% 10% 5% 0% $0 $20,000 $40,000 $60,000 $80,000 $100,000 $120,000 $140,000 $160,000 $180,000 $200,000 Mre Std PE 15%

13 Annualize the Lsses And Estimate Epsure 40% 40% 35% 35% 30% 25% LR mnthl 30% LR annual 25% LR annual 20% 15% 10% Simulatin 20% 15% 10% 5% 5% 0% $0 $200 $400 $600 $800 $1,000$1,200$1,400$1,600$1,800$2,000Mre 0% $0 $200 $400 $600 $800 $1,000$1,200$1,400$1,600$1,800$2,000Mre Av Lss Rate 8% WCL 40% Gamma 5 With an Epsure f $10mm the epected lss is $.8mm ( $10mm 8%) and the rst case lss is $4mm ($10mm 40%)

14 H Credible is the Result Cmpare the PE and LGE derived frm internal lss histr ith industr PE and LGE Eample 8 if eternal lss histr shs ne event per mnth the internal lss histr f 36 mnths is sufficient t determine ith cnfidence the actual PE 8 if eternal lss histr shs ne event in 10 ears the internal lss histr f 36 mnths is nt sufficient t determine ith cnfidence the actual PE r the internall calculated PE is nt credible 8 When internal data is nt credible, then the actual PE = z i PE i + z e PE e 8Z are credibilit factrs and there are standard statistical methds fr determining Z s

15 Using eternal data Insufficient internal lss data is supplemented ith industr lss data LOB Risk Tpes BU A TP.... Lss rate/ Epsure base $ value f Transactins Firm Specific Risk TP derived frm internal data General Industr Risk TP derived frm industr data $Value f Transactins 8% $ Value f Transactins 12% Capital = $Value f Transactins (specific lss rate+ general lss rate ) γ $4mm = $ 10,000m { (Z)( 8% ) + (1-Z) ( 12% )} 5 Z = 1 $4.6mm Z =.7

16 Using eternal data Insufficient internal lss data is supplemented ith industr lss data LOB Risk Tpes BU A TP.... Lss rate/ Epsure base $ value f Transactins Firm Specific Risk TP H is the Z factr determined? frm internal derived Credibilit Ther data General Industr Risk TP derived frm industr data $Value f Transactins 8% $ Value f Transactins 12% Capital = $Value f Transactins (specific lss rate+ general lss rate ) γ $4mm = $ 10,000m { (Z)( 8% ) + (1-Z) ( 12% )} 5 Z = 1 $4.6mm Z =.7

17 Credibilit z z z Means Are far Apart, Strng Clustering Means Are far Apart, Weak Clustering Means Are Clse, Strng Clustering Means Are Clse, Weak Clustering z

18 H Credible is the Result Cmpare the PE and LGE derived frm internal lss histr ith industr PE and LGE Eample 8 if eternal lss histr shs ne event per mnth the internal lss histr f 36 mnths is sufficient t determine ith cnfidence the actual PE 8 if eternal lss histr shs ne event in 10 ears the internal lss histr f 36 mnths is nt sufficient t determine ith cnfidence the actual PE r the internall calculated PE is nt credible 8 When internal data is nt credible, then the actual PE = z i PE i + z e PE e 8Z are credibilit factrs and there are standard statistical methds fr determining Z s What happens hen eternal data is insufficient

19 Scenari Analsis Functin and Parameters 60% 50% 40% 30% 20% 10% PE mnthl Functin Pissn Mean PE 2.8 lsses per 10,000 transactins Std PE 2 events 10,000 transactins These are estimated using Business and Risk Management Judgement 0% Mre 40% 35% 30% 25% LR mnthl Simulatin 20% 15% 10% 5% 50% 45% 40% 35% 30% 25% 20% LGE mnthl Functin LgNrmal Mean LGE 9.8 % 0% $0 $200 $400 $600 $800 $1,000$1,200$1,400$1,600$1,800$2,000Mre 15% 10% 5% 0% $0 $20,000 $40,000 $60,000 $80,000 $100,000 $120,000 $140,000 $160,000 $180,000 $200,000 Mre Std PE 15%

20 Incrprating Scenari Analsis actual PE = z i PE i + z e PE e + z s PE s These are estimated using Statistics These are estimated using Business and Risk Management Judgement

21 Op VaR Reflects Changes in PE and LE ver time Business Unit A Business Unit A - Transactin Errr and Client Restitutin Lsses (12 mnth rlling average) $800,000 $5.0 $700,000 $600,000 $4.5 $500,000 $4.0 $400,000 $300,000 $3.5 $200,000 $100,000 $3.0 $- Nv-99 Dec-99 Jan-00 Feb-00 Mar-00 Apr-00 Ma-00 Jun-00 Jul-00 Aug-00 Sep-00 Oct-00 Nv-00 Dec-00 Jan-01 Feb-01 Mar-01 $2.5 Mnthl Lsses OpVar ($ MM) Nte the Lag H is BCE incrprated

22 Use Qualitative Adjustments

23 KRD s: Ke Risk Drivers Used t mnitr changes peratinal risk fr each business and fr each lss tpe befre the change in lss eperience can be bserved ( ie lag and l frequenc events) Incrprated int Op VaR, b mdifing the risk determined b lss histr and can be used t reard and punish fr psitive r negative changes in risk prfile Objective standard measure eg a standard scre Needs t be develped Can be as simple as the audit scre r as sphisticated as the 100 metrics used b sme banks ( eg % f bk dail independentl reevaluated, % f sstem dn time, age f sstems) Eample f H KRD can be used t Adjust Op VaR KRD % Op VaR % Op VaR LE

24 Is there an alternative t the screcard apprach t the Qualitative Adjustment r mre precisel t incrprating the BCE

25 Incrprating Scenari Analsis actual PE = z i PE i + z e PE e + z s PE s These are estimated using Statistics These are estimated using Business and Risk Management Judgement Use the scenari invlving the BCE 8Business and Risk Management must estimate the effect f the BCE n PE, LGE and γ

26 General OP VaR Methdlg WCL = Epected n f Lsses Average Lss γ = E f E s PE LGE γ E f E s = E = (1-z le )E h + z le E le E h = 12 mnth average epsure E LE = latest estimate frm BM Judgement Z le is frm RM and BM Judgement PE = z h PE h + z e PE e + z bce PE bce PE h = 36 mnth average rate frm internal lss eperience PE e = 36 mnth average rate frm eternal lss eperience Pe bce = Scenari analsis (RM and BM Judgement) Z h and Z e Calculated using statistical credibilit ther Z bce is frm RM and BM Judgement

27 The AMOR Reprt Analsis and Mnitring f Operatinal Risk

28 AMOR OpVaRs ($MM) OpVaR as % f Grss Incme Cmentar SBU OP VaR Q4-00 Q1-01 Q2-01 Q3-01 Q4-00 Q1-01 Q2-01 Q3-01 Q3-01 b Lss Tpes A. Client Restitutin B. Legal liabilit Client C. Legal Liabilit Emplee D. Lss and Damage t Assets E. Reg. Cmpliance Ta Penalties F. Theft Fraud Unauthrized Act. G. Transactin Prcessing A B C D E F G A B C D E F G

29 Start up Op VaR $70 $60 $50 $40 $30 $20 $10 $20 $27 $35 $47 $56 $58 $62 $66 $70 OP VaR increases 2.5 times ver 9 ears cmpared t accunt balance grth 208 times: Reductin in infrastructure build. Reductin in fraud rates because f business maturing.. Cmpsitin f Op VaR changes ver the 9 ear hrizn First ear, 81% f risk is Transactin risk reflecting infrastructure (kisks) build up Ninth ear, 77% f the risk is Theft and Fraud. Histrical Pr lsses rates have been used $0 F2001 F2002 F2003 F2004 F2005 F2006 F2007 F2008 F2009 $16.0 $14.0 $12.0 $15 $13 81% 95% 100% $80.0 $70.0 $60.0 $69 77% 88% 93% 100% $10.0 $8.0 44% F2001 $50.0 $40.0 F2009 $6.0 $5 $30.0 $4.0 $2.0 $20.0 $10.0 $11 $5 $4 $- Transactin Regulatr Fraud Lasuits Assets 0% $- Fraud Lasuits Rev. Transactin Regulatr 0%

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31 Decmpsing Epected N Of Lsses Epected n f Lsses can be decmpsed int the a measure f the amunt f the business activit that can gives rise t the lss and the prpensit fr lsses given that activit. 8 This alls cmparisn f peratinal risk ver time, b separating ut h much f the change is due t the change in the amunt f business activit and h much is due t a change in the prpensit fr lsses 8 the measure f the amunt f business activit shuld crrelate ith the number p epected peratinal risk lsses, this measure is usuall referred t as the frequenc epsure and dented as E f Fr eample: in transactin risk, E f ma be ttal number f transactin prcessed 8 the prpensit fr lss is the prbabilit that business activit gives rise t a lss and is dented b PE Epected n f Lsses = E f PE

32 Decmpsing Average Lss Average lss can be decmpsed int the average f amunt at risk per lss event and the percentage lst per lss event 8 this alls the cmparisn f peratinal risk ver time, b separating ut h much f the change is due t the change in the amunt at risk per lss event and h much is due t a change in the percentage lst per lss event This decmpsitin is especiall useful fr risk management, hen actin ca 8 the measure f the amunt at risk shuld crrelate ith average lss per lss event, this measure is usuall referred t as the severit epsure and dented as E s Fr eample: in transactin risk, E s ma be average value f transactin prcessed 8 the percentage lst f the amunt at risk per lss event is dented b LGE ( lss given event) Average Lss = E S LGE