IFRS 9: Credit Risk Modeling II
More Powerful Exercises, Advanced Models
for adverse macroeconomic scenarios
Intensive course on IFRS 9 credit risk methodologies applying traditional econometric and machine learning models as well as innovative probabilistic machine learning and quantum computing models.
The COVID-19 pandemic emerged just two years after the 2018 implementation of IFRS 9. The pandemic stressed and affected the predictive power of the models and methodologies, posing significant challenges for creating provisions for impaired assets. In the wake of the pandemic shock, subsequent regulatory and government actions, as well as the recent unprecedented set of risk events such as war, European energy supply insecurity and global inflationary pressures, banks have gradually planned recalibrate the IFRS 9 expected credit loss (ECL) models to improve their accuracy and incorporate lessons learned. However, although adjustments to the models are necessary, new macroeconomic shocks continue to appear, influenced by high uncertainty.
Entities have faced several challenges. The first was the significant increase in credit risk (SICR) that was based on inaccurate or incomplete information. Second, the probability of default (PD) was not sensitive enough to forward-looking and non-linear information. Third, banks applied overlays more frequently, but did not justify or quantify them.
Some prestigious consulting firms propose to automate more processes, develop challenging models of PDs and ECL expected credit losses.
Therefore, we have created a course with a greater number of lifetime PD estimation and calibration models, we have increased the artificial intelligence models and added models based on quantum algorithms that on the one hand can be challenging models of the traditional ones and that will help measure nonlinear relationships.
However, the core of the course is to explain in detail credit risk methodologies to estimate and calibrate the lifetime parameters of PD, LGD and EAD adjusted to the IFRS 9 standard using econometric models, Bayesian approach, traditional machine learning, quantum machine learning and quantum algorithms.
All models must quantify the uncertainty inherent in financial inferences and predictions to be useful in financial risk management and decision making. Model parameters and outputs can have a range of values with associated probabilities. Therefore, mathematically sound probabilistic models are needed that adapt to inaccuracies and that quantify uncertainties with logical consistency. Therefore, we have included probabilistic machine learning models, that is, machine learning algorithms together with probabilistic modeling and Bayesian decision theory. These algorithms offer modern and powerful solutions in today's complex financial and economic environment.
This course includes more than 12 methodologies and exercises to estimate PD Lifetime in retail, mortgage, SME and corporate portfolios, for example, the Exogenous Maturity Vintage EMV model, Markov models, survival models, transition matrices, Deep Learning, Monte Carlo simulation quantum algorithms among others.
Forecasting and stress testing methodologies have been incorporated to generate forward looking economic scenarios. Regarding the subject, there are several modules dedicated to the design of scenarios where the interaction between the macroeconomic variables and the Lifetime PD are exposed. In addition, stress testing methodologies for IFRS 9 credit risk provisions are explained.
Regarding the LGD Lifetime, machine learning models are shown to improve the accuracy of the parameters. And regarding the EAD Lifetime, vintage models for lines of credit are explained, as well as econometric models for prepayment.
A pricing tool is delivered, which includes the estimate of ECL 12m and ECL Lifetime, regulatory capital, Raroc and Hurdle rate.
Quantum Machine Learning is the integration of quantum algorithms within Machine Learning programs. Machine learning algorithms are used to compute vast amounts of data, quantum machine learning uses qubits and quantum operations or specialized quantum systems to improve the speed of computation and data storage performed by algorithms in a program. For example, some mathematical and numerical techniques from quantum physics are applicable to classical deep learning. A quantum neural network has computational capabilities to decrease the number of steps, the qubits used, and the computation time.
The objective of the course is to show the use of quantum computing and tensor networks to improve the calculation of machine learning algorithms.
We show how quantum algorithms speed up the calculation of Monte Carlo simulation, the most powerful tool for developing credit risk models, representing an important advantage for calculating economic capital, lifetime PD and creating stress testing scenarios.
The objective of the course is to expose classical models against quantum models, explain the scope, benefits and opportunities.
To facilitate learning, most macros are delivered in Jupyter Notebook, an interactive web environment for running R and Python code, which includes videos, images, formulas, etc. that help the analysis and explanation of the methodologies.
WHO SHOULD ATTEND?
This program is aimed at risk managers, analysts and consultants who are immersed in the development, validation or audit of IFRS 9 credit risk models or for all those interested. For a better understanding of the topics, it is recommended that the participant have knowledge of statistics.
Price: 8.900 €
Europe: Mon-Fri, CEST 16-20 h
America: Mon-Fri, CDT 18-21 h
Asia: Mon-Fri, IST 18-21 h
Duration: 40 h
Presentations in PDF
Exercises in Excel, R, Python y Jupyterlab
The recorded video of the 40-hour course is delivered.
IFRS 9: Credit Risk Modeling II
Module 0: Quantum Computing and Algorithms (Optional)
Future of quantum computing in banking
Is it necessary to know quantum mechanics?
QIS Hardware and Apps
Multiple Quantum Circuits
Quantum Fourier transform and search algorithms
Hybrid quantum-classical algorithms
Quantum annealing, simulation and optimization of algorithms
Quantum machine learning algorithms
Exercise 1: Quantum operations