Credit Scoring, Artificial Intelligence and Quantum Machine Learning
COURSE OBJECTIVE
Intensive course to develop credit scoring tools, calibrate the probability of default, PD, and validate models. Traditional, probabilistic and quantum machine learning methodologies are explained. It also explains how to automate the construction and calibration of the PD with the artificial intelligence itself.
The participant will learn to develop traditional and advanced credit scoring models in the credit admission and monitoring stage. In other words, the construction of credit and behavior scoring is explained using enormous volumes of information.
Regarding data analytics, a module is exposed on advanced data processing, explaining, among other topics, sampling, exploratory analysis, segmentation and detection of outliers.
The main techniques of machine learning, supervised, unsupervised and reinforcement learning, applied to the creation of credit scoring tools, are exposed.
Traditional methodologies such as logistic regression and other, innovative, machine learning methodologies are exposed, such as: decision trees, naive bayes, KKN, LASSO logistic regression, random forest, neural networks, Bayesian networks, Support Vector Machines, gradient boosting tree, etc .
The use of deep learning neural networks to develop powerful credit scoring models that banks can implement as challenging tools or useful tools in the admission and monitoring process is explained. Feed forward, convolutional, recurrent neural networks and antagonistic generative networks are exposed. A proprietary methodology, by Fermac Risk, is explained to control deep learning models and make them interpretable. This will avoid unacceptable black boxes.
Hyperparameters are parameters whose values control the learning process and determine the values of the model parameters that a learning algorithm ends up learning. The prefix hyper suggests that they are 'higher level' parameters that control the learning process and the model parameters that result from it.
Techniques for calculating hyperparameters are shown, such as grid search, random search, and Bayesian optimization.
More than 20 credit scoring models are delivered, with different methodologies in various programming languages such as: R, Python, Jupyterlab, Tensorflow and SAS. Credit scoring models for admission, followup, recovery, income and abandonment are delivered.
Advanced methodologies for calibrating the PD IRB risk parameter are taught. Calibration by adjustment to central tendency, the philosophy of the PD PIT and PD TTC rating, the calibration of machine learning models so that they produce probabilities of default are addressed. In addition, a module has been included to develop and calibrate the PD Lifetime of IFRS 9 using deep learning models.
Automated machine learning, also called automated ML or AutoML, is the process of automating the iterative tasks of machine learning model development. Allowing risk analysts to build machine learning models with high scalability, efficiency, and productivity, while maintaining model quality, they can help not only selfbuild models but validation of credit scoring models.
Probabilistic machine learning techniques are shown to build credit scoring models such as Bayesian neural networks among other models.
Automated machine learning methodologies using genetic algorithms among other advanced techniques are explained.
The best practices for validation of credit scoring models of financial institutions using artificial intelligence and the regulatory requirements in Europe to use this type of models are indicated.
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 for the calculation of machine learning algorithms.
We believe that quantum computing will begin to transform the financial services landscape in the coming years. Banks that adopt quantum algorithms will have competitive advantages, including the potential to outpace competitors to become undisputed market leaders.
WHO SHOULD ATTEND?
The Course is aimed at professionals from financial institutions interested in developing powerful credit scoring models and calibrating their output, as well as model managers in credit risk and data science departments.
For a better understanding of the topics it is necessary that the participant has knowledge of statistics and mathematics.

Europe: MonFri, CEST 1620 h

America: MonFri, CDT 1821 h

Asia: MonFri, IST 1821 h
Schedules:
Price: 8.900 €
Level: Advanced
Duration: 40 h
Material:

Presentations in PDF

Exercises in Excel, R , SAS, Python, Jupyterlab y Tensorflow
AGENDA
Credit Scoring, Artificial Intelligence and Quantum Machine Learning
Modular Agenda
CREDIT SCORING
Module 0: Quantum Computing and Algorithms (Optional)

Future of quantum computing in banking

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 1: Quantum operations
Module 1: Artificial Intelligence for Credit Scoring

Big Data Definition

Big Data in financial institutions and fintech

Big data in Bigtech

Data typology

structured

semistructured

Unstructured Data


Big data: Volume, Velocity, Variety, Veracity and Value

Big Data Size

Big data sources

transactional data

social media dating

Credit bureau data

Origin of data sources

The data of the website

Text Data

sensor data

RFID and NFC data

Data from telecom operators

Smart grid data


banking digitization

financial inclusion

Regulation in Europe, USA and Latin America

Artificial intelligence in banking

Artificial intelligence in the credit cycle
Module 2: AI in Credit Scoring

AI in Credit Scoring for Banking and Fintech

Offline and online credit scoring

Design and Construction of Credit Scoring Models

Advantages and disadvantages

Models to face new financial crises

Machine Learning to develop and validate credit scoring

Importance of the Bureau Score

Credit Scorecard Management

Default Probability Estimation PD
Module 3: Machine Learning

Definition of Machine Learning

Machine Learning Methodology

Data Storage

Abstraction

Generalization

Assessment


Supervised Learning

Unsupervised Learning

Reinforcement Learning

deep learning

Typology of Machine Learning algorithms

Steps to Implement an Algorithm

information collection

Exploratory Analysis

Model Training

Model Evaluation

Model improvements

Machine Learning in credit scoring models

Quantum Machine Learning
