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Credit Scoring, Artificial Intelligence and Quantum Machine Learning






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, follow-up, 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 self-build 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.



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: Mon-Fri, CEST 16-20 h


  • America: Mon-Fri, CDT 18-21 h

  • Asia: Mon-Fri, IST 18-21 h







Price: 8.900 €



Level: Advanced


Duration: 40 h




  • Presentations in PDF

  • Exercises in Excel, R , SAS, Python, Jupyterlab y Tensorflow


Credit Scoring, Artificial Intelligence and Quantum Machine Learning



Modular Agenda



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 quantum-classical 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

    • semi-structured

    • 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

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