Risk Dynamics offers a wide range of topics for both quantitative and qualitative profiles in banking and insurance, including topics on modelling in Credit, Market and Operational Risks, as well as Insurance Risks, Risk Appetite and Model Risk Management.

1. Traineeship program
Summer 2017
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Updated: 11 August 2017
Risk Dynamics is part of McKinsey & Company

2. 2Risk Dynamics
Requirements and Process
Requirements
▪ Only applications submitted through Risk Dynamics website will be considered
– http://www.riskdynamics.eu/careers/traineeships
▪ Each applicant can select at most 3 topics
▪ Each applicant should submit a CV and a Cover Letter. The Cover Letter should clearly explain the
reasons for the topics selection
The selection process consists of the following rounds
▪ Round 1: Candidates are selected based on CVs & Cover Letters
– Make sure your CV & CL are outstanding
– Cover Letter should clearly explain why you selected the topics and why do you think you will be able
to perform well on them
– Only candidates with excellent level of written and oral English will be invited to the second round. CVs
& CLs in French will not be reviewed
▪ Round 2: Candidates selected in Round 1 will receive a task (on one of the relevant topics)
– You will have 5 days to complete the task
▪ Round 3: Candidates that succeeded to complete the task in Round 2 will be invited to the interview with
Risk Dynamics expert(s)
– During the interviews the candidate will be asked to present the selected topic(s) and his/her task

3. 3Risk Dynamics
List of Topics
Ref. Description Domain
1 Generic PD models – PD benchmark framework Credit Risk
2 Generic LGD models – LGD benchmark framework Credit Risk
3 Generic EC models – EC benchmark framework Credit Risk
4 Modelling Initial Margin Diversification Effects Market Risk
5 Liquidity risk models Market Risk
6 Economic Scenario Generator tool Market Risk
7 Data analysis for Financial Time Series Market Risk
8 Insurance risk validation: benchmark analysis (starting as from October 2017) Insurance Risk
9 Model Policies Toolkit Consolidation Model Risk
10 Model Governance: design a referential for MMGF Model Risk
11 Modelling Operational Risk in CCAR context Operational Risk
12 Modelling dependence in OpRisk: benchmark study Operational Risk
13 Expert Judgement validation methodology overview General topic
14 Use of machine learning in practice General topic

4. 4Risk Dynamics
Credit Risk
Generic PD Models – PD Benchmark Framework
Objectives
▪ The probability of default (PD) is a cornerstone metric in Credit Risk. Banks rely on PD models for a
variety of risk measurement calculations and risk management decisions. PD models vary greatly across
product types and methodological approaches. In this study we will leverage RD experience, RD data,
and simulations to study different PD models and build a generic framework that allows quick
methodological and performance benchmarking
What is in for you?
▪ Learn about default and other aspects of the loan lifecycle which is at the heart of the functioning of banks
▪ Learn about different techniques of modelling PD: Merton models, transition matrices, low-default portfolio
approaches, logistic regression
▪ Challenge your coding and analytical skills by building, optimizing, and running Monte Carlo simulations
▪ Gain deep insights into the workings of PD models and develop an understanding of their performance
drivers
▪ Benefit from 10+ years of RD experience through a library of reviewed models
Required Skills
▪ Analytical skills and mindset
▪ Advanced knowledge of statistics / econometrics
▪ Advanced Excel skills
▪ Advanced programming skills, especially in R, are a strong advantage
▪ Good verbal and presentation skills are an advantage

5. 5Risk Dynamics
Credit Risk
Generic LGD Models – LGD Benchmark Framework
Objectives
▪ Loss-given-default (LGD) is a cornerstone metric in Credit Risk. Banks rely on LGD models for a variety of
risk measurement calculations and risk management decisions. LGD models pose a challenge in
modelling since they are based on. In this study we will leverage RD experience, RD and vendor data,
and simulations to study different LGD models and build a generic framework that allows quick
methodological and performance benchmarking
What is in for you?
▪ Learn about default, the non-performing loan workout process, and other aspects of the loan lifecycle
which is at the heart of the functioning of banks
▪ Exercise your (big) data analysis skills by delving into a database in search for meaningful data and
dependence
▪ Challenge your coding and analytical skills by building, optimizing, and running Monte Carlo simulations
▪ Gain deep insights into the workings of LGD models & build an understanding of their performance drivers
▪ Benefit from 10+ years of RD experience through a library of reviewed models
Required Skills
▪ Analytical skills and mindset
▪ Advanced knowledge of statistics / econometrics
▪ Advanced Excel skills
▪ Proclivity towards data analysis / mining is a strong advantage
▪ Advanced programming skills, especially in R and SQL, are a strong advantage
▪ Good verbal and presentation skills are an advantage

6. 6Risk Dynamics
Credit Risk
Generic Economic Capital Models – EC Benchmark Framework
Objectives
▪ Banks rely on Economic Capital (EC) models for an estimate of the capital required to mitigate
unexpected losses specific to their portfolios. EC models are the most complex in Credit Risk and have
the widest conceptual coverage as they often operate on boundary of Credit and Market Risk. In this study
we will leverage RD experience, RD data, and existing RD EC tools to build a generic framework that
allows quick methodological and performance benchmarking of EC models
What is in for you?
▪ Learn about the loan lifecycle and portfolio management which is at the heart of the functioning of banks
▪ Gain exposure to the most advanced methods in Credit Risk
▪ Challenge your coding and analytical skills by building, optimizing, and running Monte Carlo simulations
▪ Gain deep insights into the workings of EC models & build an understanding of their performance drivers
▪ Benefit from 10+ years of RD experience through a library of reviewed models
Required Skills
▪ Analytical skills and mindset
▪ Advanced knowledge of statistics / econometrics
▪ Advanced Excel skills
▪ Proclivity towards data analysis / mining is a strong advantage
▪ Advanced programming skills, especially in R, are a strong advantage
▪ Good verbal and presentation skills are an advantage

7. 7Risk Dynamics
Market Risk
Modelling Initial Margin Diversification Effects
Objectives
▪ The objective of the traineeship is to develop in Risk Dynamics’s existing library a calculation engine for
Initial Margin (IM) model for a portfolio with the focus on the diversification effects following the regulatory
requirements
▪ For this, the candidate will have to code an IM model for a single product and for a portfolio, and be able
to implement and test different techniques of dependence modelling (e.g. correlation and copulas)
What is in for you?
▪ You will learn to apply complex statistical tools on real case examples
▪ You will get applied knowledge on financial instruments
▪ You will get applied knowledge on financial regulation
▪ You will understand the sources of market risk and how to measure it
▪ You will improve your coding skills
Required Skills
▪ Strong knowledge of statistics and time series. Knowledge on copulas modelling is a plus
▪ Strong coding skills in R a mandatory
▪ Basic knowledge on financial products and their pricing

8. 8Risk Dynamics
Market Risk
Liquidity risk models
Objectives
▪ The objective of this study is to first design the modelling of liquidity risk (modelling of needs, resources,
stress scenarios and KRI) and then build a prototype (inR) of liquidity risk model (liquidity risk monitoring
tool) within risk dynamics existing library.
What is in for you?
▪ Learn about financial market
▪ Lear about liquidity risk
▪ Learn how financial industries manage their day-to-day cash flows and how they mitigate the risk of cash-
flow shortage
▪ Learn how to design and build a prototype
Required Skills
▪ Basis on financial products and financial industries
▪ Interest for financial markets
▪ Good coding skills in R and excel (VBA)
▪ Autonomy is key but well balanced with team work

9. 9Risk Dynamics
Market Risk
Economic Scenario Generator tool
Objectives
▪ The objective of this study is to develop economic scenario generator (i.e. Monte Carlo, stochastic
finance, pricing models for financial instruments, etc.), on one selected market risk factor within Risk
Dynamic’s existing library (in R)
▪ The focus for this year would be on the development of scenarios for Equity, FX and credit spread
What is in for you?
▪ Learn about financial market
▪ Get applied knowledge on financial mathematic with real data
▪ Lear how to build a model
Required Skills
▪ Strong coding skills in R a mandatory
▪ Strong knowledge on financial mathematics (stochastic finance)
▪ Basis on financial products and their pricing
▪ Autonomy is key but well balanced with team work
▪ Accuracy is key here

10. 10Risk Dynamics
Market Risk
Data analysis for Financial Time Series
Objectives
▪ The objective of the traineeship is to define a strategic approach for data analysis when assessing the
financial time series (in the context of IM modelling)
▪ The candidate will have to develop the documentation and the relevant code (in R)
What is in for you?
▪ You will learn to apply complex statistical tools on real case examples
▪ You will get applied knowledge on financial instruments
▪ You will get applied knowledge on financial regulation
▪ You will understand the sources of market risk and how to measure it
▪ You will improve your coding skills
Required Skills
▪ Strong knowledge of statistics and time series analysis
▪ Strong coding skills in R a mandatory
▪ Writing and oral English

11. 11Risk Dynamics
Insurance Risks
Insurance Risk Validation: Benchmark Analysis
Objectives
▪ Collect benchmark data (emergence parameters, volatilities etc.)
▪ Develop an approach to translate benchmark data in usable information
▪ Develop a benchmarking framework and a simple benchmarking model
▪ Develop a presentation setting out approach and results
What is in for you?
▪ Flavor of insurance model validation (predominantly underwriting and reserving risk).
▪ Provide a perspective, through benchmarks, to assess/ compare internal modes; a desirable skill for any
future insurance model validation project/ job.
▪ Overall idea about capital models in the context of a general insurance company
Required Skills
▪ Analytical skills, strong oral, writing, presentation skills
▪ Advanced knowledge of Word, Excel, and PowerPoint
▪ Basic knowledge of general insurance
▪ Basic knowledge of solvency-II and capital model is strongly desired but not mandatory

12. 12Risk Dynamics
Model Risk
Model Policies Toolkit Consolidation
Objectives
▪ The existing Risk Dynamics Model Risk Management (MRM) Referential has a policies, guidelines and
process component which aim at gathering the best practice and standards in terms of structure and
guidelines along all stages of the model management lifecycle
What is in for you?
▪ During the traineeship you will learn about the best practices on model risk management and the
relevance of it for financial institutions. You will also learn about writing risk policies and procedures that is
a frequently requested skill
Required Skills
▪ Analytical and organizational skills to properly structure information and draft the appropriate templates
▪ Strong writing skills and advance knowledge of word and power point

13. 13Risk Dynamics
Model Risk
Model Governance: Design a Referential for MMGF
Objectives
▪ The existing Risk Dynamics Model Risk Management (MRM) Referential has a governance component
which aims defining the “best practice” the location of the MRM function within the organization, the
stakeholders involved in the model management lifecycle with the respective roles and responsibilities.
The scope is not only limited to a single model but for managing the whole portfolio of models of the bank
What is in for you?
▪ During the traineeship you will learn about the best practices on model risk management and the
relevance of it for financial institutions. You will also learn about defining governance structures for
financial institutions that is key for any risk management process
Required Skills
▪ Analytical and organizational skills to properly structure information
▪ Innovative and autonomous approach for research and proposal of new ideas
▪ Strong writing skills and advance knowledge of word and power point

14. 14Risk Dynamics
Operational Risk
Modelling Operational Risk in CCAR context
Objectives
▪ Investigate the regulatory (Fed) expectations on the CCAR for Operational Risk model
▪ Perform literature review and summarize market best practices on different methodologies
What is in for you?
▪ During this traineeship the trainee will have an opportunity to understand the current state of regulation for
Operational Risk capital requirements and the challenges that banks face
▪ This traineeship will allow the candidate to learn complex statistical techniques and tools and apply them
on the real case examples of Operational Risk modelling which will lead to a better understanding of the
nature and problematics of Operational Risk. Also, the project will allow the student to understand what is
the function of validation in relation to modelling and regulatory expectations.
Required Skills
▪ Good level of English. Good knowledge of Matlab and/or R
▪ Good understanding of Operational Risk Measurement and Management
Reference
▪ “Taking the Stress out of Operational Risk Stress Testing” McKinsey

15. 15Risk Dynamics
Operational Risk
Modelling Dependence in OpRisk: Benchmark Study
Objective
▪ Perform a qualitative and quantitative benchmark analysis of the various mathematical techniques used to
model dependence for Operational Risk (banks and insurance)
▪ Perform analysis on the risk factor driving dependencies
What is in for you?
▪ During this traineeship the trainee will have an opportunity to learn some advanced statistical modelling
techniques as well as to gain knowledge on the practical aspects of scenarios development in financial
institutions
▪ This traineeship will allow the candidate to develop a certain set of skills and modelling techniques that
are highly demanded in the industry from the quantitative profiles
Required skills
▪ Strong quantitative skills required: knowledge on probability theory and statistics; strong programming
skills (Matlab or R)
Reference
▪ “Correlations and Systemic Risk in Operational Losses of the U.S. Banking Industry”. Federal Reserve
Bank of Richmond
▪ “Good practice guide to setting inputs for operational risk models” Institute and Faculty of Actuaries
▪ Cope, E. & Antonini, G., 2008. Observed Correlations and Dependencies Among Operational Losses in
the ORX Consortium Database (in collaboration with the ORX Analytics Working Group),

16. 16Risk Dynamics
General Topic
Expert Judgement Validation Methodology Overview
Objectives
▪ Expert Judgement (EJ) is widely used in financial modelling, e.g. when calibrating model parameters.
Regulators are currently paying more and more attention to these modelling choices and require that the
EJs are rigorously validated. The existing Risk Dynamics EJ validation approach needs to be aligned with
the current regulatory requirements (both European and US) and adopted to different types of institutions
What is in for you?
▪ During the traineeship you will learn about regulatory requirements on EJ (and not only) and get to see
how real capital models are constructed. You will also learn about what is validation and how it is
performed
Required Skills
▪ Analytical skills, strong oral, writing, presentation skills
▪ Advanced knowledge of Word, Excel, and PowerPoint
Reference
▪ See Fed (SR 15-19) 18 Dec 2015: “Model overlays (including those based solely on expert or
management judgment) should be subject to validation or some other type of effective challenge. The
term “effective challenge” means critical review by objective, informed parties who have the proper
incentives, competence, and influence to challenge the model and its results”

17. 17Risk Dynamics
General Topic
Use of machine learning in practice
Objectives
▪ Machine learning is a very powerful statistical tool, providing a lot of incites when used
wisely
▪ Banks and insurance companies are currently exploring the opportunities of using this tool
for business decision, in products’ pricing and regulatory models
▪ This leads to a challenge for validators and regulators to review and approve such models
▪ Risk Dynamics is developing an approach to review and test the machine learning models to
be able to provide assurance around model accuracy, precision and robustness
What is in for you?
▪ You will learn to apply complex statistical tools on real case examples
▪ You will get applied knowledge of regulatory models for banks and/or insurance companies,
and explore the different ways of models testing
Required Skills
▪ Strong knowledge of statistics and basic knowledge of machine learning techniques
– Coursera certified courses: Neural Networks for Machine Learning, Practical Machine
Learning, etc.
▪ Basic knowledge on financial institutions and financial risks