OBJECTIVE OF THE SOLVENCY II COURSE
The course aims to educate participants on the requirements of the Solvency II Directive, the recent IFRS 17 regulation on insurance contracts, and risk appetite and stress testing methodologies in insurance companies. The course also explores the potential of utilizing generative artificial intelligence and quantum computing for modeling economic capital, reserves, premiums, and claims. Additionally, it covers the optimization of portfolios and asset and liability management in insurance companies using these advanced technologies.
Generative AI can provide significant benefits in the context of Solvency II by allowing insurers to generate synthetic data for various purposes, such as modeling and stress testing. This can help address challenges related to limited data availability and privacy concerns.
With generative AI, insurers can simulate different scenarios and generate large amounts of data to assess potential risks and evaluate the impact on solvency requirements. This can improve risk management and decisionmaking processes.
Quantum computing can potentially revolutionize the computational power available for Solvency II calculations. Quantum algorithms can solve complex optimization problems more efficiently and accurately than classical computers, allowing insurers to perform complex calculations and simulations in a shorter time frame.
Quantum computing can also enhance risk modeling and analysis by enabling more precise and realistic simulations. This can lead to better estimation of reserves, capital requirements, and pricing models, ultimately improving the accuracy of solvency assessments.
The course provides instructions on how to assess and measure various risks associated with insurance companies, including market, operational, life and nonlife insurance subscription risks, catastrophes, credit and liquidity. It also covers the requirements of the risk selfassessment known as ORSA (“Own Risk and Solvency Assessment”).
The course focuses on life and nonlife insurance underwriting risk, valuation of life and nonlife insurance provisions, advanced claims modeling, and biometric risk.
Additionally, the course reviews and compares the directives of standard formulas and internal models to understand their advantages and disadvantages.
IFRS 17 is a standard that explains the accounting requirements for insurance contracts. Its impact on insurance companies, costs, and benefits, along with the main methodologies for valuing insurance contracts, are clearly outlined. The relationship between IFRS 17 and Solvency II is also explained.
Furthermore, internal market and credit risk models are provided, along with advanced asset and liability management techniques such as immunization, temporary interest rate structure, cash flow matching, and stochastic optimization of assets and liabilities.
Reduced form, structural, and portfolio approach models are explained to measure credit risk for insurance companies.
This comprehensive course explains InsuranceLinked Securities, such as longevity bonds, swaps, and weather derivatives, as part of risk management. The course imparts risk management knowledge and teaches techniques to create scenarios, measure risk appetite and tolerance, and conduct stress tests.
To ensure effective learning, the course includes practical exercises and real data analysis using Excel with VBA, as well as scripts in Python, R, and SAS. Participants will analyze real financial statements to measure the impact of scenarios and stress tests and the required IFRS sensitivities.
OBJECTIVE OF THE INTRODUCTION COURSE
The preparatory course aims to provide participants with prior knowledge to maximize the quality of teaching and ensure a consistent level of understanding before they begin the Solvency II, IFRS 17, and Stress Testing course. The course covers a total of seventeen modules from various disciplines including quantum computing, quantum mechanics, R and Python programming, statistics, probability, finance, machine learning, probabilistic machine learning, generative AI, introduction to financial risk, and actuarial science for nonactuaries. The preparatory course will help improve the understanding of the Solvency II course. During the preparatory course, participants will be required to complete an extraclass activity to enhance their learning.
WHO SHOULD ATTEND?
This program is aimed at risk professionals, actuaries, managers, analysts and consultants in the insuraThis program is designed for professionals in the insurance sector, including risk professionals, actuaries, managers, analysts, and consultants. It is recommended that participants have a good understanding of statistics and mathematics to better comprehend the topics covered. The program offers practical exercises in Python, R, and Excel to help participants gain handson experience and not just theoretical knowledge.nce sector. For a better understanding of the topics, it is recommended that the participant have knowledge of statistics and mathematics. The participant will know not only the theory but practical exercises in Python, R and Excel.
Schedules:

Europe: MonFri, CEST 1619 h

America: MonFri, CDT 1821 h

Asia: MonFri, IST 1821 h
Course SII Price: 7 000 EUR
Early Bird Price: 6 000 EUR
Ending May 3
Preperatory Course Price* : 3.000 EUR
* This course is optional
Level Course SII:
Advanced
Lever Preparatory Course: Intermedium
Course S II Duration: 40 h
Preperatory Course Duration: 24 h
Material:
Presentation in PDF, Exercises: Python, R, Excel y JupyterLab.
AGENDA
NextGeneration Insurance:
AI for Solvency II and IFRS 17
PREPARATORY COURSE
Module 1: Probability
Objective: Explain some elementary concepts of the mathematical theory of probability. It exposes which are the probability distributions used in financial and insurance risks and how to estimate parameters. The importance of probability in Solvency II is explained.

Introduction to probability

Combinatorial analysis

Conditional probability and independence

Random variables

Density and distribution functions

Expectation, variance, moments

Probability distributions

Frequency distributions, Poisson, Binomial, Negative Binomial

Loss distributions, lognormal, EVT, gyh, beta, gamma, weibull, etc.

Random Vectors

Distribution fitting and parameter estimation

Use of probability distributions in Solvency II

Exercise 1: Probability distribution fits in R
Module 2: Statistics
Objective: Inferential statistics consists of a set of techniques to obtain, with a certain degree of confidence, information from a population based on information from a sample. Statistics is essential for the construction of models and their validation.

Introduction

Variables and data types

Descriptive statistics

Inferential statistics

Random samples and statistics

Point estimate

Estimation by intervals

Hypothesis tests

Importance of statistics in Solvency II

Exercise 2: Descriptive statistics in Python of data from an insurance company

Exercise 3: Hypothesis testing in R
Module 3: Finance
Objective: To review the concepts of the value of money over time, financial mathematics, valuation of annuities, bonds, and valuation models Capital Asset Pricing Model and Arbitrage pricing theory. The models are essential for the valuation of assets and liabilities of an insurance company.

Value of money over time

Financial mathematics and annuities

Bond valuation

Duration and convexity

CAPM and APT model

Stochastic processes

Monte Carlo simulation

Exercise 4: Valuation of bonds in Excel

Exercise 5: Estimation of duration and convexity in Excel

Exercise 6: Estimation of the CAPM and APT in Excel

Exercise 7: Monte Carlo Simulation and Stochastic Processes in R
Module 4: Programming in Python
Objective: Explain what the Python programming language is and its functionalities. It explains what Jupyter is and how to install it. Expose basic notions of programming and the libraries that will be used to develop Solvency II models.

Introduction to Python

Environment and library installation

Jupyter

Import and export of data

basic programming

statistical tools

regression libraries

finance bookstores

Machine Learning Libraries

Quantum Libraries

Exercise 8: Programming in Python
Module 5: Programming in R
Objective: Explain what R and Rstudio are and how to install them. Explain basic notions of R programming and the libraries used to develop Solvency II models.

Introduction to R

Environment and library installation

R Studio

Import and export of data

Basic programming

Statistical tools

Regression libraries

Finance bookstores

Actuarial science bookstores

Exercise 9: programming in R
Module 6: Machine Learning
Objective: Automatic machine learning, in English machine learning, essential for systems to be intelligent, allows the development of predictions based on data and improves the projections of traditional models. The use of machine learning and deep learning algorithms is introduced. The benefits of machine learning in risk management for insurance companies are explained.

Introduction Machine Learning

Differences with statistics

Supervised and unsupervised models

decision trees

Support Vector Machine

Kmeans

Assembly Learning

Random Forest

neural networks

Introduction to ensemble models

Introduction to Deep Learning

Exercise 10: Estimation of the Support Vector Machine and Random Forest

Exercise 11: Deep learning algorithm creation
Module 7: Introduction to Financial Risks
Objective: Lay the theoretical foundations on the financial risks that impact insurance companies, explain the types of risks and the sources of such risks. Understand the probability and impact of events that trigger financial risk in insurance companies.

What is risk?

Financial risks in insurance companies

Probability and Impact

Sources of financial risks

Differences between financial and nonfinancial risks

Market risk

Interest rate risk

Liquidity risk

Credit risk

Operational risk
Module 8: Actuarial Sciences
Objective: Introduction of actuarial sciences for participants without actuarial training. A brief introduction to life and nonlife insurance is presented, as well as actuarial mathematics.

What do actuaries do?

Introduction to Life insurance

Introduction to NonLife insurance

Type of contracts

Introduction to Life Insurance Reserves

Introduction to NonLife Insurance Reserves

Margin Based Pricing

Introduction to actuarial mathematics

Introduction to Life Insurance

Introduction to NonLife Insurance

Exercise 12: Modeling the distribution of the severity and frequency of claims in Excel and R

Exercise 14: Simulation of the current values of an Annuity of a life annuity.
Module 9: Quantum computing and algorithms
Objective: Quantum computing applies quantum mechanical phenomena. On a small scale, physical matter exhibits properties of both particles and waves, and quantum computing takes advantage of this behavior using specialized hardware. The basic unit of information in quantum computing is the qubit, similar to the bit in traditional digital electronics. Unlike a classical bit, a qubit can exist in a superposition of its two "basic" states, meaning that it is in both states simultaneously.

Future of quantum computing in insurance

Is it necessary to know quantum mechanics?

QIS Hardware and Apps

quantum operations

Qubit representation

Measurement

Overlap

matrix multiplication

Qubit operations

Multiple Quantum Circuits

Entanglement

Deutsch Algorithm

Quantum Fourier transform and search algorithms

Hybrid quantumclassical algorithms

Quantum annealing, simulation and optimization of algorithms

Quantum machine learning algorithms

Exercise 15: Quantum operations
Module 10: Introduction to quantum mechanics

Quantum mechanical theory

wave function

Schrodinger's equation

statistical interpretation

Probability

Standardization

Impulse

The uncertainty principle

Mathematical Tools of Quantum Mechanics

Hilbert space and wave functions

The linear vector space

Hilbert's space

Dimension and bases of a Vector Space

Integrable square functions: wave functions

Dirac notation

operators

General definitions

hermitian adjunct

projection operators

commutator algebra

Uncertainty relationship between two operators

Operator Functions

Inverse and Unitary Operators

Eigenvalues and Eigenvectors of an operator

Infinitesimal and finite unit transformations

Matrices and Wave Mechanics

matrix mechanics

Wave Mechanics
Module 11: Introduction to quantum error correction

Error correction

From reversible classical error correction to simple quantum error correction

The quantum error correction criterion

The distance of a quantum error correction code

Content of the quantum error correction criterion and the quantum Hamming bound criterion

Digitization of quantum noise

Classic linear codes

Calderbank, Shor and Steane codes

Stabilizer Quantum Error Correction Codes
Module 12: Quantum Computing II

quantum programming

Solution Providers

IBM Quantum Qiskit

Amazon Braket

PennyLane

cirq

Quantum Development Kit (QDK)

Quantum clouds

Microsoft Quantum

Qiskit

Main Algorithms

Grover's algorithm

Deutsch–Jozsa algorithm

Fourier transform algorithm

Shor's algorithm

Quantum annealers

DWave implementation

Qiskit Implementation

Exercise 16: Grover, Fourier Transform and Shor algorithm simulation
Module 14: Quantum Machine Learning

Quantum Machine Learning

hybrid models

Quantum Principal Component Analysis

Q means vs. K means

Variational Quantum Classifiers

Variational quantum classifiers

Quantum Neural Network

Quantum Convolutional Neural Network

Quantum Long Short Memory LSTM


Quantum Support Vector Machine (QSVC)

Exercise 17: Quantum Support Vector Machine
Module 15: Quantum computing in insurance companies

Building Blocks of Payoff Valuation

Distribution Loading

Payoff Implementation

Calculation of the Expected Value


Amplitude Estimation

Amplitude Estimation based on Phase Estimation

Amplitude Estimation without Phase Estimation


Grover's Quantum Search Algorithm

Insurancerelated Payoffs

Overall Payoff


Insurancerelated Quantum Circuits

Whole life insurance

Dynamic Lapse


Quantum Hardware

simulator

royal hardware


Exercise 18: Insurancerelated Quantum Circuits
Module 16: Tensor Networks for Machine Learning

What are tensor networks?

Quantum Entanglement

Tensor networks in machine learning

Tensor networks in unsupervised models

Tensor networks in SVM

Tensor networks in NN

NN tensioning

Application of tensor networks in credit scoring models

Exercise 19: Neural Network using Tensor Networks
Module 17: Probabilistic Machine Learning

Probability

Gaussian models

Bayesian Statistics

Bayesian logistic regression

Kernel Family

Gaussian processes

Gaussian processes for regression


Hidden Markov Model

Markov chain Monte Carlo (MCMC)

Metropolis Hastings algorithm


Machine Learning Probabilistic Model

Bayesian Boosting

Bayesian Neural Networks

Exercise 20: Gaussian process for regression

Exercise 21: Bayesian neural networks
Module 18: Generative AI
Generative artificial intelligence is artificial intelligence capable of generating text, images, or other media, using generative models. Generative AI models learn the patterns and structure of their input training data and generate new data that has similar characteristics. Generative AI differs from other types of AI as it is about creating something new that is not modified or copied from its training data. Generative AI is a generalpurpose technology used for multiple purposes across many industries. There are many types of multimodal generative AI tasks such as text summarization that produce a shorter version of a piece of text while retaining the main ideas, creating source code from natural language code comments, reasoning through a problem to discover potential new solutions or latent details and assigning a category to a given piece of content such as a document, image, video, or audio clip among other applications.

Introducing generative AI

What is Generative AI?

Generative AI Models

Generative Pre trained Transformer (GPT)

Llama 2

PaLM2

DALLE


Text generation

Image generation

Music generation

Video generation

Generating text

Generating Code

Ability to solve logic problems

Generating Music

Enterprise Use Cases for Generative AI

Overview of Large Language Models (LLMs)

Transformer Architecture

Types of LLMs

OpenSource vs. Commercial LLMs

Key Concepts of LLMs


Prompts

Tokens

Embeddings

Model configuration

Prompt Engineering

Model adaptation

Emergent Behavior

Specifying multiple Dataframes to ChatGPT

Debugging ChatGPT’s code
Human errors 
Exercise 22: Embeddings for words, sentences, question answers

Exercise 23: Embedding Visualization

Exercise 24: First let's prepare the data for visualization

Exercise 25: PCA (Principal Component Analysis)

Exercise 26: Embeddings on Large Dataset

Exercise 27: Prompt engineering

Exercise 28: Advanced Prompting Techniques

Exercise 29: Large Language Models (LLMs)

Exercise 30: Retrieval Augmented Generation

Exercise 31: Traditional KMeans to LLM powered KMeans

Exercise 32: Cluster Visualization

Exercise 33: Semantic Search

Exercise 34: Tokens and Words

Exercise 35: Tokenization in Programming Languages
SOLVENCY II COURSE, IFRS 17 and STRESS TESTING
Module 1: Solvency II
Objective: Explain how Solvency II reflects the new risk management practices to define the necessary capital and manage financial and insurance risks. Solvency Capital Requirement SCR, Minimum Capital Requirement MCR and the three pillars of Solvency II are explained in detail.

The Solvency II Directive and EIOPA

General structure

Basel II and III experience

Implementation Schedule

Asset Valuation

Technical Provisions

Segmentation

Products


Liability Analysis: Best Estimate and Margin Risk

Own Resources: Tier 1, Tier 2 and Tier 3

MCREstimate and Calculation

SCRStandard Formula

Internal Model Directives

Pillar 1: Own resources for solvency

Solvency Capital Requirement (SCR)

Standard Approach

Internal Model

Technological aspects and implementation

Minimum Capital Requirement (MCR)


Pillar 2 Supervision Process and Own Risk and Solvency Assessment (ORSA)

ORSA definition and scope

The ORSA role


Pillar 3 Transparency Requirements

Financial Condition Report

Solvency II Directive


IFRS

Residual Margin

Risk Margin

Best Estimate

Module 2: Standard Formula Methodology
Objective: Explain the formulas of the standard approach for the estimation of the SCR and MCR. Understand the risks of insurance companies. Explain in detail the mathematical formulas and possible expected values.

Technical specifications for the preparatory phase part 1

Dependency Structure

Risk mitigation techniques

Market risk

Interest rate risk

Equity risk

real estate risk

Spread Risk

Concentration Risk

liquidity risk


Credit risk

Counterparty risk

LGD and PD calculation


Operational risk

Standard Formula


Underwriting risk: Nonlife

Reserve Risk

Premium Risk

catastrophic risk


Underwriting Risk: Life

Mortality Risk

Longevity Risk

Morbidity Risk

Disability Risk

Portfolio Fall Risk

Expense Risk

Revision risk


Technical Health Risk

Technical specifications for the preparatory phase part 2

Determination of the riskfree interest rate

Exercise 1: Estimation of SCR Mkt of interest rate and Mkt Spread in a bond portfolio and SCR Mkt of equities in a stock portfolio.
INTERNAL AND ORSA MODELS
Module 3: Approval of Internal Models
Objective: Define what internal models are and explain the guidelines on the use of internal models that insurance companies must take into account so that the supervisory authorities approve and continue to allow the use of internal models to calculate solvency capital.

Preapplication

Application

Evaluation and right to withdraw the application

Decision on the application: Terms and conditions

Monitoring
Module 4: Own Risk and Solvency Assessment (ORSA)
Objective: ORSA is the acronym for Own Risk and Solvency Assessment and explains the set of processes used to assess risks according to capital needs. The management framework, the ORSA process and the ORSA reports are explained in detail.

ORSA Scope

Regulatory context:

management framework

ORSA process

ORSA report

Government system

Entity risk

Stress test and scenario analysis

Capital requirements and solvency assessment

Business plan and capital planning
Term Structure of Interest Rates
Module 5: Modeling of interest rate term structure (ETTI)
Objective: The relevance of adequately modeling the term structure of the interest rate or yield curve is crucial for the adequate valuation of the liabilities of the insurance company. It explains how to build the curve, the role of stochastic models and extrapolation methodologies among many other topics.

Yield Curve Concept

nelson siegel

Yield curve smoothing and term structure models

Interpolation Methods: Cubic Splines

Extrapolation Methods: WilsonSmith

stochastic modeling

Cox–Ingersoll–Ross model

Heath–Jarrow–Morton model


Selecting objective variables

Principal component analysis

Selection of scenarios

Vacicek's model

Vacicek interest rate model

Libor Market Model

Interest rate curve in EIOPA

basic curve

Last Liquid point

volatility adjustment

Flow matching adjustment

Implementation of extrapolation

Exercise 2: Principal Components Exercise in python
Exercise 3: Estimating Nelson Siegel parameters in python
Exercise 4: Interpolation in Excel
Exercise 5: CIR simulation calculator and Vasicek python
Exercise 6: Caplet and Swaption using Libor Market Model in Excel and VBA
Exercise 7: WilsonSmith extrapolation method in Excel and R
IFRS 17
Module 6: International Financial Reporting Standard IFRS 17 and IFRS 4
Objective: The IFRS 17 standard profoundly changes the approach to accounting for insurance, moving from a traditional scheme, based on historical values, to an approach closer to the "economic value" of the contracts. The methodologies and measurements of the insurance contract are explained.

IFRS 4 valuation of insurance contract liability

Objective and scope

Current exit value

Projection of estimated future flows

Liabilityadjusted market rate

risk margin

Current value approach

IFRS 17: Insurance Contract

Objective and scope

Typology of insurance contracts

Disaggregation and classification of IFRS 9 contracts

Differences with IFRS 4

Implementation dates

IFRS 17 enhancements to current accounting practices

Implementation costs

Information on profitability

Estimation of the present value of future cash flows

risk adjustment

Contractual service margin

Difference in income statement with IFRS 17


Methodologies and measurement of insurance contracts

Building Block Approach (BBA)

Variable Fee Approach (VFA)

Premium Allocation Approach (PAA)


IFRS 17 and Solvency II

Exercise 8: Impact of economic scenarios on the balance sheet, income statement and future cash flows by valuation of life insurance contracts, under the IFRS 17 approach, including risk adjustment and contractual service margin in Excel and R.
VALUATION OF PROVISIONS
Module 7: Valuation of Life Insurance provisions
Objective: The Guidelines on the valuation of Life insurance technical provisions are shown to increase the coherence and convergence of the professional practice of all types and sizes of insurance companies. Fermac Risk shows the European experience of this practice.

Deterministic Life Insurance Models

Deterministic portfolio valuation

Stochastic portfolio valuation

Technical Life Risk

Protection against technical risk of life with options

Contracts with PB

Contracts without PB

unit link

Variable Annuities

Reinsurance

Dynamic Fall Model (Lapse rate)

Rescue Options

Profit Sharing Option

Exercise 9: Life Insurance Portfolio Valuation Tool, includes:

Vasicek interest rate simulation

Stochastic Mortality Risk Simulation

Lapse rate modeling

Options using black sholes model.


Exercise 10: Variable Annuities using Black Sholes model

Exercise 11: Generative AI in Valuation of Life Insurance provisions
Module 8: Valuation of NonLife Insurance provisions
Objective: The Guidelines on the valuation of NonLife insurance technical provisions are shown to increase the coherence and convergence of the professional practice of all types and sizes of insurance companies. Some traditional and modern techniques for calculating the reserve are explained.

The technical provision for benefits. Regulations in Solvency 2

Aggregate Claims Modeling

Frequency Distributions

Distributions of the claim amount

Analytical methods

Monte Carlo Simulation

Trianglebased methods for calculating Loss Reserving Provisions

Grossing up

link ratio

Chain Ladder

Bornhuetter Ferguson


Stochastic methods for calculating the Provision for benefits.

Mack's method

Bootstapp Method


Machine Learning in NonLife Insurance

Claims reserving based on Bayesian neural networks

Chain Ladder Neural Network


Exercise 12: Fit frequency using negative binomial and Poisson

Exercise 14: Claims amount adjustment using lognormal, gamma, weibull, exponential and GH in python and R

Exercise 15: Estimation of accident rate distribution with Monte Carlo simulation in R

Exercise 16: ChainLadder Neural Network

Exercise 17: Estimating provisions using the Run Off Chain Ladder

Exercise 18: Estimating provisions using Bootstrap in R

Exercise 19: Generative AI in Valuation of NonLife Insurance provisions
MARKET RISK
Module 9: Value at Risk (VaR) and Expected Shorfall in life and nonlife lines
Objective: Explain the concept of the Value at Risk VaR and the Expected Shortfall in the life and nonlife branches. The treatment of returns and volatility using GARCH models is explained.

Introduction to VAR

VAR in life insurance

VAR in the nonlife business

Volatility Estimation

GARCH(1,1)

GARCH Multivariate

EWMA


Volatility Forecasting

Parametric Models

Normal VaR

tstudent distribution

lognormal distribution


Linear Model for Stocks and Bonds

Quadratic model for options

VaR extensions

Expected Shortfall or Tail VaR

Conditional VaR


Cash flow mapping

Exercise 20: Simulation and forecasting volatility using GARCH(1,1) and multivariate model in R

Exercise 21: estimation of the internal model of VaR and Expected Shortfall in life and nonlife insurance
Module 10: Parametric VaR with Extreme Value Theory
Objective: Explain the theory of extreme value to apply it to internal models. This distribution allows estimating the probability of truly extreme events. The pros and cons of these distributions in insurance practice are explained.

EVT Extreme Value Distributions

gumbel

Frenchet

Weibull


Generalized Pareto distributions

Exponential

Pareto

Beta


Threshold estimate

Model Selection

Hill and Mean Excess Plot


Generation of random EVT values

EVT estimation under Bayesian approach

Disadvantages of EVT

Exercise 22: Estimation of Graphs: Mean Excess, QQ and Hill plot in R

Exercise 23: Maximum likelihood parameter estimation of GDP in SAS and R

Exercise 24: VaR estimation by EVT in R

Exercise 25: Quantum VaR estimation
Module 11: Historical Simulation and Monte Carlo
Objective: VaR is explained by Monte Carlo simulation applied to insurance companies. Being the best methodology to estimate the VaR in a time horizon of one year.

VaR Historical Simulation

Adjust for volatility


VaR Monte Carlo simulation

Simulation with a risk factor

Simulation with multiple risk factors

Variance Reduction Methods


Normal Multivariate Distribution and TStudent

VaR Monte Carlo based on Gaussian copula and tstudent copula

Exercise 23: VaR estimation: using Monte Carlo Simulation and Historical Simulation in Excel and R

Exercise 24: Historical Simulation Backtesting

Exercise 25: VaR using Gaussian copula and tStudent in SAS and R
Module 11: Market Risk
Objective: Good market risk practices for insurance companies are explained. The results of the SCR under the standard formula are compared against the internal models.

Standard Formula on Market Risk

Submodules in market risk

Interest rate risk

Equity risk

real estate risk

Spread Risk

Concentration Risk

liquidity risk


SCR VaR 99.5%

Internal and partial models

Internal Market Risk Model

VaR for interest rate risk

Stochastic Process Selection

VaR using stochastic process of an asset

Simulation with principal components

Scenario Simulation


VaR for interest rate risk with principal components

Spread VaR

exchange rate VaR

Equity VaR

Concentration and correlation risk modeling

VaR of Options

Delta Normal VaR

Delta Gamma VaR

Monte Carlo simulation


Boundary Structure

Exercise 26: Estimating the VaR of options with Monte Carlo simulation in Excel and R

Exercise 27: Cash Flow mapping and VaR estimation of a bond portfolio

Exercise 28: VaR estimation of nonlife and life risk
Module 12: Stress Testing and Backtesting
Objective: Stress testing is one of the best tools for managing market risk. Consider exceptional but plausible events. Insurance companies will also need to validate internal models with backtesting.

Stress Testing Approaches

Historical Stress Testing

Reverse Stress Test

Stress testing in correlation

Stress testing on volatility

Multivariate stress testing

Backtesting

Kupiec`s Test


Frequency Conditional Coverage

Analysis of losses in the tail of the distribution

Clean and dirty backtesting

Exercise 29: Stress testing on a correlation matrix in Excel

Exercise 30: Backtesting of VaR in Excel
CREDIT RISK
Module 15: Credit Risk Structural and Reduced Form Models
Objective: Structural credit risk models require financial information from the company and have proven to be efficient during the pandemic. These models help measure the credit risk of fixed income investments, particularly bonds.

Structural Models

Merton's model

KMV model


reduced form models

JarrowTurnbull Model

Duffie and Singleton Model

Neutral default probabilities

Conversion of default currents into discrete PDs

Adjustment of reduced form models to historical databases

Construction of default probability curves

Validation with Falkenstein and Boral Test

Jump to default

zero coupon bonds

voucher with coupons

convertible bonds

CDS Valuation


Exercise 31: Structural model in R and Excel

Exercise 32: Construction of default probability and hazard rate curves in Excel and SAS

Exercise 33: Bonus and CDS valuation in Excel with VB

Exercise 34: Internal model of market and credit risk

CIR simulation of fixed and variable income interest rates

JarrowTurnbullLando model for credit risk with transition matrices.

Comparison against standard formulas.

Module 14: Credit Risk Portfolio Models
Objective: It explains how to model the credit risk of investment portfolios of bonds, loans and credit derivatives. The credit risk of reinsurers is explained. The creditmetrics and Creditrisk+ approaches to economic capital estimation are shown.

Rating Models

PD and LGD estimation

default correlation

asset mapping

Economic Capital Models

creditmetrics

Credit risk +

Onefactor model


Reduced Form Models

Counterparty Risk

Reinsurance Counterparty Default Risk

Credit Risk in Reinsurance portfolio approach

Concentration Risk

Credit Risk in the Credit Insurance portfolio approach

Exercises 35: Economic capital with a unifactorial model using Monte Carlo Simulation in Excel and SAS.

Exercise 36: Economic capital: CreditRisk+ in SAS

Exercise 37: Economic capital: Creditmetrics in Excel

Exercise 38: Economic Capital of the bond portfolio
LIFE AND NONLIFE INSURANCE RISK
Module 15: NonLife Underwriting Risk
Objective: This risk is divided into three large blocks, the risk for premiums: it refers to future claims that may arise during and after the period for which the solvency calculation is made, that is, that the expenses plus the losses due claims are greater than the premiums received. The risk due to reserves due to two causes, the miscalculation of provisions and fluctuations in the actual number of claims around the midpoint. The third is catastrophic risk.

Analysis Standard Formulas

Underwriting risk: Nonlife

Reserve Risk

Premium Risk

catastrophic risk

Health underwriting risk

NonCatastrophic Risk

Internal models Premium Risk

Internal model Reserve Risk


catastrophe risk

Internal model using Monte Carlo Simulation

Internal model using Multiyear approach

VaR estimate 99.5%


Catastrophic Risk Modeling

Frequency and Severity

catastrophe science

Tsunamis

Hurricanes: Frequency, Regions

Hurricane Modeling

Earthquakes, frequency and severity


Quantum computing to estimate insurance capital

Introduction

Fundamentals and Notations of Quantum Mechanics

Classical surplus process with quantum mechanics

Quantum algorithm to predict the insurance capital

premium gate

Claim Gate

The expected reserve in an insurance company


Exercise 39: VaR for Premium risk in Excel and Python

Exercise 40: VaR for Reserve risk in R

Exercise 41: Internal nonlife underwriting risk model and comparison against standard formulas in SAS and R

Exercise 42: Quantum computing to estimate capital
Module 16: Life Underwriting Risk
Objective: This risk is divided into Biometric Risk (mortality, longevity, disability/illness), Portfolio Fall Risk, Expense Risk, Revision Risk, and Catastrophe Risk. A comparison of results between the SCR by standard formula and internal models is shown. Traditional mortality models and others with advanced machine learning techniques are explained.

Biometric Risk

Mortality Risk

Longevity Risk

Morbidity Risk


Portfolio drop risk

Expense risk

Revision risk

Catastrophic risk and pandemics

Actuarial models for pricing

Behavioral Risks

Dynamic Mortality Tables

Mortality models

Model Lee Carter

Singular Value Decomposition

Stochastic Mortality Model

Longevity Risk

improvement factors

longevity index


Mortality Risk Models using Machine Learning

Continuous Models:

CoxNet, Cox Tree, Cox XGBoost, Survival Tree, Random Survival Forest


Discrete Models:

Random Forest, LightGBM, XGBoost Logistic, GAM CatBoost


Analytical Survival Distributions

Internal Risk Model Life Insurance

Risk management

Gamification

Behavioral risk analysis

Insured Linked Securities


Exercise 43: Stochastic Mortality Shock Model in SAS

Exercise 44: Lee Carter, Makeham and Logit model in SAS

Exercise 45: Internal Model Life Insurance Risk

Monte Carlo simulation

Lee Carter model

Model and simulation of interest rate term structure

Copulas in Excel and R


Exercise 46: Discrete and Discrete Mortality Models: Cox XGBoost, Survival Tree, Random Survival Forest, and LightGBM
OPERATIONAL RISK
Module 17: Operational Risk
Objective: Explain both the advanced management of operational risk in insurance companies and an introduction to the measurement of this risk to obtain a distribution of losses.

Introduction Operational Risk

Loss Event Management

Risk Control Self Assessment

Scenario Based Assessment

Key Risk Indicators

Capital estimation LDA approach

Exercise 47: Estimation of Economic Capital of 5 business units, aggregated and individual, using the following Frequency and Severity distributions:

Frequency

Poisson

Negative Binomial


Severity

lognormal

burr

gamma

Weibull

Inverse Gaussian

GDP EVT

LogLogistic

GH 4 parameters

Mixture of Lognormals

lognormalEVT

Alpha Stable


PoissonGamma Bayesian approach

Lognormal partition and GDP

Scenarios with Expert criteria

Exercise 48: Selection of the best distribution using goodnessoffit tests in Excel

Exercise 49: Estimation of economic capital with truncated data

Exercise 50: Internal model using Monte Carlo Simulation with effect of deductible / insurance excess in R

Exercise 51: Internal model with Monte Carlo Simulation with frequency distribution with Gaussian copulas in R

Exercise 52: Internal model with Monte Carlo Simulation of aggregate osses of business units with t and frank copulas in R

Exercise 53: Comparison of internal models with Recursive Panjer, Fast Fourier Transformation and Monte Carlo Simulation in R and Excel
RISK MANAGEMENT AND MITIGATION
Module 18: Insured Linked Securities (ILS)
Purpose: ILS are defined, broadly, as financial instruments whose values are driven by insurance loss events. The instruments are linked to catastrophes, mortality and longevity. They help the transfer and mitigation of risk as well as the diversification of capital.

Definition InsuranceLinked Securities

derivatives market

Derivatives and bonds linked to Property and Casualty risk

Weather Derivatives

Catastrophe Bonds

Catastrophe Derivatives


Derivatives and bonds linked to longevity and mortality risk

Longevity Swaps

Longevity Bonds


Risk management in ILS portfolios

Exercise 54: Valuation Longevity Swap in Excel

Exercise 55: Valuation derived from Climate in Excel
VALIDATION OF INTERNAL MODELS
Module 21: Validation of Internal Models I
Objective: The validation process of internal models is explained, the most common techniques such as backtesting. The appropriate reporting to validate models is explained in general terms.

validation process

modeling process

modeling tools

Backtesting Analysis

Stress Testing

Results stability

Model limitation

reporting

Scoring model
Module 19: Validation of Internal Models II
Objective: The validation of detailed internal models for each type of risk is explained. Advanced SCR validation techniques calculated by internal models are explained.

Validation of Internal Models

Market risk

Credit risk

Operational risk

Underwriting risk: Nonlife

Underwriting Risk: Life


Validation of:

Model Design

Model Output

Processes, data and test of use


Kupiec`s Test for market risk

Loss aggregation validation

Testing distributions using Berkowitz test

loss distribution

Simulation of the critical chisquare value

Berkowitz test in subportfolios

power assessment

Scope and limits of the test

Model risk due to uncertainty

Exercise 56: implementation of the Berkowitz test in internal credit models

Exercise 57: Simulation of losses and model risk in nonlife underwriting risk
ASSET AND LIABILITY MANAGEMENT
Module 20: Quantum Portfolio Management

Portfolio diversification

Allocation of financial assets in insurance companies

Financial risk tolerance

Asset Portfolio Optimization

efficient frontier

Financial portfolio simulation

Financial portfolio simulation techniques

Portfolio Management using Reinforcement Learning

Portfolio Management using quantum algorithms

Exercise 58: Portfolio optimization using quantum algorithms
Module 21: Asset and Liability Management
Objective: The management of assets and liabilities is becoming more important for insurance companies every day due to the pandemic. Optimization models are explained, from the wellknown cash flow matching, to advanced models of stochastic programming of assets and liabilities. Liquidity risk is explained.

Tools to manage assets and liabilities

Duration Gap analysis

Interest rate risk

Liquidity risk

Cash Flow Testing

Immunization

Cash Flow Matching


Optimization of assets and liabilities

Dynamic Financial Analysis

Stochastic and dynamic scenario trees in assets and liabilities

dynamic programming

Stochastic dynamic programming

Maximization of the financial margin and economic value

Application of recent economic and financial theories

Conditioning factors of liquidity, capital and Solvency 2

Stress Testing Scenarios


International financial reporting regulations and Solvency II

Sensitivities in IFRS 4 Financial Statements

Life and NonLife Insurance Risk

Financial Risks


Exercise 59: Optimization of Cash Flow Matching in Excel with Solver

Exercise 60: Portfolio optimization using stochastic dynamic programming in SAS

Exercise 61: Impact on financial statements due to changes in insurance and financial risk sensitivities in Excel

Exercise 62: Using Generative AI in ALM
Module 22: Quantum ALM

ALM Quantum Approach

Quantitative Methods in ALM

Bailey and Redington approach

Operations Research Techniques

classic optimization

Quantum Computing in Asset–Liability Management

Quadratic Unconstrained Binary Optimization (QUBO)

Number of Qubits

Exercise 45: Optimization of assets and liabilities using quantum algorithms
STRESS TESTING
Module 23: Scenario Analysis
Objective: Explains how to build risk scenarios. Activity that is becoming more pressing by the day due to the pandemic and its serious implications for the economy.

Definition of the scenarios

Using the scenarios

scenario identification

Scenario typology

Scenariobased risk assessment

Scenario Analysis Process

Governance in the scenarios

Definition of risk appetite

Scenario evaluation

Economic Scenario Generator (ESG)

Exercise 63: Economic Scenario Generator in Excel
Module 24: Forecasting Models
Objective: In order to project scenarios for the future, it is necessary to have traditional tools such as VAR and ARIMA models and other more sophisticated and precise ones such as machine learning.

Data processing

NonStationary Series

DickeyFuller test

Cointegration Tests


Econometric Models

ARIMA models

VAR Autoregressive Vector Models

GARCH models


Machine Learning Models

Supported Vector Machine

LSTM Recurrent Neural Network

Bayesian Neural Network LSTM

Quantum LSTM


Review of assumptions of econometric models

stationary series

heteroscedasticity

Outliers

serial correlation

Collinearity detection


Exercise 64: Tests of nonstationary series and cointegration

Exercise 65: VAR models in R

Exercise 66: Forecasting Machine Learning SPV and NN in R

Exercise 67: Forecasting LSTM

Exercise 68: Forecasting Bayesian LSTM

Exercise 69: Forecasting with Transformers of Generative AI
Module 25: Stress Testing for Insurance companies
Objective: Stress testing consists of generating for each scenario shocks to parameters such as the mortality rate, share price, interest rates, etc., and measuring the impact they would have on capital. ORSA's role in this matter is explained. And a global stress testing exercise for an insurance company is shown.

Stress testing aligned with ORSA

Stress testing analysis 2011, 2014 and 2016 in EIOPA

Quantitative and qualitative aspects of stress testing

Stress testing scenarios

Interest rate risk

Low longterm interest rate

double hit


Credit Spread Risk

Nonlife insurance risk

Reinsurers Credit Risk

Catastrophe Risk

life insurance risk

Mortality Events

Longevity improvements


Liquidity risk

Impact on assets and liabilities

Impact on SCR and MCR

Correlations and copulas to model dependency

Stress Testing as a decisionmaking tool

Global Exercise 70: Stress Testing in SAS, R, Excel with VBA and Gephi, includes:

Risk Appetite and Business Plan

Forecasting of the Income Statement and Balance Sheet

capital planning

Application of Scenarios and External Shocks

Network analysis of main macroeconomic variables

Impact on assets and liabilities

Impact on SCR and MCR

Using Generative AI in stress testing