Introduction to Machine Learning with Applications to Credit Risk

The use of statistical models to predict banking risk is a standardized practice in the financial sector. Among the most widely used tools are regression models, above all for predicting default and building credit scores. However, the exploitation, generation and availability of data over the last twenty years, together with the development of new quantitative models, have made it possible to use data to obtain better performance in risk prediction. Indeed, the evidence suggests that Machine Learning methods can achieve greater accuracy than traditional models, mainly through more flexible functional estimation. This finding is not trivial, since even small improvements in model performance can translate into significant increases in profitability.

Facilitator: José Antonio Pellerano

More than 15 years of experience as an Applied Econometrician

He has taken part in various consulting and research projects including collaborations with Nestlé Dominicana, the World Bank, the Inter-American Development Bank, the Quito Electric Company, the Dominican Internal Revenue Service, the Vice Presidency of the Republic, the Santo Domingo City Council, the Unified Beneficiary System, Analytica, among others. He has also taught at the undergraduate level in the United States and at the graduate level in the Dominican Republic.

He earned his PhD in Economics from Texas A&M University, a master’s in Economics from the Pontificia Universidad Católica de Chile and a bachelor’s in Economics from the Pontificia Universidad Católica Madre y Maestra. His most recent research and consulting work focuses on applying experimental and quasi-experimental methods with the main goal of promoting evidence-based decision-making in both the public and private sectors. He has also developed various Data Science initiatives to promote data training and use in the country in a broad sense.

Program Objective

The main objective of this course is for participants to be able to develop their first Machine Learning models applied to credit risk. Beyond that, it seeks to foster participants’ curiosity and empowerment so that they can adapt the knowledge acquired to their institution’s needs. The methodology is based on discussion and the application of code with practical examples (learning by doing). The course format is in person in order to encourage discussion among participants. The course will be taught in “R” and includes an introduction to this programming language, so no prior knowledge is required, although it is recommended

Content:

Module 1: Introduction to Machine Learning

  • Machine Learning, Artificial Intelligence and Data Science
  • Basic concepts of Machine Learning
  • Practical example

Module 2: Introduction to "R"

  • Data handling and description
  • Data visualization
  • Code with examples

Module 3: Statistical Modeling

  • Logistic model
  • Machine Learning Models
  • Important concepts:
  • a) Cross Validation
  • b) Tuning
  • c) Overfitting
  • Model performance metrics
  • Applications:
  • a) Credit origination model
  • b) Credit behavior model (Behavioral Scoring)

Format

Hybrid

Duration

7 Weeks (6 In-person and 1 Virtual)

Start date

May 6, 2026

Location

La Isla Building, Av. Tiradentes, at the corner of Presidente González Street, Ensanche Naco

Facilitator

José Antonio Pellerano

Capacity

15 – 20 Participants

Schedule

6:00 p.m. – 8:00 p.m.

Training Hours

14 Hours

Cost

USD$ 1,100

Member Cost

USD$ 1,000

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Club de Gestión de Riesgos de la República Dominicana

A non-profit association that promotes risk culture and best practices in the Dominican financial system.

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