Onno de vrij (sas) better decision making 12-10

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RISK DECISIONINGRISK IN FINANCE - NIJENRODE CONGRES

ONNO DE VRIJ12 OCTOBER 2015


THE BUSINESS VIEW ON MODELS


MODELS WHAT IS THE BUSINESS BENEFIT?

Better decisions


ANALYTICS MATURITY


ANALYTICS IN ORGANIZATIONS ORGANIZATIONS APPROACH ANALYTICS DIFFERENTLY

Embrace Analytics Modernize

Analytical Platform

Visualize and explore data Innovate with

advanced algorithms

Development of Analytics

Top down(Strategy and vision including analytics, full attention on board level)

Bottom up(A fragmented approach on analytics, initiated on a department level or individuals)

Immature(No structured process, ad hoc analytics, partly or not using analytical life cycle

Mature(Structured process, organized and more need for centralized approach using analytical life cycle

Next development step

Analytical approach

Analytical maturity


MODELS PERCEPTION VS REALITY?

What we want … What a model gives us … What is required to achieve goal …

Perception Reality


MODELS ANALYTICAL LIFECYCLE


MODEL VALIDATION NOT JUST TECHNICAL


MODEL VALIDATION NOT JUST TECHNICAL


ANALYTICS IS A PROCESS AVERAGE TIME SPEND BEFORE ACTION

TIME TO DECISION

INEFFIC

IENC

Y

RE-W

OR

K

LOST TIM

E

ActDeploy models

and scoreDevelop and test models

Access data and prepare

50% 20% 30%


ANALYTICS IS A PROCESS CLOSING GAPS TO IMPROVE TIME-TO-MARKET

TIME TO DECISION VALUE CAPTURED

INEFFIC

IENC

Y

RE-W

OR

K

LOST TIM

E

ActDeploy models

and scoreDevelop and test models

Access data and prepare


MODELS ANALYTICAL LIFECYCLE


CLOSE THE LOOP SHARE RESULTS


CLOSE THE LOOP SHARE RESULTS


THE REGULATORY VIEW ON MODELS

Copyr igh t © 2012 , SAS Ins t i t u te I nc . A l l r i ghts res erved .

RISK IN BANKING TRENDS IN THE MARKET

REGULATOR:StandardizationBenchmarking

General models / rules

BANK:Personalization

Real-timeSpecific models

Different needs. Demand a Robust & Flexible Risk Environment


MODEL REGULATION IT IS GOING TO BE LONG AND TOUGH RUNRegulators

EBA’s

Model Quality

Review

IFRS 9,

Stress testing

Focus on

whole

modeling

lifecycle

More

governance

More

models

Faster

deployment /

performance

Picture source: Satoshi Kambayashi

1. Push for more model governance

2. Push for faster deployment and performance of models

3. Push for more modelsMRM Regulatory Run 2015 / 2016 / 2017/ 2018

Bankers

Basel III

CRD reporting

AQR / Anacredit

Basel IV

BCBS 239

Reg

ulat

ory

focu

s

ILAAP


MODEL REGULATION 1. PUSH FOR MORE MODEL GOVERNANCE

Financial Times, August 16th:“The European Central Bank has quietly given itself up to four years for an “intrusive” review that could force the eurozone’s biggest banks to hold even more capital. Having originally hoped to complete the review of banks’ risk models within a year or two, the ECN has set a deadline of four years for work on the project, according to a tender document seen by the Financial Times.”

3 main areas of ECB / EBA’s recent activities:I. Supervisory review and evaluation process (SREP) guidelines (EBA/GL/2014/13) with a

dedicated section on how the Model Risk (Management) should be reviewed by the regulators.

Deloitte: Overall, the Guidelines represent a major shift in terms of breath of risk coverage in the SREP and the detail of the approach for carrying it out. Taken at face value, the Guidelines will require a significant increase in the resources dedicated to the SREP, both by banks and, in some cases, by supervisors too.

II. Benchmarking exercise (ITS/2015/01)

III. Specifications for Ongoing Assessment of IRB approaches (EBA/CP/2014/36) “Model Quality Review”


MODEL REGULATION SREP GUIDELINES AND MODEL RISK MANAGEMENT- EXAMPLE

Summary of questions# to be asked by each country’s Regulator

Assessment of model risk

SENIOR MANAGEMENT RESPONSIBILITY (188, 233f) Does Senior Management understand degree of model risk in credit, market & operational risk?

SCOPE OF REVIEW (264): To what extent are models used to support significant business decisions ?

IMPACT (267): How significant is model risk (is there sensitivity, scenario & stress testing) ?

VALIDATION (285): How sound are model validation & review processes ?

CONTROLS (265): What are model risk control mechanisms & how are these tested ?

• SREP = Supervisory Review and Evaluation Process# Numbers in each section are SREP Guideline paragraphs

Pillar II capital add-ons for model risk under SREP

capital requirement

SREP Guidelines

Defines the notion of Model Risk and how it should be assessed by

the regulators

Applicable from 1.1.2016

233f: Awareness of the degree of model risk that prevails in the institution’s

market risk pricing models and risk measurement

techniques

Not just IRB models:


SAS MODEL RISK MANAGEMENT DISCOVER SUCCESS STORY

• 680+ models; 300 usages/applications created

• 1000+ findings & 1000+ action plans back loaded

• Cataloged 12,000+ linked instances to establish a chain of dependency among model, validation, users, and roles.

• Established a 360° link between model, usage, stakeholders, validation, issues and action plans for model lifecycle management.


MODEL REGULATION 2. PUSH FOR FASTER MODEL DEPLOYMENT AND PERFORMANCE

Model Governance Run 2015 / 2016 / 2017

• BCBS 239The principles for Risk Data Aggregation and Reporting push for faster calculation / modelling runs and reporting. Larger banks has to follow these principles also in the modelling area.

• Increasing regulatory focus on model deployment – SAS use casesSAS has helped 2 large banks (Ireland, Brazil) which have been approached by the regulator and asked to significantly reduce the duration of the end-to-end Credit Risk modelling process including the model deployment.

Efficiency and robustness of the Model development

and Model deployment processes can have

significant impact on the overall Model Risk and management thereof


MODELING LIFECYCLE IMPROVEMENT

The Irish bank case:• Large Irish bank was approached by regulator with request to vastly reduce the duration of the whole end-to-end process (could be up to 18 months). The maximum time for deployment was set to 2-3 months. The Irish bank challenge:• Hand over between the modeling team and deployment team where modelling code had to be

manually retyped and data reconnected by IT people lacking the modeling / business insightsThe Irish bank solution• Upgrade of modeling platform which allows sharing the data linkage and modeling code between

model developers and the deployers • SAS Credit Scoring Solution running in database in Teradata (unique feature of SAS) that allows

the modelling code to directly run within Teradata without any need for rewriting• Grid / Parallel computing for increased performance• The ambition is to reduce the end-to-end time for IRB models from 15 months down to 3 months,

including also the data preparation

SAS USE CASE 1


MODELING LIFECYCLE IMPROVEMENT

The Brazilian bank case• Similarly a large Brazilian bank needed to vastly improve the speed of the end-to-end process (was 8-12 months) but also the governance around it. Furthermore also improvement of the loan approval process was in scope.The Brazilian bank solution• In the addition to the Irish solution:

• data exploration and analytics toolkit for supporting of pre-modelling data analysis and soft-business validation of the models

• Beside the scoring also the Analytics / Data quality performed directly in database - Teradata (SAS “accelerators” together with High-performance data mining / analytics)

• Platform for credit approving rules and real time scoring and model governance toolkit• As a result, the end-to-end process (including data preparation) went from 8-12 months

down to 2 months

SAS USE CASE 2


MODEL REGULATION 3. PUSH FOR MORE MODELS

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IFRS 9


LOAN IMPAIRMENTS UNDER IFRS 9 VS IAS 39IFRS 9 EXPLAINED Loan impairment is accounting action of decreasing the loan’s value when the (full)

recovery is in doubt and impacts both the bank’s Balance Sheet (Loan Loss Allowance or Provision) and also the Profit (Impairment losses or Loan Loss Allowances). This impact is then subsequently carried on to the Capital Adequacy Ratio as well.

IFRS 9 replaces IAS 39 (from 2018 onwards) and introduces the Expected Credit Loss (ECL) loan impairment approach that covers also future losses and applies to all financial assets at inception (under IAS 39 it is only incurred losses and the provisions are made only for e.g. loans with overdue payments)

ECL is the weighted estimated impact of future default on the expected cashflows based on estimates of :

• Probability of Default (PD)• Loss Given Default (LGD)• Macroeconomic conditions• ….

• Contrary to the existing credit risk models IFRS 9 requires in number of instances the usage of lifetime Expected Loss as opposed to 1 year Expected Loss (Basel)

.


LOAN PROVISIONING MORE RELEVANT IN RISK & FINANCEIFRS 9 EXPLAINED

1) Push on Equity:the increase of provisions will constantly decrease the equity - will make it more volatile.

2) Push on Profit:the increase of provisions will generate one-off hit on Profit. After implementation the P/L will become more volatile

3) Push on Available Capital:Impact on Equity will transfer to certain extent into the Available Capital as well

4) Required Capital: Impact will differ depending on the chosen approach (IRB / SA) and loan specific details. Generally a slight increase is expected by the industry

CAR

5) Push on Capital Adequacy Ratio (CAR)Impact on available capital will push CAR down

FINANCE (IFRS 9)

RISK (BASEL)

Loan Loss Provisioning

Available Capital

Required Capital

Balance Sheet Assets (Equity)

Income Statement Expenses (Profit)

CAR


NEED OPTIMAL BALANCE

Focus on Impact optimization and not just the compliance

Compliance Optimal Financial Impact

Day 1 hit on P/L and Capital Buffer

Ongoing future volatility

Implementation Costs + Benefits

Workload

IFRS 9 IMPLEMENTATION


THE ‘FACTORY’ VIEW ON MODELS


SAS ANALYTICS FACTORY

CLOSED LOOP ANALYTICS ENABLING OPTIMAL MODEL DEPLOYMENT

SOURCE / OPERATIONAL

SYSTEMS

MODEL MANAGEMENT

MODEL DEVELOPMENTDATA PREPARATION

MODEL DEPLOYMENT

Leverages data quality and governance functionality to monitor and identify analytical model degradation as well as the data used for analytics


SUMMARIZING

Better Decisions

High quality

In time

Wide scope

Automate

Culture


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Category:	Presentations & Public Speaking
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Onno de vrij (sas) better decision making 12-10

Presentations & Public Speaking