The Role of Actuaries in Banking
17 February 2023
C. Chimwanda, FRM, CQF
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Disclaimer
This presentation is intended for educational purposes only and does not replace
independent professional judgement. Statements of fact and opinions expressed
are those of the presenter individually and, unless expressly stated to the
contrary, are not the opinion or position of my current or previous employers, the
Actuarial Society of Zimbabwe, its cosponsors, or its committees. The presenters
opinions are based upon information considered reliable. Distribution of the
presentation without written permission of the presenter is prohibited.
Contents
1. What are banks?
2. Bank regulation
3. Basel III Framework.
4. Modelling.
5. Enterprise Risk Management
6. Discussion
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What is a Bank?
A bank is an institution whose current operations consist in granting loans and receiving deposits from
the public [Freixas and Rochet(2008)].
Any Entity Licenced by banking regulators to operate as a bank.
Functions of Banks [Freixas and Rochet(2008)]:
o Liquidity and Payment Services;
o Transforming Assets (Convenience of denomination, Quality Transformation, Maturity Transformation)
o Managing Risks (Credit, Market, Liquidity, Operational and Other Risks);
o Processing information and monitoring borrowers (gives rise to informational asymmetries); and
o Role in Resource Allocation Process.( bank-oriented vs market-oriented economies ).
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Structure of Banks
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Major Players in the Global Financial Reforms in the aftermath of the 2008 GFC
Existed Before Crisis
Existed After the Crisis
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Need for Regulation in Banking
Financial Regulators responsible for, among others, fostering financial stability.
Post Global Financial Crisis (GFC) regulatory reforms, more focused on systemic stability and
macroprudential regulation.
Minimising the impact of contagion and reducing systemic risk in the banking system.
The major assumption being: greater capital, among other things, reduces system-wide fragility.
Improves borrower screening and risk monitoring functions of banks.
Reduces Moral Hazard as banks also provide a chunk of equity for every loan created.
Minimum capital requirements a combination of a dollar based floor and risk based measures.
NDS1 (Para 194 to 202).
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From Basel I to III
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Basel III Framework
Basel III
Pillar 1 – Risk- Leverage TLAC Pillar 2 Pillar 3
based
Ratio1
Capital RWA Liquidity Large
Risk Exposures
Operational
CET1 Credit CCR Market
Liquidity Interest Rate Risk in the
Banking Book (IRRBB)
SMA
AT1 SA SA-CCR2 Standard
Coverage
Ratio (LCR)
Tier 2 F-IRB IMM IMA
New with Basel III*
Net Stable
+ Funding
A-IRB
CVA Risk
ES
Ratio Substantially updated
Charge (NSFR) with Basel III (includes
Conservation Buffer Basel 2.5)
+ DRC
Exposures Securitization Finalised in December 2017
Macroprudential to CCPs
SES
Countercyclical Buffer
+ Consultations for
Margin OTC Revisions June 2018
Derivatives
G-SIBs & D-SIBs Buffer
1 Was scheduled to migrate to Pillar 1 from 2018 onwards
2 Replaces existing current exposure and standardised methods effective 1 Jan 2017 9
Pillar II
Principle 1 ICAAP (Internal Capital Adequacy
Bank
Assessment Process)
Banks to have process for assessing
adequacy of capital in relation to risk profile
Principle 2
Supervisors to review and evaluate banks’
internal capital adequacy assessments
Principle 3 Regulatory
Supervisory Review Process (SREP) Authorities
Supervisors to expect banks to operate
above regulatory minimum capital ratios
Principle 4
Supervisors to intervene and take action to
prevent capital falling below minimum
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Internal Capital Adequacy Assessment Process (ICAAP)
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Regulatory versus Economic Capital
Regulatory Capital Economic Capital
Buffer Buffer
Other Risks (Business
Risk, etc.)
Core Capital Market Risk Capital Tier A Market Risk Capital
Tier 1 Core Capital
Operational Operational
Risk Capital Risk Capital
Supplementary
Capital Credit Credit
Risk Risk
Tier 2 Capital Tier B Capital
Supplementary
Capital
Available Required Available Required
Financial Regulatory Financial Economic
Resources Capital Resources Capital
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Insurance vs Banking Banking Overview
Insurance Banking
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Quant vs Actuary Banking Overview
Actuary Quant
Insurance Banking
Life and Non-life Commercial and
Insurance Trading desk
products Data analysis Credit Products
Model
development
Programming
Risk Management
Pricing
Regulatory
Compliance
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Key Technical Actuarial Skillsets for enhancing banking sector
performance
Analyzing trends for predicting future outcomes
Mathematical and Statistical skills
Understanding of financial and business concepts
Computer programming skills (R, Python, SaS, Java, etc.)
Key Non-Technical Actuarial Skillsets for enhancing banking sector
performance
Communication
Emotional Intelligence
Critical Thinking
Model Life Cycle
Review
Review
Validation
Validation Request
Request
Validation
Validation
Model
Review
Request
Implementation
Implementation
Validation
Validation
Model
Implementation
Requirements
Data
Policies
Collection
Methodological
Testing
Choices
Computing
Engine
Design
Design
Validation
Validation
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Risk Analytics
Risk measurement techniques
Credit Risk:
• Naïve Bayesian
• Survival Analysis
Capital Requirement Assessment
• Cox Proportional Hazards
• Kaplan-Meier Curves
• Black-Scholes Model
• Other model types: Vintage Analysis (especially for CECL)
Regression and Neural Networks
Market Risk
• Expected Short-Fall (Conditional VAR)
Operational Risk
• Loss Distribution Approach (LDA)
• Fast Fourier Transforms (FFT)
• Extreme Value Theory
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Asset Liability Management
Balance Sheet Forecasting Crisis and Contingent Planning
Asset allocation, manage interest risks and liquidity risks to
prevent mismatches between the cash flows of the assets Stress-testing and scenario generation
and the cash flows of the liabilities. A stress test is an analysis of the balance sheet of a bank
Liquidity Risk Management after crashes or economic crisis. Several scenarios are
• Duration and convexity analyses simulated to see how robust the bank is.
Stress-testing forms part of ICAAP.
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Machine Learning in Banking and Finance
Machine learning in banking and finance is now considered a key aspect of financial several services and applications.
Machine learning is a subset of data science that provides computers the ability to learn and improve from experience without being explicitly
programmed.
Machine Learning Algorithms can be broadly classified into three types i.e. supervised learning algorithms, unsupervised learning algorithms and
reinforcement learning algorithm as shown below
Supervised learning is mainly used in financial systems and
model developments as it highly promotes clarity of data, feature
engineering and facilitates ease of training.
However, one of the significant challenges that the industry face in the implementation of machine learning algorithms is the absence of good
quality data.
Machine Learning use cases in the banking sector
Financial Investment Automation of Processes Transactions security
Monitoring Predictions
Client Data Manageme
Risk Management & Treasury Management Financial Advisory
Analytics
Decision Making Service Improvements Client Retention Marketing
Machine Learning application in Zimbabwe
The Reserve Bank of Zimbabwe in 2019 started to investigate the implementation of various machine learning tools to predict
bank distress (RBZ June 2019, Financial Stability Report).
Support Vector Machines (SVM), Decision Trees,
Classification and Regression Trees (CART), Linear
Discriminant Analysis (LDA), Random Forest and K-NN
algorithms were considered and all features came from
both institutional and macroeconomic variables.
The study shows that the Support Vector Machine and
Random Forest appear to be more efficient algorithms to
predict financial distress in Zimbabwe for the given data,
in comparison to other algorithms.
The bank noted that future work will focus on identifying
more features with higher predictive capabilities in the
identification of banks that are likely to become
distressed.
Boruta algorithm clustering banks based on non-performing
loans (RBZ, 2019)
COSO 2017: Components (5) & Principles (20)
Private & Confidential – not for distribution 23
Enterprise wide Risk Management (ERM)
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Enterprise wide Risk Management (ERM)
Strategic
Business
Solvency
Credit
Concentration
Liquidity
Operational
IRR
Price Risk
FX
Compliance
Legal
IT
Cybersecurity
Financial Crime
Regulatory
Reputational Risk
Macro-Economic
Conduct Risk
AML/CFT
Stakeholder
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Actuaries in Banking Industry Challenges
Limited availability of fundamental
roles in emerging markets.
Lending may require more qualitative
insights such that analytics may just be
complementary to decision making.
Actuaries need knowledge of financial
statements since they may need to
augment balance sheets.
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References
1. Quantitative Research, Michael Page City Publication: Matthew Jones, Tom Mardon, Andrew Cook
2. BCBS (2019). The Basel Framework https://www.bis.org/basel_framework/index.htm?export=pdf
3. Howard Davies (2010). Global Financial Regulation after the Credit Crisis. London School of Economics and Political Science.
https://onlinelibrary.wiley.com/doi/full/10.1111/j.1758-5899.2010.00025.x
4. RBZ (2011). Guideline No: 1-2011/BSD Technical Guidance on the Implementation of the Revised Capital Adequacy Framework in Zimbabwe. BANK LICENSING,
SUPERVISION & SURVEILLANCE. https://www.rbz.co.zw/documents/BLSS/guide_circ_not/technical-guidance-on-basel-ii.pdf.
5. Financial Stability Institute: Various Papers. https://www.bis.org/fsi/index.htm.
6. Cannata. F (2011). Basel III and Beyond: A Guide to Banking Regulation after the Crisis, Risk Books. 2011.
7. Martin Neisen and Stefan Roth (2017) Basel IV: The Next Generation of Risk Weighted Assets.
8. Cooley.T.F (2011): Regulating Wall ST. The Dodd-Frank Act and the New Architecture of Global Finance. NYU STERN.
9. Chris Matten. (2000). Managing Bank Capital. Wiley.
10. Freixas. X. and Rochet. J. Microeconomics of Banking, MIT (2008).
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Thank you
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