a rating-based framework for credit portfolio risk-return

Istambul

May 29 2013

Lorenzo Bocchi

Partner

A Rating-Based framework for Credit Portfolio Risk-Return optimization and Loan Pricing

Istanbul

May 29 2013

Lorenzo Bocchi

Partner

A Rating-Based framework for Credit Portfolio Risk-Return optimization and Loan Pricing

proprietary and confidential A Rating-Based framework for Credit Portfolio Risk-Return optimization and Loan Pricing | 2

A closer look to Prometeia


� Company founded in 1974 and fully owned by

private and independent shareholders

� More than 400 experts distributed in 5

regional offices: Bologna, Milan, Istanbul,

Beirut and Douala

� Presence in more than 20 countries, with

particular focus on Europe, Middle East,

Russia & CIS

� Local presence in Africa from 2010:

reference in North Africa and affiliate

company based in Douala

Who we areA leading European company in financial consulting and economic research, serving more than 200

financial institutions in EMEA region


What we doWe provide highly-specialized consulting and IT solutions, integrating analytical models, market and

customer data, financial and economic scenarios

Our areas of expertise:

� ALM & Treasury Risk

Liquidity Risk & Financial PlanningTraded & Non-traded Market RiskIAS & Basel Compliance

� Credit Risk

Lending Application ProcessRating model development & Portfolio ManagementIAS & Basel Compliance

� Performance Management

Fund Transfer PricingManagement ControlPlanning & Forecasting

Risk & Performance Management

Models & Methodologies

SpecializedIT solutions

Consulting services

Data analysis servicesTraining


Why we are different | business modelOur business model is unique in combining a highly-specialized approach to banking needs with an

unparalleled completeness of offering on Credit Risk, ALM & Treasury Risk

SW offering

Serviceoffering

Traditional consulting

companies: wide range of services

with no proprietary SW solutions

Traditional IT vendors: wide range of SW with

limited consulting / implementation support

Prometeia: full coverage of client

needs in the Risk & Performance field

Our customers can benefit

from having only one partner

in establishing its own Risk

framework from end to end

� we manage the entire project lifecycle: faster and effective implementation, lower costs and project risks

� having a proprietary SW house, we can easily customize our solution to meet the Bank’s specific needs


Turkish loan market evolutionTurkish loan market is rapidly increasing in both the corporate as well as the retail areas while Euro

area has negative growth. Risk managers need to be conscious about the consequences in terms of

capital absorption and credit strategies

Introduction

Source: TCMB, Financial Stability Report, November 2012 Source: Prometeia on Bce database

Ann

ual g

row

th r

ate

%

OthersItalyEURO area High spread

Retail and corporate credit growth


Turkish credit risk evolutionTurkish credit risk reached its peak in 2009 than reduced in 2010 and 2011, while in 2012 increased

compared to the previous year. In the EU area a deep increase of NPL shows the need to effectively

manage the lending process

Introduction

Source: TCMB, Financial Stability Report, November 2012 Source: Bloomberg, EBA 90 SIFI Banks, September 2012


Regulatory capitalBasel capital requirements are progressively more pressing: capital optimization as well as effective

allocation are required

Introduction

Basel III time-tableNote

� Further capital buffers for Systemic Important Financial Institutions (SIFI)

� In Europe CRD IV will implement Basel 3 changes; to reduce impact on real economy, an SME supporting factor is being introduced


Regulatory capitalBasel 3 rules imply a significant increase of capital requirements: banks are forced to develop

effective capital strategies

Introduction

Deleveraging: Basel 3 impacts

-

2.00

4.00

6.00

8.00

10.00

12.00

14.00

Basilea 2 (1) 2= (1) + CcB 3= (2) + CccB 4=(3) +SIFI

MaxMaxMaxMax RWA RWA RWA RWA / / / / Capital Capital Capital Capital

ApproachApproachApproachApproach CoeffCoeffCoeffCoeffRWA RWA RWA RWA /Capital /Capital /Capital /Capital ((((LeerageLeerageLeerageLeerage)))) VarVarVarVar%%%%

Basel 2 (1) 8% 12,50 100%2= (1) + CcB 10.5% 9,52 -24%3= (2) + CccB 13% 7,69 -38%4=(3) +SIFI 15.5% 6,45 -48%

� RWA optimization

� Business model focused on activitiesand services «RWA saving»

� Effective capital allocation/valuebased management

� Cost/income ratio enhancement(cost optimization) strategies

� Active capital planning strategies

-24%

-38%

-48%

Basel 2(1)

Consequences


Turkish banking sector capital ratio vs. other countriesTurkish banks need to face their growth through an adequate capital structure: active capital

management strategies need to be implemented

Introduction

Source: TCMB, Financial Stability Report, November 2012


Strategic risk management and active loan perspectiveAfter Financial Crisis the Financial Stability Board and regulators have enhanced the necessity of a

comprehensive risk management with different implication to loan portfolio strategy, wrt credit cycle

Introduction

Active capital management

AFRAFRAFRAFR

Internal capitalInternal capitalInternal capitalInternal capital

time time time time

� Banks assess the consistency between capital and

risks and ensure that it remains adequate through time

� Capital adequacy should be verified through time and

under base and stressed scenario

� Planned and credible management actions most be

defined to ensure the coherence between capital at

risk and equity (contingency capital plan)

European market perspective

� Deleveraging

� Selective asset disposal (focus on core

business)

� Improve asset quality (policies RWA

saving)

Turkish market perspective

� Managing the growth on a risk – return

perspective

� Explore quick-wins for RWA reduction

Stressed Stressed Stressed Stressed

scenarioscenarioscenarioscenario


OutlineIn order to effectively manage credit growth, banks need to focus on the following key issues:

Introduction

� RWA optimization� Loan policies� Pricing

1.RWA optimization 3. Pricing2. Loan policies

Objectives

Outputs

Exploit regulation opportunities to minimize capital requirements

Reduction of capital requirements:� Standard� A-IRB

Define portfolio re-composition strategies in order to maximize returns minimizing risks

Risk-return ratios improvement

Identify risk-adjusted break-even pricing thresholds and define mark-up strategies to achieve loan policy goals

Effective pricing selection, risk reduction, cross selling, …


1 | Introduction


3 | Loan policies

4 | Pricing



Capital managementCapital ratios are or will be under pressure due to both the economic growth as well as regulatory

prudential policies

RWA optimization

Capital ratios

Eligible capital

RWA=

Management actions

� Lending growth implies an increase

of RWA

� Capital ratios additionally tend to

decrease according to Basel 2 and

Basel 3 requirements

� Managerial actions need to be

implemented

Pressure on capital ratios

Capital management

Capital strategy

Capital governance

Capital adequacy

Capital saving

Loan policies


Capital management | RWA optimizationAccording to tactical actions, active portfolio improvements and AIRB validation banks have great

capital requirement saving opportunities

RWA optimization

Up to+ 140 bps post AIRB validation (benchmark)

Up to + 50 bps according to risk

appetiteUp to + 40 bps according to STD specific features

Base line

Significant

capital saving

and process

improvement

due to capital

management

actions

Illustrative:Eurepean banking benchmark savings

Capital requirement savings

1. Tactical improvement (constant portfolio)

3. Improvement due to AIRB

2. Active loanportfolio management

Actions

Total: up to 230 bp

Main Focus of

the presentation


‘Tactical’ RWA improvement – standard approachStandard capital requirement approach saving opportunities can be found in specific areas through

specific “scouting” activities

RWA optimization

Credit risk:

standard

approach

� Segmentation (retail, …)

� ECAI weighting schemes offering capital requirement benefits

� Credit risk mitigation policies (CRM)

� Provisions and coverage ratios

� Off sheet balance weigthting schemes

� …

� Capital requirement saving

� lending process

improvements

Areas Benefits

Other

� Securitizations, …

� Counterparty risk weighting schemes

� Data quality management

� …

� Operation specific

improvements

Following a checklist based approach could help in reduce capital

requirement and maintain it through time


‘Strategic ’ RWA improvement - AIRB approachThe adoption of advanced rating modeling has implication both from the lending process as well as

from the capital requirement perspective

RWA optimization

PDModels

� Model calibration (forward looking vs. PIT ) –

default definition , …

� …

LGD models

� Defaulted asset calibration

� Downturn LGD calibration

� …

Application � B2 asset class segmentation (corporate vs

retail)

� Application of LGD wrt CRM (e.g. personal

guarantees)

� …

Issues to be managed A-IRB optimization

� Up tt 40 bp CT1

� …

� Savings wrt baseline of 50 bp Ct1

� …

� Customer threshold segmentation (e.g., retail, ...)

� Trade off management of capital deduction

(provision shortfall wrt EL models)

� …

AIRB process improvements and capital requirement savings


Basel 2 implicationsBasel 2 framework impacts not only capital requirements but also the whole banking organization

RWA optimization

� Budget based on risk adjusted performances

� Risk adjusted pricingEarnings

Processes and operational costs

� Internal rating lending process standardization

� Re-engineering of the lending process (rating based limits, …)

Cost due to credit risk

� Provisions IASB compliant using Basel 2 metrics

� Early Risk detection based on the rating system

� Policy rules (loan policies)

… the introduction of risk measurement tools have positive impacts on the whole organization

both from the managerial perspective as well as from the monitoring point of view

Regulatory capital � Potential capital savings (in particular for the retail activities)

� Advantages for IRB

banks

� Managerial benefits

due to risk

management tools

and procedures (loan

policies, process re-

engineering, …)

Focus Implications Key benefits


1 | Introduction


3 | Loan policies

4 | Pricing



Loan policies frameworkLoan policies need to be integrated in the planning process and a deep collaboration among banking

departments is required to achieve credit portfolio risk-adjusted targets

Loan policies

Loan policies

Credit risk, RWA, … evolution and optimization

Strategic planning , budgeting, targets

(volumes, gross margins, …)

Risk-adjusted asset allocation

policy rules

Credit dpt.Area pianificazione e risk management

Credit portfolio management

Target risk-adjusted

performances (e.g.,RORAC)

Commercial dpt.

� Identification of departments involved in the process

� Commercial network commitment

� Planning and budgeting process integration both from a functional as well as from the IT perspectives

Key points

Strategic planning and risk management dpt.

Portfolio management (credit risk, provisions, concentration name, concentration sector)

Strategy execution, product innovation, pricing management

Planning


Portfolio optimization | OverviewAn effective credit portfolio management relies on the joint evaluation of: risk, return and

diversification

Loan policies

negative

min

avg

max

Risk- adjusted

performances

(i.e. Raroc, Rarorac, EVA, …)

� Identification of homogeneous

portfolio clusters

� Quantification of overall client

return and risk

� Diversification:

� Name concentration

� Sector concentration

� Identification of portfolio

strategies:

� Budget constraints

� Lending addressed to best

performing clusters

� Pricing strategies

Portfolio analysis

risk

diversification

retu

rn

+

-

+ -

+


Portfolio optimization | Economic capital model (1/2)Adopting and properly calibrating credit portfolio model, is possible to understand specific risk

decomposition and then address credit portfolio strategies to an active management of credit

portfolio

Loan policies

Active management of big exposuresActive management of big exposuresActive management of big exposuresActive management of big exposures Credit portfolio strategiesCredit portfolio strategiesCredit portfolio strategiesCredit portfolio strategies

Name concentration Sector concentration

Expected lossExpected lossExpected lossExpected loss Economic capitalEconomic capitalEconomic capitalEconomic capital Outstanding - million €

0%

10%

20%

30%

40%

50%

1 101 201 301 401 501 601 701 801 901

Perdite attese Capitale economico

Primi 100 clienti(su 500 000) Primi 1000 clienti

Clientiordinatiper esposizione0%

10%

20%

30%

40%

50%

1 101 201 301 401 501 601 701 801 901

Perdite attese Capitale economico

Primi 100 clienti(su 500 000)

0%

10%

20%

30%

40%

50%

1 101 201 301 401 501 601 701 801 901

Expected loss Economic capital

Top 100 clients Top 1000

Clients by exposure

Ris

k(%

ex

posu

re)

7 0006 0005 0004 0003 0002 0001 000

5%

4%

3%

2% 47 110

190Transport

AgricultureFinancial

RE

Services Distribution

Families

manufacturing

(%

)7 0006 0005 0004 0003 0002 0001 000

5%

4%

3%

2% 47 110

190

clients


Portfolio optimization | Economic capital model (2/2)

Loan policies

� Model choice � Market vs. Accrual

� Expected shortfall

Main issue Possible solution

� Correlation calibration � Market vs. PD based

� Single name factor

� Incremental VAR calculation � Concentration vs systematic risk

� Impact on ES

� Coherence with Standard / AIRB regulatory

capital

� To properly manage the trade off between Economic

Capital estimation (and more detailed risk concentration

measurement) with the consistency with standardized/

IRB more conservative Calculation

� a scaling approach could be used, in order to obtain

consistent levels of RWA capital but properly considering

different concentration risk rivers among portfolio

segments

Some issues have to be addressed in economic capital model calibration, for a proper managerial use


Portfolio optimization | Key featuresIn order to manage the portfolio evolution three elements are required:

Thus, according to banking risk appetite, portfolio optimization strategies are defined as follows

Loan policies

� As-is portfolio analysis� Economy forecast� Constraint evaluation

Computational algorithms for incremental risk and systemic risk

Capital Planning

Input Simulation Output

� Planned capital absorbed by systemic risk on a multi-year horizon

� Planned concentration risk due to large corporate loans

� Capital requirement forecast under stressed conditions

Basic info

Forecasts Risk drivers driven aggregation by asset classes

Operational

bounds

� Portfolio composition by sector/branch/region

� Risk parameters

� Forecasts of strategic plan’s figures drilled down by business, sectors, regions

� Exposures evolution on Large Corporate

� Hypothesis on planned PD� Hypothesis on

diversification

� Elasticity from system-wide figures, drilled down by sector/branch/region

� Portfolio composition


Loan policies

Every enterprise can be assigned to a specific micro-sector and be evaluated according to specific benchmarks for competitiveness, exposure to systemic risk and businesses sustainability

Prometeia economy forecast | A comprehensive approach

This framework is available for micro-sector with detailed data and forecast on � Real Growth: turnover, internal demand, export performance,

international competitor analysis, market segmentation

� Cost and Prices: factor cost analysis, impact of raw material price and energy intensity move, industrial and consumer prices

� Financial Account: profitability, EBIT, EBITDA, leverage, financing needs, sources and cost, risk and debt sustainability

sector specific models

firmbalance sheets

macro scenario

(Prod/Pot) (var%(Prod)) (Deb/Cap)(Fin. Cost/VA)(ROI)

10% 10% 15% 25% 20% 20%10%

ISEF: synthetic risk and performance index Growth and returns, recent history

Industry Manufaturing

Ret

urn

on E

quity

Production var

ISEF


The comparison of different credit cluster allows to figure out the actual positioning as well as the

potential evolution of the portfolio

Target portfolio | Risk-adjusted analysisLoan policies

Risk %

Risk adjusted return %

� The portfolio is split in clusters� Cluster’s dimension is represented by the size of «bowls» in the scatter plot� Four areas are identified in the scatter plot:

� The goal is to move the portfolio toward the high return-low risk area

Risk-adjusted performance analysis

High return, low risk

Low return, high risk

Average portfolio risk

Average portfolio risk-adjusted return

As-is Target

Portfolio average risk-adjusted performance

Policy rules

Definition of growth strategies considering: risk-adjusted performances,

budget, name concentration, sector concentration,…

Rorac < avg ptf Rorac > avg ptf

Volumes

Cost of risk

Margins

Growth=system Growth>system

Growth<system Growth=system

Pricing function of the risk and evenctually additional spread

Pricing function of the risk


Once identified areas where to increase, maintain and reduce credit it is required to quantify the

intervention and define policy rules to implement portfolio strategies

Optimization processLoan policies

� Budget goals and constraints

� Targets are differentiated

according to: geographic area,

sector, …

� Expected and unexpected loss

targets� Policy rules:

� Rules are differentiated according

to: geographic area, sector, rating,

…

� Rules are defined according to

product specific features: long

term, mid term, …

Target portfolio: customer segment perspective Transmission mechanics


Policy rules are developed according to the existing risk management procedures (i.e.: rating process,

monitoring instruments, …) defining intervention areas (traffic lights)

Policy rulesLoan policies

Portfolio composition and policy rules

� To reduce:� Short term loans: rating class

from CC reduction 10%� …

� To keep:� Short Term: self-liquidating loans in

rating class until CC� …

� To acquire� Short Term: develop short term

operations in rating class AAA-BB;…

� Mid-Long Term: new lending only ifcollateralized; if not collateralizedthe area manager authorization isrequired for loans with maturity nolonger than 10 years; …

� …

Example of policy rules

Risk-adjperformance

Area 2 Area 3

Sector 1 Sector 2

1

2

3

n

…

To keep

To descard

High Low Low

∆ Exp ++ - -

∆ PD + - -

To acquire or expand

Boundaries for policy rules deployment must be calibrate d in relation to the expected difference of PD/EL in the simulation of an optimized portfolio

Area 1

Ou

tpu

t

….

….


Credit risk monitoring and control assumed a critical role in CRO agendas, both from regulatory and

managerial point of view

RM solution | ERMAS PrometeiaLoan policies

ERMAS™ Credit Risk Module allows for analytical and reporting capabilities that support banks in running economically viable credit business, helping in active credit portfolio management and tableaux de bord reporting

� Accurate analysis of all Basel asset classes Accurate analysis of all Basel asset classes Accurate analysis of all Basel asset classes Accurate analysis of all Basel asset classes considering standard and IRB methodologies as well as regulatory concentration risks and VaRcalculations

� Integrated with databases for Basel regulatory capitalIntegrated with databases for Basel regulatory capitalIntegrated with databases for Basel regulatory capitalIntegrated with databases for Basel regulatory capital so input data feeding is simple and balanced with regulatory outputs

� High flexibility in customizing analysisHigh flexibility in customizing analysisHigh flexibility in customizing analysisHigh flexibility in customizing analysis dimensions, simulation parameters and stress scenarios

� Standard reporting capabilities and powerful analytical toolsStandard reporting capabilities and powerful analytical toolsStandard reporting capabilities and powerful analytical toolsStandard reporting capabilities and powerful analytical toolssupporting drill-down analysis and interpretation of system results


Prometeia suite for credit mgmt can be implemented stand-alone or integrated with third-party SW

solutions, Prometeia is also a primary player in the development of PD and LGD models

Case study | Prometeia ERMAS architectureLoan policies

data feed

credit riskcredit riskcredit riskcredit risktableautableautableautableau----dededede----boardboardboardboard

ERMAS Prometeia credit suite

Electronic Electronic Electronic Electronic Credit Credit Credit Credit

ApplicationApplicationApplicationApplication

RWA engine module

economic capital

module

capital planning/capital planning/capital planning/capital planning/stress stress stress stress modulemodulemodulemodule

credit risk reporting

business/regulatory reporting

default/recovery data

Analytics

core system/DWH

rating

LGD

on-l

ine

ECAPro

RatingPro

rating input

Methodology support

economy forecast

rating engine

on-line

Creditmart

pricing module

Repository

Bat

ch E

TL

0.00%0.00%0.00%0.00%

2.00%2.00%2.00%2.00%

4.00%4.00%4.00%4.00%

6.00%6.00%6.00%6.00%

8.00%8.00%8.00%8.00%

10.00%10.00%10.00%10.00%

12.00%12.00%12.00%12.00%

14.00%14.00%14.00%14.00%

0000 0.050.050.050.050.10.10.10.10.150.150.150.150.20.20.20.20.250.250.250.250.30.30.30.30.350.350.350.350.40.40.40.40.450.450.450.450.50.50.50.50.550.550.550.550.60.60.60.60.650.650.650.650.70.70.70.70.750.750.750.750.80.80.80.80.850.850.850.850.90.90.90.90.950.950.950.950.990.990.990.991111


Loan policies

Prometeia’s ECA solution is an integrated, workflow-driven software platform, enriched with state of

the art risk analytics, that allows credit officers to improve portfolio credit quality and put control the

whole credit origination process for all loan categories

� Advanced reporting tools (benchmark analysis,

portfolio’s and client’s risk report, process

efficiency report, …..)

� Minimal IT and bandwidth requirement

� Open architecture ready to integrate different

banking core systems

� Fully integrated to Prometeia’s CreditMart risk DB

and RiskPro rating engine

� Ready to implement risk modelling evolution

(monitoring, credit strategies, …)

ECA features

ECAPro solution | Main features


Loan policies

The client exposures (internal and external behavioral) are analyzed according to homogeneous

categories and with a defined time series

Prometeia ECA solution | Loan policy analysis

traffic light analysis

of managerial status

and credit policies

guidelines. Contagion

criteria implemented

at group level

parametrical

management of

categories according to

the Bank’s internal

classification

analysis performed

at single client level

or for the group

Master Data Risk evaluation Position Action Plan


Policy rules constitute a crucial step in the risk-based management banking evolution: their

implementation imply a series of issues ranging from data management to process re-engineering

Loan policies | Implementation issuesLoan policies

Critical issues

� Interaction among banking departments: risk management, lending, marketing, planning, …

� Database:

� Integration of return, cost, risk, capital, … information

� Data management

� Risk parameter calibration:

� Macroeconomic and bank specific forecast

� Risk dynamics in terms of: EAD, PD, LGD, RWA, concentration, …

� Budgeting:

� Risk premium, incentives, …

� Loan policies implementation:

� Loan pricing risk adjusted ..


1 | Introduction


3 | Loan policies

4 | Pricing



Pricing framework

Pricing

The goal pursued is to analyze the components of the risk adjusted pricing, define a pricing catalog

customized on bank’s borrowers and identify rules to effectively manage customer specific

exceptions Pricing management

Priceto

value

Priceto

client

Priceto

market

PPPPricing enginericing enginericing enginericing engine Behavioral pricingBehavioral pricingBehavioral pricingBehavioral pricing

Price to valuequantification of the risk components as well as «industrial» costs of lending

Price to marketdefinition and active management of «pricing catalog»

Price to client customer based exceptions due to customer specific features

Break-even

Final mark-up

Pricing catalog Catalog exception


Key features of pricing process

Pricing

In order to effectively price their lending, banks need to start from the price to value and analyze the

market where they operate in order to define an effective pricing catalog and manage prices with

customers

1. Price to value

3. External benchmark

4. Pricesensitivity

5. Mark up strategy

6. Pricing catalog

� Break-even threshold

� Comparison of banking system pricing applied to homogeneous deals

� Sensitivity of the volume of credit on price changes

� Definition of the mark-up to be applied coherently with the portfolio targets (RAROC analysis, …)

� Starting from price to value, considering the mark-up strategy: definition of the cluster pricing catalog

2. Internal benchmark

� Comparison of ongoing banking conditions within the clients cluster

7. Price to client

� Price adjustment according to: cross-selling, …


Fair price can be defined as a function of risk-free fund transfer price, liquidity and options spreads,

credit spread and operating costs

1. Price to value | Fair pricePricing

fair price = FTPex-ante+ Credit SpreadOP

credit spreadOP spreadOP

credit risk: unexpected loss from economic capital model / regulatory capital

direct and indirect costs

cost of capital for shareholder value targets

commissions

risk-premium approach

to be defined the blend of FTP components with respect to risk-free curves

risk free (from money market and IRS / CIRS quotations)FTPex-ante FTPrisk-free

spreadOPT

description

cap/floor optionsswitch rate type optionprepayment option…

spreadMKT spread funding (liquidity/bank rating)basis risk

components

cost-of-risk approach

notes

credit risk: excpected loss


FTP is defined as a function of the different financial risk type associated to potential transactions

1. Price to value | Ex-ante FTP

Pricing

volatility surface

market rate curve liquidity spread curvemarket rate curve

Re-fixing parameter curve

Reinvestment /

refinancing risk of

positions with fixed

rate or floating rate

linked to parameters

directly observed in the

market (e.g. Euribor,

Libor)

Reinvestment/refinanci

ng risk of positions with

floating rate linked to

parameters not directly

observed in the market

(i.e. position with

unnatural leg with

respect to the interest

payment frequency)

Reinvestment/refinancing

risk of positions with

floating rate and

embedded options

Volatility risk spread

related to the bank

rating and funding

strategy

Yield curve risk

Basis risk

ex-ante FTPCommercial BU position

RIS

K T

YP

E

Volatility risk

Liquidity risk


The credit spread of the operation “SOP” is calculated adopting a lifetime profitability approach,

considering the present value of contractual cash flows, net recovery, net operating costs and cost of

capital remuneration

1. Price to value | Credit spread

Pricing

Σt PV(contractual cash-flows)t

Contractual cash flows are expressed as a function of the risk-free FTP, the target credit spread and weights given by (1-interpolated PD)

Sop = [PV (expected CASH FLOWS)] = [ALLOCATED AMOUNT + PV (COSTS + COST OF CAPITAL) ]

Σt PV(recoveries in case of default in t)

+Cash flows in case of default depend on the outstanding andare weighted using interpolated marginal default probabilitiesand the recovery rate (1-LGD)

Σt PV(net operating costs)t

-All direct costs, indirect costs, fees and commissions should beincluded

-The capital requirement depends on the regulatory weight (inpercentage) and the outstanding according to the amortizationrule; its remuneration can be considered in line with the extra-return over risk free rate for shareholders.

=ALLOCATED AMOUNT

(nominal value)

Σt PV(capital requirement remuneration)t


The availability of all risk and cost price-impacting factors in an integrated environment allows to

highlight all relevant price effects in defining pricing strategies

1. Price to value | Risk parameter effects

Pricing

0.0%

0.2%

0.4%

0.6%

0.8%

1.0%

1.2%

1.4%

1.6%

1.8%

2.0%

5 10 15 20

Spre

ad

“Approach” effect

the evaluation approach has considerable impact on fairprice for worst rating classes in light of higher capitalrequirements

a high maturity implies a considerable gap with respectto the initial risk conditions (reflecting transitionmatrixes and rating development philosophy – PIT vs.TTC; credit spreads term structures for worst ratingclasses are monotone decreasing wrt maturity

“Maturity” effect

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

1 2 3 4 5 6 7 8 9 10 11

Classi Rating

Spre

ad

“Risk mitigation” effect collateral value and type impact considerably on fairprice with respect to all rating classes

unsecured

secured

Rating class

maturity

Rating class

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

1 2 3 4 5 6 7 8 9 10 11

Classi Rating

Spre

ad

Risk Neutral Risk adverserisk premium cost of risk


Originating from loan policy strategies, re-pricing actions can be realized considering cluster specific

features and, within each cluster comparing customer’s attractiveness and profitability

2. Internal benchmark | Re-pricing

Pricing

Axis of analysis

� Loan policy clusters (regional area, sector, …)

� Risk clusters (Rating, EL,...)

� Portfolio features: products, services, …

� Share of Wallet

� Risk-adjusted indicators

� …

“Drill “Drill “Drill “Drill down” down” down” down” on on on on specific specific specific specific

customercustomercustomercustomer

Key indicators:Key indicators:Key indicators:Key indicators:

RiskRiskRiskRisk----adjusted adjusted adjusted adjusted income, credit income, credit income, credit income, credit

cost,…cost,…cost,…cost,…

Homogeneous cluster where to investigate differences among customers in terms of risk-adjusted returns, credit risk, … in order to carry out re-pricing strategies

Clustering

Loan policy cluster: SME Loan policy cluster: SME Loan policy cluster: SME Loan policy cluster: SME ---- Real estate Real estate Real estate Real estate ---- AreaAreaAreaAreaMatrix A (rating 1Matrix A (rating 1Matrix A (rating 1Matrix A (rating 1---- 4 4 4 4 –––– Prime customers)Prime customers)Prime customers)Prime customers)

EVA / RORACS

OW

–M

AR

KE

TIN

G A

TT

RA

CT

IVE

NE

SS

� % Custom: 30%% Custom: 30%% Custom: 30%% Custom: 30%� AvgAvgAvgAvg PD : 0,36%PD : 0,36%PD : 0,36%PD : 0,36%

� …………

� % Custom: 20%% Custom: 20%% Custom: 20%% Custom: 20%� AvgAvgAvgAvg PD 0,30%PD 0,30%PD 0,30%PD 0,30%

� …………

� % Custom.: 40%% Custom.: 40%% Custom.: 40%% Custom.: 40%� AvgAvgAvgAvg PD : 0,21%PD : 0,21%PD : 0,21%PD : 0,21%

� …………

� % NDG: 10%% NDG: 10%% NDG: 10%% NDG: 10%� AvgAvgAvgAvg PD : 0,15%PD : 0,15%PD : 0,15%PD : 0,15%

� …………

LOW

HIG

H

LOW HIGH

1111 2222

3333 4444


Prometeia pricing framework

Pricing

For each cluster and each customer/product customized pricing catalog is defined…

Risk-adjusted environment

Financial risk environment

Pricing engine

Output e Reporting

Capital absorption computation engine

Credit risk inputs� Rating classes� LGD� Customer

segment (Basel 2)

� Transition PD matrices, multi-year matrices

Financial operation catalog

Risk parameters (PD, LGD, EAD,…)

Risk free curves

Input tables

Front end

Cu

stom

ized

re

por

tin

g

Classe ImportoClasse ImportoClasse ImportoClasse Importo/segmento/segmento/segmento/segmento

Classe Classe Classe Classe di PDdi PDdi PDdi PD

Classe Classe Classe Classe LGD / EaDLGD / EaDLGD / EaDLGD / EaD

1 2 3 4 5 6 7 8 ...

60%

50%

40%

30%

20%

10 000 €25 000 €

50 000 €

100 000 €16 27 34 44 60 82109136 ...

Retail

Hypercube with all

banking dimensions

� Pricing catalog cluster 1Pricing catalog cluster 1Pricing catalog cluster 1Pricing catalog cluster 1

� Financial operation 1 (cash Financial operation 1 (cash Financial operation 1 (cash Financial operation 1 (cash flow structure, warranties, flow structure, warranties, flow structure, warranties, flow structure, warranties, fees, operating costs, …)fees, operating costs, …)fees, operating costs, …)fees, operating costs, …)

� Financial operation 2 …Financial operation 2 …Financial operation 2 …Financial operation 2 …

� Pricing catalog cluster Pricing catalog cluster Pricing catalog cluster Pricing catalog cluster 2222

� …………

Ris

k a

dju

sted

p

rici

ng

cat

alog

Exceptions due to loan policy strategies and/or customer

specific features


Internal benchmarking allows to identify the internal target spread for both the current account as

well as loans: according to the “performance flag” a re-pricing strategy can be carried

2. Internal benchmark | Re-pricingPricing

A B IA B IA B IA B I ND G _S IS B AND G _S IS B AND G _S IS B AND G _S IS B A No me G e s t o reNo me G e s t o reNo me G e s t o reNo me G e s t o re A re a G e s t o reA re a G e s t o reA re a G e s t o reA re a G e s t o re

S p re a d d a S p re a d d a S p re a d d a S p re a d d a

I mp i e go I mp i e go I mp i e go I mp i e go

R e al i zzat o - R e al i zzat o - R e al i zzat o - R e al i zzat o -

R e v o c aR e v o c aR e v o c aR e v o c a

S p re ad d a S p re ad d a S p re ad d a S p re ad d a

Imp i e go Imp i e go Imp i e go Imp i e go

O b i e t t i v o - O b i e t t i v o - O b i e t t i v o - O b i e t t i v o -


F l ag F l ag F l ag F l ag

P e rf o rmanc e - P e rf o rmanc e - P e rf o rmanc e - P e rf o rmanc e -



Imp i e g o Imp i e g o Imp i e g o Imp i e g o


In f . 1 YIn f . 1 YIn f . 1 YIn f . 1 Y



O b i e t t i v o - I nf . O b i e t t i v o - I nf . O b i e t t i v o - I nf . O b i e t t i v o - I nf .

1 Y1 Y1 Y1 Y



Inf . 1 YInf . 1 YInf . 1 YInf . 1 Y


Imp i e g o Imp i e g o Imp i e g o Imp i e g o


S up . 1 YS up . 1 YS up . 1 YS up . 1 Y



O b i e t t i v o - O b i e t t i v o - O b i e t t i v o - O b i e t t i v o -

S u p . 1 YS u p . 1 YS u p . 1 YS u p . 1 Y



S up . 1 YS up . 1 YS up . 1 YS up . 1 Y

06175 000003046320 TACCHI MARIA CRISTINA AREA_GE_CENTRO 0,724% 4,231% 1 0,111% 0,878% 1

06175 000003104055 LAGUZZI GIUSEPPINA AREA_GE_LEVANTE 13,327% 4,231% 0 0,958% 0,987% 1

06175 000003465647 LAGUZZI GIUSEPPINA AREA_GE_LEVANTE 1,377% 4,231% 1 1,000% 0,987% 0

06175 000003556498 LAGUZZI GIUSEPPINA AREA_GE_LEVANTE 0,868% 0,987% 1

06175 000003850304 TACCHI MARIA CRISTINA AREA_GE_CENTRO 0,470% 0,987% 1

06175 000003071373 LAGUZZI GIUSEPPINA AREA_GE_LEVANTE 4,019% 4,635% 1

06175 000007097281 COSTA SABRINA AREA_GE_CENTRO 1,621% 4,635% 1

Actual spread

Target spread according to the customer’s cluster

Performance flag

(1= Under performing

0 = Over performing

Curent account: repricing strategies have the greatest impacts

XX_1

…

XX_n

AREA_1

…

AREA_m

Bnk_1

…

Bnk_p

Bank Customer Branch manager

AreaActual spreadCurr. Acc

Target spread

Flag perform

ance

Actual spreadLoan <1Y

Target spread

Flag performance

Actual spreadLoan >1Y

Target spread

Flag performance

The impact of repricing strategies as well as pricing strategies is strictly linked to the sensitivity of volumes to pricing changes (pricing sensitivity)


3. External benchmark | Second step of the cluster analysis

Pricing

In order to identify external benchmarks it is necessary to specify the financial features of the

operations we wish to consider (and target customers)

� The analysis can be carried out relying on:

� The overall banking system data

� Specific data collected on the market:

competitors

� In this latter case, we can consider both the

competitor’s pricing catalog as well as their

real prices (mistery shopping analysis)

Drivers of the analysis


4. Sensitivity | The impact on volumes

Pricing

According to a given sensitivity threshold, we can define the pricing strategy

Impact of alternative sensitivities

customer B

a 100.b.p. increase of the

price (starting from a

given price) leads to a

2% reduction of the

underwriting probability

customer A

a 100.b.p. increase of the

price (starting from a

given price) leads to a

20% reduction of the

underwriting probability

volume after volume after volume after volume after rererere----pricingpricingpricingpricing

initial probabilityinitial probabilityinitial probabilityinitial probability

volume after volume after volume after volume after rererere----pricingpricingpricingpricing

initial probabilityinitial probabilityinitial probabilityinitial probability

high sensitivity

low sensitivity

B


5. 6. Mark-up and Pricing catalog

Pricing

In order to maximize the profitability, a step function can be drawn according to: benchmarking, price

sensitivity and loan policy targets

AAA AA A BBB BB …

Pricing catalogPrice to value

Mark-up

Rating

EVA<0

Step function mark-up according to rating classes and cap due to competitors or law restrictions

Pricing cap

Pri

cing


Price to client | The roadmap

Pricing

The price to client is the final result of a series of analysis and bargaining activities

Price to market

Price to value Price to client

(final price)

Internal benchmark (internal average cluster price)

External benchmark

(system average cluster price)

(-/+)

(+)

Price to client roadmap

Loan policies

(eventualeventualeventualeventualpenalties due to loan policy targets)

(-)

Marketing policies

(relationship management: cross-selling, up-selling,… )

Mark-up


Pricing

According to category, product, rating, ... the ECAPro pricing engine shows the loan policy traffic light according to implemented policy rules simulating optimal customer-specific contractual conditions

Prometeia ECA solution | Simulation engine

Simulation of optimal conditions according to

Bank’s internal loan policy and pricing targets

Master Data Risk evaluation Position Action Plan


Pricing strategies are a crucial mechanics through which banks implement their lending strategies:

the risk-adjusted perspective induces banking management to develop new tools and procedures

Pricing | Implementation issuesPricing

Critical issues

� Interaction between financial and credit risk management

� Database:

� Integration information about: risk free curves, credit risk parameters, …

� FTP management, …

� Cluster specific pricing:

� Statistical analysis and expert analysis

� External pricing information and internal sensitivity analysis

� Pricing catalog:

� The definition of the pricing is linked to: strategic targets, marketing opportunities, risk appetite, …

� Price to client:

� Process re-engineering: exception rules, …


1 | Introduction


3 | Loan policies

4 | Pricing



RWA optimization, loan policies and pricing are strictly linked and integrated. Banks need to revise

their strategies considering jointly capital requirements, returns and risks

RWA optimization, Loan policies and pricing : remarksConcluding remarks

Methodology and information systems

� Introduction of (budget) targets on sector /

regional area / risk-adjusted returns, …

aligned with loan policies

� Integrated database: strategic planning

information, credit data, RWA projection, …

� Simulation tools to allow the definition of

coherent loan policies

� Supporting tools to be exploited in

implementing day-by-day activities and

monitor the executed operations

Organization and (risk) governance

� Definition of responsibilities and operative

workflow among credit dpt., risk management

dpt., strategic planning, …

� Coherence among planning-budgeting, capital

planning, concentration targets

“….. Institutions should ensure that (credit) “….. Institutions should ensure that (credit) “….. Institutions should ensure that (credit) “….. Institutions should ensure that (credit) concentration risk is taken into account concentration risk is taken into account concentration risk is taken into account concentration risk is taken into account adequately within their ICAAP and capital adequately within their ICAAP and capital adequately within their ICAAP and capital adequately within their ICAAP and capital planning frameworks. In particular, they should planning frameworks. In particular, they should planning frameworks. In particular, they should planning frameworks. In particular, they should assess, where relevant, the amount of capital assess, where relevant, the amount of capital assess, where relevant, the amount of capital assess, where relevant, the amount of capital which they consider to be adequate to hold given which they consider to be adequate to hold given which they consider to be adequate to hold given which they consider to be adequate to hold given the level of concentration risk in their the level of concentration risk in their the level of concentration risk in their the level of concentration risk in their portfolios…” portfolios…” portfolios…” portfolios…” (Source CEBS)(Source CEBS)(Source CEBS)(Source CEBS)


Loan policies need an integrated decision support framework where data, methodology and

managerial experiences are strictly linked together

Integrated credit risk management | Prometeia supportConcluding remarks

�SW solutions enabling the integrated management of portfolio risks�Capital projection and

customized product design development

�Access and profiling�Basic and advanced function

(i.e., drill down lending history, …)�Application form�Approval work-flow

�Methodological support to develop risk-adjusted planning metrics�Customer specific parameter

estimates

�Credit process analysis�GAP summary�Re-engineering assessment�Step-by-step implementation

consolidated track record

Process tailored solutions

Methodological solutions

RWA, economic capital, stress testing, capital

planning

ERMAS credit risk ECA

Comprehensive single-partner consulting and SW implementation offer for risk management

Credit risk management Process re-engineering


Prometeia

Via G. Marconi 43, 40122 Bolognatel. +39 051 6480911, fax +39 051 220753

Via M.Gonzaga 7, 20123 Milantel. +39 02 80505845, fax + 39 02 89074658

Via Tirso, 26, 00198 Rome tel. +39 06 45441350, fax +39 06 45441369

Italy

Prometeia middle east

7th flr, Dakdouk Bldg, Selim Bustros St.Tabaris Square, Ashrafieh - Beiruttel. +961 1 328233, fax +961 1 327233

Lebanon

www.prometeia.com

[email protected]

a rating-based framework for credit portfolio risk-return

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