Credit Risk, Artificial Intelligence and Quantum Algorithms
COURSE OBJECTIVE
Advanced and intensive course on credit risk modeling using artificial intelligence and quantum computing, among many other topics: credit scoring tools, modeling of PD, LGD and EAD parameters of the advanced IRB approach of Basel III, credit risk methodologies for IFRS 9 impairment models, stress testing models of credit risk and economic capital. The impact of COVID19 on credit risk models is explained.
Machine and deep learning are used to build powerful credit scoring and behavior scoring tools, as well as to estimate and calibrate risk parameters and stress testing.
A module on advanced data processing is exposed, explaining among other topics: sampling, exploratory analysis, outlier detection, advanced segmentation techniques, feature engineering and classification algorithms.
The course explains the recent final reforms of Basel III regarding the new standard approach and Advanced IRB, IFRS 9 related to credit risk and the new guidelines on estimation of PD and LGD and treatment of exposures in default of EBA.
Predictive machine learning models are shown such as: decision trees, neural networks, Bayesian networks, Support Vector Machine, ensemble model, etc. And in terms of neural networks, feed forward, recurrent RNN, convoluted CNN and adversarial Generative architectures are exposed. In addition, Probabilistic Machine Learning models such as Gaussian processes and Bayesian neural networks have been included.
Advanced methodologies are taught to estimate and calibrate risk parameters: PD, LGD and EAD. The Lifetime PD estimate used in the IFRS 9 impairment models is exposed.
Methodologies and practical exercises of Stress testing in credit risk using advanced techniques of machine learning and deep learning are shown. And a practical exercise with financial statements to understand the impact of stress testing on capital and profits.
We have a global exercise to estimate the expected loss at 12 months and ECL lifetime using advanced credit risk methodologies, including PD, LGD, EAD, prepayment and interest rate curve models.
The course shows economic capital methodologies in credit card, mortgage, SME and Corporate portfolios. As well as capital allocation methodologies.
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.
WHO SHOULD ATTEND?
The Course is aimed at professionals from financial institutions of credit risk and financial risks. For a better understanding of the topics, it is recommended that the participant have knowledge of statistics and credit risk.
Price: 6.900 €
Schedules:

Europe: MonFri, CEST 1619 h

America: MonFri, CDT 1821 h

Asia: MonFri, IST 1821 h
Level: Advanced
Duration: 40 h
Material:

Presentations PDF

Exercises in Excel, R, Python y Jupyterlab