Download - Michael Rehwinkel

7/28/2019 Michael Rehwinkel

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Agenda

JPMorgan Chase & Co. Operational Risk Overview

Objectives and Framework

Tools for Risk Measurement and Risk Management

Control Self Assessment Process

Risk Event Database

Capital Methodology

Reporting

Firmwide Technolo Platform

Future Agenda: Maximizing Return on Investment

2010 JPMorgan Chase & Co. Strictly Confidential and Proprietary

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Overview

Development and Implementation Timeline

JPMor an Chase is movin into the fourth sta e of a four-sta e model

Begin loss Anal ysi s and

quan t i f i ca t ion

and self

assessment

Repor t ing

Ad Hoc

Ad Hoc Orientation

Orientation Analytics

Analytics Framework

Framework

Business

specif ic only ;

no f i r m-wi de

da ta

Begin l oss

quan t i f i ca t ion

and self

assessment

Analy si s and

Repor t ing

Int egrat ed r isk

management

f ocused on

cont inuous

Traditional Audit &

Financial reviews

Governance

framework

Dashboard reporting

Risk driver analysis

Predictive

models

mprovement

pera ona r s

managementmostly reactive

an ar oss a a

reporting

Rigorous self

assessment model

Root cause

analysis

Internal and External

orre a ons

across risk classes

Risk mitigation

tools

Risk-based capital

2002Pre 2000 2006 2010 +


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Overview

Objectives and Framework

Improve understanding of drivers

Objectives Integrated Framework Design

Create environment of reliabledata, transparency of issues,

ss

ControlControlAssessmentAssessment

Risk EventRisk EventDataData

escalation and accountability

Move to no surprise environment

BusinessBusinessUnitsUnits

GovernanceGovernanceFrameworkFramework

OperationalOperational

RiskRiskCapitalCapital

issues as they develop and before

losses are incurred

Meet regulatory requirements:Basel II, Sarbanes Oxley 404 & FDICIA

IntegratedIntegratedReportingReporting

Key RiskKey Risk

IndicatorsIndicators(in(in

development)development)

through lower losses, reduced

volatility, greater productivity and

more efficient capital usage

Single, Integrated Technology Platform


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Tools for Measurement and Management

Risk and Control Identification and Management

Central Source of Operational Risk Information in the Firm provided byself-assessment process supplemented by other sources of data

Comprehensive operational risk and control data

Business Processes Information Technology: Infrastructure and Applications

Controls

Compliance

Multi le SourcesAssess

Effectiveness

Self Assessment

Internal Audit

Identify Gaps &Action Plans

Identify Gaps &Action Plans

External Audit

Regulatory Issues

Report &

Track

ynam c ssessment

Linked to Loss Data and Capital


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Risk Event Data: Fundamental for Improved Risk Management

Trend of Total Errors by Date

Occurred

15

65

115

165

215

265

01/1 01/2 01/3 01/4 02/1 02/2 02/3

IntegratedError

TrendAnalysis

HISTORICLOSSDATA

SeverityDistribution ofErrors $1Million andGreater(firmwide)

006

0.08

0.1

0.12

0.14

0.16

0.18

0.2

Pro

ba

bilit

y

Effect of 'Find Time' on Event Severity

1,000

1,500

2,000

2,500

Avg.Impact

300

400

500

600

700

#ofEvents

AverageImpact

#ofEvents

Capital Allocation

Lessons Learned

RiskEvent Profile -ActivitybyFunctionFY 2001,AllEconomic andTiming Events

(inabsolute value,$000)

All Op Risk Events Economic Impact>=$20k(FY 2001/2002,in $millions)

0

0.02

0.04

.

1 3 5 7 911

13

15

17

19

21

23

25

27

29

31

33

35

37

39

41

$millions

0

500

1-15 15-30 30-90 90-180 6mo-1yr 1yr+

FindTimeLine

0

100

200

Root CauseAnalysis

Customer Service

Client Rel / Front Office

Trading / PortfolioManagement

TransactionSupport &Mgmt

Core Ops / CorpService

Finance / Corporate Staff

Technology/ Applications

N/A-Other

0

10,000

20,000

30,000

40,000

50,000

60,000

Total Impact ($000)

ActivityType

Function

$0

$50

$100

$150

$200

$250

$300

E xecu tion C lients F raud /T hef t E mployee/S afety Majo r E xte rna l

$0

$1

$2

$3

$4

$5

$6

$ ImpactAvImpact

AvgImpactTotal

Impact


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Capital Methodology

Capital measures of regulatory and economic capital based on statisticalmodel that includes the four required regulatory elements

1. CALCULATE BASE CAPITAL 3. ASSESS CAPITAL2. ADJUST FOR

InternalLoss Data

APPROPRIATENESSINTERNAL CONTROL

FACTORSInternal Losses

Exter nal Losses

BaseCapital

StatisticalModel

Risk-basedCapital

QualitativeAdjustmen

t

Int ernal and

External

Benchmarks

ScenarioAnal sisLoss Data

LDA Frequency distributionbased on internal data

Severity distribution based on

Audit Grades

Control Gap ActionPlan metrics

Senior ManagementReview

Appropriateness of

Aggregate Loss Distributiongenerated via Monte Carlo

Potential for othermetrics / KRIs

Capital May be adjustedUp or Down


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Combination of Internal and External Loss Data Enables Risk

Measures at Business / Risk Type Level (31 Units of Measure)

Cap ta Ca cu ate or our aggregate r stypes for each business

Business Practice / Litigation Risk (BP)

xceptons to overcome s ortcomngs:

Asset Management BP risk split forInvestment Mgmt and Private Banking

Execution Risk (EX) Fraud Risk (FR)

Investment Banking BP risk split intounderwriting and non-underwriting

Underwritin risk uses different mp oyee spu e s

Plus one aggregated firmwide

Dama e to Ph sical Assets DPA

exposure-based methodology


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Standard LDA Approach Used (for all but one Unit of Measure)

The Modeling Approach is based on the standard actuarial model of estimating afrequency distribution and a severity distribution to derive an aggregate loss distribution

Same Model for Regulatory and Economic CapitalFre uenc of Loss Severit of Loss

Frequency Distribution Poisson

ensity

Density

Severity Distribution

Mixture of Lognormal (up to 6 components)Annual Frequency

D

Loss Amount in $

Internal Data

External ORX Data

Aggregate Operational Loss

Regulatory Economic

99.9% C.I. 99.97% C.I.

UnexpectedLoss

ExpectedLossD

ensit

y

Belowthresholdlosses

1-Year Holding Period 1-Year Holding Period

Includes Expected Loss Excludes Expected Loss

(= Model EL +-

Mean 99.9th

percentile

0

Regulatory Capital

99.97th

percentile

Thold


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Large Tail Events Distort Results from Traditional LDA Approach

A Generation 2 Exposure-based Model Developed for IB Underwriting Risk

Model Attributes Model Summary

Port f o l io of Relat ionships Total $ amount at risk?xposure

Pr obabi l i t y of Event (PE)

rel

ation

Event FrequencyHow often do such eventsoccur?

Invest or Loss (IL)Even

tC

o

Given an event how muchcould an investor have lost?

Event Severity

How much could a settlement

Event Correlation

be as a % of investor loss?

What is the joint


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Reporting

Losses in current period, risk and control ratings, Issue resolution status, KRIs etc.

9 Information conveyance, actionable reports

Historic Data Trends Loss data, CSA metrics, KRIs etc.

9 Information conveyance

Ad hoc query capability

Tailored reportsuppor s ana ys s

Benchmarking,

JPMC vs. ORX Benchmark projects, Range of Practices, LDCE

9 Information conve ance, some actionable re orts

Visualization

Simple management dashboard, Heat maps, E2E process maps


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One Firmwide Technology Platform for Operational Risk Data

Used by all businesses globally

In-house development

Launched in 2004

6,500 Registered users

3,000 average users / month

Compliance Assessment

Test Capture

My Alerts

Key Metrics

M Profile

Updates

Capital

IT Risk

Administ rat ion


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Reports


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Future Agenda

Leverage the investment in time, effort and $ to improve risk managementpractices, and increase financial performance through lower losses, reducedvolatility, greater productivity and more efficient use of capital.

Im roved Anal tics and Re ortin

Greater insight Forward looking

More actionable

Key Risk Indicator Development

Directionally correct, if not statistically defensible

Benchmarking Internal: Quantitative and Leading Practices

External: Quantitative (via ORX) and Leading Practices (ORX, RMA and others)

Extended Integration p r s an us ness per ormance me r cs

Op risk and other risk classes


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