Магистратура
2022/2023
Построение скоринговых моделей с использованием методов машинного обучения
Статус:
Курс по выбору (Бизнес-аналитика и системы больших данных)
Направление:
38.04.05. Бизнес-информатика
Кто читает:
Департамент бизнес-информатики
Где читается:
Высшая школа бизнеса
Когда читается:
1-й курс, 2, 3 модуль
Формат изучения:
без онлайн-курса
Охват аудитории:
для своего кампуса
Преподаватели:
Мунерман Илья Викторович
Прогр. обучения:
Бизнес-аналитика и системы больших данных
Язык:
английский
Кредиты:
6
Контактные часы:
48
Course Syllabus
Abstract
The main aim of this course is a species of different country data sets and technics for integration this data for common international data environment. It also considers the features of algorithms for use online data for the instant response of the model to changing environmental circumstances, taken into the consequences of a pandemic. The course is designed for listeners which known elementary economics, finances, IT and mathematics and may be able for economists, IT specialists, managers, include MBA and journalists.
Learning Objectives
- Ability to use modern methods and relevant information technologies to formulate and solve fintech problems
- Master data-driven services marketing skills
- Ability to estimate the performance of financial decisions based on modern models and computer programs
- Knowledge of the types of modern scoring systems, methods of their design and data sources
- Ability to use data mining and machine learning methods to solve applied problems
- Ability to design business models based on intelligent IT services
Expected Learning Outcomes
- Describe the main types of scoring ratings, etc. common features and differences between them
- Formulate and correctly interpret definitions of concepts, terms and categories used in the development of scoring models
- Formulate what problems scoring models solve
- Be able to quickly recognize problems and find the scoring model needed to solve it
- Demonstrate the ability to solve design and economic problems in professional activity
- Formulate the differences between due diligence, financial risk indices and comprehensive credit scoring
- Be able to form a scoring line for solving a specific task of the subject of economic activity
- Apply basic machine learning tools
- Solve problems of building machine learning models using modern software
- Apply main approaches to building scoring systems based on modern financial concepts, such as methods of residual income, claim burden, payment discipline indices, etc.
- Calculate corrections to scoring models depending on the purposes of their construction, calculate credit limits, adjust the probability of occurrence of various events
- Master the methods of Big Data analysis used to solve professional tasks at the micro, meso and macro levels, including at the level of the financial market
- Apply algorithms of recommendation systems and black box interpreters
- Formulate basic methods of scoring work in a large company or marketplace
- Be able to organize comprehensive counterparty scoring, including monitoring of the dealer network and potential customers, develop recommendation systems and calculate the functional value of complex assets
- Demonstrate the mastery of Fintech tools
Course Contents
- Contemporary financial analysis main challenges
- Big data, data mining, data science
- Main data types
- Correlation
- Data processing. Modeling
- Estimation and model testing
- Interpretable models and recommendation systems
- Applications
Assessment Elements
- Home assignmentsIndividual data processing tasks
- Case disscussionsWhen preparing for discussions in practical classes, it is necessary to use not only lecture material, educational literature, but also regulatory legal acts and materials of law enforcement practice. Theoretical material should be correlated with legal norms, since changes and additions may be made to them, which are not always reflected in the educational literature
- Kaggle contestsDuring the course, three closed championships are held in Kaggle, on the construction of scoring models of the value of real estate, the probability of default and another one in agreement with the level of students
- Final examTest based on course materials
Interim Assessment
- 2022/2023 3rd module0.25 * Home assignments + 0.25 * Kaggle contests + 0.3 * Final exam + 0.2 * Case disscussions
Bibliography
Recommended Core Bibliography
- Bernd Engelmann, & Ha Pham. (2020). Measuring the Performance of Bank Loans under Basel II/III and IFRS 9/CECL. Risks, 8(93), 93. https://doi.org/10.3390/risks8030093
- Brooks,Chris. (2019). Introductory Econometrics for Finance. Cambridge University Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.b.cup.cbooks.9781108422536
- Ian Goodfellow and Yoshua Bengio and Aaron Courville. Deep Learning, 2016. URL: http://www.deeplearningbook.org
- Siddiqi, N. (2017). Intelligent Credit Scoring : Building and Implementing Better Credit Risk Scorecards (Vol. 2nd edition). Hoboken, New Jersey: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1441143
Recommended Additional Bibliography
- Gareth James, Daniela Witten, Trevor Hastie, & Robert Tibshirani. (2013). An Introduction to Statistical Learning : With Applications in R. Springer.
- Greenwald, A., Nosek, B., & Banaji, M. (2016). Understanding and Using the Implicit Association Test: 1. An Improved Scoring Algorithm. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.4AB789A7
- Trevor Hastie, Robert Tibshirani , et al., The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd edition, 2017. Free from the publisher: https://web.stanford.edu/~hastie/ElemStatLearn/printings/ESLII_print12.pdf