onno de vrij (sas) better decision making 12-10
TRANSCRIPT
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RISK DECISIONINGRISK IN FINANCE - NIJENRODE CONGRES
ONNO DE VRIJ12 OCTOBER 2015
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THE BUSINESS VIEW ON MODELS
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MODELS WHAT IS THE BUSINESS BENEFIT?
Better decisions
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ANALYTICS MATURITY
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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
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MODELS PERCEPTION VS REALITY?
What we want … What a model gives us … What is required to achieve goal …
Perception Reality
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MODELS ANALYTICAL LIFECYCLE
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MODEL VALIDATION NOT JUST TECHNICAL
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MODEL VALIDATION NOT JUST TECHNICAL
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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%
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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
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MODELS ANALYTICAL LIFECYCLE
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CLOSE THE LOOP SHARE RESULTS
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CLOSE THE LOOP SHARE RESULTS
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THE REGULATORY VIEW ON MODELS
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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
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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
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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”
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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:
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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.
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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
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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
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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
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MODEL REGULATION 3. PUSH FOR MORE MODELS
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IFRS 9
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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)
.
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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
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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
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THE ‘FACTORY’ VIEW ON MODELS
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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
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SUMMARIZING
Better Decisions
High quality
In time
Wide scope
Automate
Culture
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