The document discusses Basel II analytics covering the three pillars of credit risk, market risk, and operational risk. For credit risk, it describes various risk components like probability of default, exposure at default, loss given default, expected loss, and different approaches for calculating capital requirements. For market risk, it mentions risks like market data analysis, sensitivity modeling, and stress testing. For operational risk, it discusses the basic indicator approach, standardized approach, and advanced measurement approach. It also covers the internal capital adequacy assessment process under Pillar 2 and market discipline requirements under Pillar 3.
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Connecting the dots: regulatory reforms in SingaporeKate Saunders
Whilst risks to the global outlook remain, regulatory reforms in the financial sector by the Monetary Authority of Singapore (MAS) are continuing. At our April briefing, we looked at some of the relevant topics in MAS’ pursuit to strengthening the resilience of Singapore’s financial system through regulatory reforms.
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Outlook and market survey on the fresh Standards for Minimum capital requirements for market risk (FRTB), published January 14th, 2016.
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CH&Co can assist banks navigate through these fundamental changes
Outlook and market survey on the fresh Standards for Minimum capital requirements for market risk, published January 14th, 2016.
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CH&Co can help banks cope with these changes.
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ABSTRACT: This research aims to examine how ESG disclosure and risk disclosure affect the total risk of
companies. Using cross section data from 355 companies listed in Indonesia Stock Exchange, data regarding
ESG disclosure and risk was collected. In this research, ESG and risk disclosures are measured based on content
analysis using GRI 4 guidelines for ESG disclosures and COSO ERM for risk disclosures. Using multiple
regression, it is concluded that only risk disclosure can reduce the company's total risk, while ESG disclosure
cannot affect the company's total risk. This shows that only risk disclosure is relevant in determining a
company's total risk.
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leadership recruitment processes, ethnic and regional politics, tribalism and mediocrity, poor planning, and
variation of project design have been the causative factors that undermine projects implementation in postindependence African states, particularly in Nigeria. The study, thus, argued that successive governments of
African states, using Nigeria as a case study, have been deeply engrossed in this obnoxious practice that has
undermined infrastructure sector development as well as enthroned impoverishment and mass poverty in these
African countries. This study, therefore, is posed to examine the similarities in causative factors, effects and
consequences of corruption and how it affects governance, projects implementation and national growth. To
achieve this, the study adopted historical research design which is qualitative and explorative in nature. The
study among others suggests that the governments of developing countries should shun corruption and other
forms of obnoxious practices in order to operate effective and efficient systems that promote good governance
and ensure there is adequate projects implementation which are the attributes of a responsible government and
good leadership. Policy makers should also prioritize policy objectives and competence to ensure that policies
are fully implemented within stipulated time frame.
KEYWORDS: Developing Countries, Nigeria, Government, Project Implementation, Project Failure
“To be integrated is to feel secure, to feel connected.” The views and experi...AJHSSR Journal
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successes and failures since their implementation in 2014, there is limited research on the integration of subSaharan African children into schools. This paperis part of a Ph.D. research project that aims to fill this gap. It
reports the main findings of a study conducted with migrant children enrolled in two public schools in Rabat,
Morocco, exploring how integration is defined by the children themselves and identifying the obstacles that they
have encountered thus far. The following paper uses an inductive approach and primarily focuses on the
relationships of children with their teachers and peers as a key aspect of integration for students with a migration
background. The study has led to several crucial findings. It emphasizes the significance of speaking Colloquial
Moroccan Arabic (Darija) and being part of a community for effective integration. Moreover, it reveals that the
use of Modern Standard Arabic as the language of instruction in schools is a source of frustration for students,
indicating the need for language policy reform. The study underlines the importanceof considering the
children‟s agency when being integrated into mainstream public schools.
.
KEYWORDS: migration, education, integration, sub-Saharan African children, public school
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partnerships. A case study design of the qualitative research method was used to achieve this objective. For the
study, one polytechnic college of Oromia regional state, and two industries were purposively selected. From the
sample polytechnic college and industries, a total of 17 sample respondents were selected. Out of 17
respondents, 10 respondents were selected using the snowball sampling method, and the rest 7 respondents were
selected using the purposive sampling technique. The qualitative data were collected through an in-depth
interview and document analysis. The data were analyzed using thematic approaches. The findings revealed that
TVET-industry partnerships were found weak. Lack of key stakeholder‟s awareness shortage of improved
training equipment and machines in polytechnic colleges, absence of trainee health insurance policy, lack of
incentive mechanisms for private industries, lack of employer industries involvement in designing and
developing occupational standards, and preparation of curriculum were some of the impediments of TVETindustry partnership. Based on the findings it was recommended that the Oromia TVET bureau in collaboration
with other relevant concerned regional authorities and TVET colleges, set new strategies for creating strong
awareness for industries, companies, and other relevant stakeholders on the purpose and advantages of
implementing successful TVET-industry partnership. Finally, the Oromia regional government in collaboration
with the TVET bureau needs to create policy-supported incentive strategies such as giving occasional privileges
of duty-free import, tax reduction, and regional government recognition awards based on the level of partnership
contribution to TVET institutions in promoting TVET-industry partnership.
KEY WORDS: employability skills, industries, and partnership
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4. 4
The Basel II Framework
Pillar 1
– Minimum Capital Requirement Calculation
Credit Risk
Market Risk (little changes vs. Basel I)
Operational Risk
– Regulatory Reporting
Pillar 2
– Internal Capital Adequacy Assessment Process
Capital requirement vs. capital estimates
Risk Management
– Pillar 1 Risks
– Credit risk concentration
– Interest Rate Risk in the banking book
– Other Risks : Liquidity, Reputation, Strategic, …
– Supervisory Review Process
Audit (External, Internal)
Regulatory Supervision
Pillar 3
– Firms will have to publish their risk profile and risk data
Supplementary Pillar III reporting, Annexes of Balance Sheet, …
6. 6
PILLAR 1 PILLAR 3PILLAR 2
Increased
Supervisory
Power
Increased
Disclosure
Requirements
Minimum
Capital
Requirement
Market
Discipline
Requirements
Supervisory
Review
Process
Rules
To Calculate
Required Capital
New Regulatory Structure Based on Three Pillars
Capital
Adequacy
Basel II – the Three Pillars
7. Basel II 3 Pillar Analytics
Pillar I : Capital Adequacy Calculations
Credit Risk Market Risk Operational Risk
Calibration
of
EAD
Calculation
of
Risk Weights
Based on PD & LGD
Limits and
Collateral
System
-Counterparty Risk
-Country & Corporate
Concentration Risk
ALM Stress testing
Financial Projection
Models
Upgrade Internal
Rating Systems
Prudential Limit
Global Limit
Country Limit
Private sector Limit
Portfolio and Asset
Concentration Risk
Interest Rate
Risk and Basis Risk
Support for all 3
approaches
Basic Indicator Approach: Capital
Calculated as a percentage of Gross
Income
Standardised Approach: Line of
Business Based Exposure Indicators
Advanced Measurement
Approach: Capital computation as
per Opsrisk Loss Data Approach
Approach:
Internal Ratings Based Approach (Advanced): IRBA
Standardised
Measurement
Methods
Pillar II : Supervisory Oversight Pillar III : Market Discipline
Usage of Metadata ICAAP, Economic Capital,
RAROC*
Rules Based Engine
Capital Adequacy Reporting
Quantitative DisclosuresRisk Assessment Reports
Flexible Reporting
Qualitative Disclosures
Equity Position
Risk
Currency
Risk
Liquidity
Risk
VaR
Calibration
of
PD’s / LGD’s
Definition
of
RWA
*Risk-Adjusted Return on Capital (RAROC)
8. 8
Basel II Risk Analytic Coverage
Basic
Indicator
Standardised
Advanced
Foundation
Standardised
IRB
Operational
Credit
Pillar 2
Regulatory Review
Pillar 3
Market Discipline
Pillar 1
Capital Requirements
Market
Advanced
9. 9
Approaches Risk Components Mitigation
Basel I Counterparty Nature
(Sov, Corp, OECD country etc)
Supervisory values
Limited set of eligible risk mitigants
Substitution of RW
Basel II
Standardised
External Ratings
Supervisory values
Limited set of eligible risk mitigants
Basel II
IRB Foundation
PD : by the bank
LGD, EAD : fixed
More eligible mitigants
Apply on PD, LGD, EAD
Basel II
IRB Advanced
PD, LGD EAD : by the
bank
Even more eligible mitigants
Apply on PD, LGD, EAD
Basel Credit Risk Approaches Overview
10. 10
Changed Capital Requirement
Minimum Regulatory
Capital
Capital
(Credit & Market) Risk adjusted assets
= 8%
Minimum Regulatory
Capital
Capital
Credit
risk
Operational
risk
Market
risk
=
+ +
8%
Basel II
Basel I
11. 11
Credit Risk
• Basel II places emphasis on improving the management and
measurement of credit risk
• The measurement of credit risk implies assessing the borrower’s
creditworthiness.
12. 12
1. What is the probability of a
counterparty going into default?
2. How much will that customer
owe the bank in the case of
default? (Expected Exposure)
3. How much of that exposure
is the bank going to lose?
“Probability of Default”
“Loan Equivalency”
(Exposure at Default)
“Severity”
(Loss Given Default)
PD
LGD
EaD
=
=
=
X
X
Size of Expected Loss “Expected Loss“ EL=
=
Components of Credit Risk
13. 13
Expected Loss
(EL) =
Probability of
Default
(PD)
Severity of Loss
(LGD)
Exposure at
Defaultxx
Standardise = External x Regulatory x Regulatory
Rating Imposed Imposed
IRB = Proprietary x Regulatory x Regulatory
Foundation Rating Imposed Imposed
IRB = Proprietary x Proprietary x Proprietary
Advanced Rating Severity Exposure
Credit Risk Components
14. Credit Risk – Functional Architecture
1
4
Evolution of regulatory framework has added to the complexity of models requiring banks to further
their:
Risk Control and reporting process
Data Management
Processing capabilities of the systems
16. Market Risk
16
Experience Snapshot
Process Consulting for
VaR Calculation for
leading bank in Singapore
Portfolio Assessment
Modeling for large hedge
fund
Performance Evaluation
System for One of the top
US Investment
Consultants
Strategic Exposure Limits
Management for One of
the Largest Investment
Banks
Client Information
Management System
(CIMS) for Leading US
West Coast Bank
18. 18
Operational Risk
• Capital requirement for Operational Risk (OR) introduced
• Banks’ OR models not as developed as for Credit Risk
• Operational Risk (OR) will add to banks’ regulatory capital
requirements
• Increased cost for OR might offset any capital savings on Credit Risk
• Operational risk is not restricted to banks, it’s present in all
organisations including yours
19. Operational Risk Application
19
Experience Snapshot
Operational Risk Data
Capture for Large
European Bank
Capital Charge
Calculation & Reporting
for Large Canadian
Bank
Operational Risk
Management System for
Reputed Bank in
Scotland
Operational Risk Data
Capture & Reporting for
Premier Provider of
Asset Servicing, Fund
Admin & Investment
Mgmt.
20. Liquidity Risk Framework
20
Liquidity Risk
Governance &
Oversight
Measurement
Management
R
eporting
Systems &
Controls
O
ff-
Balance
SheetItem
s
Asset-
Liability
M
ism
atch
Regulatory
Reporting
ContingencyFunding Plan
Diversification of
Sources of Funds
Liquid Asset
Buffer
Emergency Day – to - Day
Models
Metrics
Early
W
arning
Indicators
Probabilistic
Behavioural
Scenario
21. 21
Internal Capital Adequacy Assessment Process
Emphasis is on ‘P’ – Process
Confusingly, ICAAP now also refers to the calculated capital figure
A process by which a firm assesses its risks and mitigation for its
business and sets appropriate levels of risk capital
An ICAAP is specific to each firm
– Minimum standards apply
– Fit for its purpose
– Appropriate to the risks assumed
There is no prescriptive definition of an ICAAP.
– senior management ownership and responsibility for own process
– FSA will review through ARROW assessments.
22. 22
Overview of Pillar 2 and ICAAP
CAPITAL: Relationship between Pillar 1, Pillar 2 and the ICAAP
minimum capital
requirement;
calculated using
prescribed parameters
(advanced or
standardised).
Pillar 1 Pillar 2 ICAAP
the firm's own assessment
of its capital needs;
need not be calculated by
reference to regulatory
capital (firms which use
economic capital models
will express their capital
using a variety of measures
e.g. tier 1, shareholders
funds).
supervisory assessment of
the amount of regulatory
capital necessary to cover:
Pillar 1 risks (including any
uncertainties in their
calculation); and
risks not included in Pillar 1.
calculated on a forward-
looking basis through, at
least, an economic
downturn.
23. 23
ICAAP should covers …
Other Risk Types
Interest rate risks in the banking book
–Maturity transformation
Pension risk
–Liabilities
Business risk
–Market volume volatility
–Competition
Liquidity risks
–Funding & Refinancing
Strategic risks
–Political / Legal / Social
Risk types in pillar 1
Counterparty risks
–Credit risk
–Settlement risk
–Country risk
–Equity risk
Operational risks
–Risks caused by persons,
processes, technology and external
impacts
Market risks in the trading book
–Interest rate risks
–Special risks
–Currency risks
–Credit spreads
26. Integrated Risk & Finance Analytics View
Covering RAROC, VaR, RWA, Operational, Market & Credit Reporting
Data Warehouse: The Bank wide data warehouse stores the raw and processed
data from the calculation engines. It holds transaction level data and enables
views of the data by multiple dimensions e.g. counterparty, general ledger
account, functional organization, product etc. data is extracted from the business
units specific systems as frequently as is required to provide timely and
meaningful bank wide views of risk
RAROC
Capital
ELELExpensesvenue CROR
Re
Credit
Data
Integration&enrichment
Risk & FINANCE DW (Economic Data)
-Loans & Borrowings
-Economic Capital
-Revenue
-Expense
-Budgets
-Risk Capital charges
Risk & FINANCE DW
-Risk Capital- Prudential Limits (RWA)
-RWA for exposures
-Investment Portfolio
-Expected Loss EL
META DATA & BUSINESS RULES
SUPPORT
-Common meta data
-Business rules definitions & support
Integration&enrichment
RWA (Regulatory) Engines
Analytics Engines
EOD Calculators
-VaR
-Stress Testing
-Back Testing
-Prudential Limits
-Operational Risk (via
Dashboard)
INTRADAY Calculators
-VaR
-Trade position
-Real Time Limits
-Desk Level Analytics
-Operational Availability
(via dashboard)
Accounting Engine
-P&L
-RAROC
-Other Accounting Measures GL
Data Mart
(Regulatory
Reporting)
Data Mart
(Economic
Reporting)
Data Mart
(Operational Risk
Dashboard)
Reporting
Architecture
Reporting
Engine
Reporting
Engine
ServicesAPIs
RISK DIMENSIONS:
-Market Risk
-Credit Risk
-Operational Risk
-Prudential & Operational Limits
-Risk Capital Charges
& Measures
1
2
3
1. Example RAPM equation for illustration
2. This represents a shared architecture for both EOD & intraday pre deal analytics 3. RISK DASHBOARD for operational quantitative & graphical risk evaluations
Financial
Data
Client risk
Data
Market risk
Data
ODS’s
Data
Business
Actors
Traders
Debt
Managers
Operations
Accounting
Management
Regulators
Compliance
Pre deal RWA
Intraday / pre
deal
analytics
27. 27
Regulatory compliance such as Dodd‐Frank, Basel II & Basel III require Big Data demands placed on financial firms
to track the source of data, how it has changed over time, and who has changed
Critical success factors
Integration of risk data from different source & building large iCAAP risk warehouse
Creation of counterparty risk environment to support AIRB implementation of Basel II
Integration of business definition, metadata and data Governance across business lines into iCAAP warehouse to facilitate
reporting
Creation of Risk Analytic reporting to support Basel II – Capital & Economic calculation and Fed reporting
Configuration and support of 3rd party Risk calculation engines and RWA calculation for Basel II reporting
Critical success factors
Integration of risk data from different source & building large iCAAP risk warehouse
Creation of counterparty risk environment to support AIRB implementation of Basel II
Integration of business definition, metadata and data Governance across business lines into iCAAP warehouse to facilitate
reporting
Creation of Risk Analytic reporting to support Basel II – Capital & Economic calculation and Fed reporting
Configuration and support of 3rd party Risk calculation engines and RWA calculation for Basel II reporting
Envisaged Benefits
Reduced close to 55% of the risk based capital allocation
Compliant within a year due to the implementation of large iCAAP data warehouse
Meet Fed Basel II reporting needs
Creation of counterparty risk data mart to facilitate internal risk ranks
Scenario
Dodd‐Frank regulation – Large volumes of OTC derivatives need to be cleared at CCPs (Central
Counter Party) require the clearing and risk management systems to be able to handle the volumes
Basel II & III regulations – Key requirements for voluminous credit data storage and management
include maintaining a cradle‐to‐grave history of obligors increasing data storage needs. Analytical
reporting as per Basel III for calculating NSFR (Net Stable Funding Ratio) & LCR (Liquidity Coverage
Ratio) also need large volumes of data processing
Big Data Usage
Integration and Aggregation
Storage Management
Processing
Analytics
Regulatory Compliance Driving Big Data Analytics
30. Integrated & Unified Trade Data Analytics
30
Big Data Usage
• Processing
• Analytics
• Integration and Aggregation
• Storage Management
• Reporting / Dashboarding
• Monitoring
Consolidate the trades & positions across all asset classes and geographies for a birds eye view trade performance,
risk, trade analytics, and optimization of trading costs
The need for multiple desks trading
similar products to leverage
same/aligned process flows enabling the
firm to increase trade efficiency and
accuracy, decrease the time required to
market new products, and adapt more
quickly to changing market conditions
Analyze Volume and size of trades:
By desk, LoB, product, execution
venue, clearing venue and
geography. Trade Volumes help to
obtain “per trade” metrics (cost,
revenue, profits, resources,
technology)
Calculate / Unify risk: Get a single
view into market and credit risk.
Calculate credit risk across
products / LE for a counterparty
Identify distribution of execution
costs by LoB, products and desks.
An analysis can result in
rationalization of technology,
people and processes to optimize
execution cost structures
Unified
Trade
Data
Analytics /
MIS
Client
Service
Risk &
Profitability
Calculations
Cost Control
& Operations
Mgmt
Scenario Scenario
Scenario Scenario
31. Integrated & Unified Trade Data Analytics | Envisaged Benefits
31
Improved Post Trade support and “Where’s my Trade” transparency
Seamless presentation of Client data and dashboards
Better communication with clients with better management of trade
confirmations
Support for sales trader, execution management and international
order handling across asset classes
Better information resulting in faster product roll outs; better
pricing
Cross product margining can be used to provide material benefits to
clients
Enhanced, top‐down view of internal trading volumes, the financials
of each trade, the counterparties involved and execution metrics
MIS information like
• Trends in revenue, profitability and costs
• Trade distributions by execution venues, clearing venues,
LoB, Desk and Product
Correlations between revenue, cost and profitability. Negative
correlations indicate that investments are not producing sufficient
returns
Identify profitability by client/s: Identification of profitable clients
would result in optimization of investments in Sales (people,
processes and tools) to retain valuable customers and go after more
profitable segments
Correctly apportion ‘real’ costs and identify most profitable business
units
Improve the speed and execution accuracy of hedging activities with
one consolidated view of positions across asset classes. This can help
the firm’s hedging activities focus internally and not often priced by
and remain on the books of– the originating desk. Consequently
there will be less deals facing the street and more internally. This
will also lead to broker fee savings and wire transfer/administrative
fee savings
Affirmations tracking; improved trade tracking and enhanced STP
Distribution of funding and collateral costs. An analysis would result
in better transfer pricing mechanisms and tools for banks
Bank can optimize which brokers it uses (internal / external) based
what they charge
Improve costs & overheads associated with Reconciliation
Improvements in Regulatory Reporting processes
Accurately calculate PnL, build centralized PnL calculators
Improvement in cross product netting can free up a lot of capital
Improved ability to simulate Risk Stress scenarios and identify risk
concentrations. Stress is a hot topic in regulatory space
Pre‐trade risk checks become possible with unified trade
information
Distribution of Credit, Market and Operational risks
Client Service: Unified Trade data can facilitate.. Analytics & MIS: Unified Trade data can be used for..
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