Solvency II: Quantum Risk Modeling
OBJECTIVE OF THE SOLVENCY II COURSE
The objective of the course is to show the participant the requirements of the Solvency II Directive, the recent IFRS 17 regulation: insurance contracts, as well as risk appetite and stress testing methodologies in insurance companies.
The course proposes the use of artificial intelligence and quantum computing for the modeling of economic capital, reserves, premiums and claims. As well as for the optimization of portfolios and the management of assets and liabilities in insurance companies.
It explains how to measure market, operational, life and nonlife insurance subscription risks, catastrophes, credit and liquidity to which insurance companies are exposed. The requirements of the risk selfassessment known as ORSA (“Own Risk and Solvency Assessment”) are exposed.
The content of the course places special emphasis on the modules of life and nonlife insurance underwriting risk, valuation of life and nonlife insurance provisions, advanced modeling of claims and biometric risk.
The directives of the standard formulas and internal models are reviewed, and compared, to know the advantages and disadvantages of each one.
IFRS 17, is fully explained. The impact it will have on insurance companies, costs and benefits is shown, as well as the main methodologies for valuing insurance contracts. The relationship between IFRS 17 and Solvency II is also explained.
Internal market and credit risk models are delivered, as well as advanced asset and liability management exercises, among other techniques: 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.
For risk mitigation, InsuranceLinked Securities are explained, among others, longevity bonds, swaps, weather derivatives, etc.
This intensive course, in addition to providing the participant with risk management knowledge, does so with methodologies to create scenarios, measure risk appetite and tolerance, and develop stress tests.
To ensure the participant's learning, we complement the theory with practical exercises and real data, in Excel with VBA, and macros in both R and SAS.
Real financial statements are analyzed to measure the impact of the scenarios and stress tests, as well as the required IFRS sensitivities.
OBJECTIVE OF THE INTRODUCTION COURSE
The objective of the preparatory course is to offer the participants, before entering the Solvency II, IFRS 17 and Stress Testing course, prior knowledge that maximizes the quality of teaching and homogenizes the level of the participants.
There are a total of seventeen modules from various disciplines, among others,
Quantum computing, quantum mechanics, R and Python programming, statistics, probability, finance, machine learning, probabilistic machine learning, introduction to financial risk, and actuarial science for nonactuaries. The preparatory course will improve the understanding of the Solvency II course.
During the preparatory course, the participants will be required an extraclass activity to improve learning.
WHO SHOULD ATTEND?
This program is aimed at risk professionals, actuaries, managers, analysts and consultants in the insurance 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 1620 h

America: MonFri, CDT 1821 h

Asia: MonFri, IST 1821 h
Course SII Quantum Price: 8.000 EUR
Preperatory Course Price : 5.000 EUR
Level: Advanced
Course S II Duration: 40 h
Preperatory Course Duration: 24 h
Material:
Presentation in PDF, Exercises: Python, R, Excel y JupyterLab.
AGENDA
Solvency II: Quantum Risk Modeling
Modular Agenda
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