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Banking Regulation and Supervision

Evidence — From Credit Scores to Systemic Risk

This course introduces the empirical methods and public data used in bank regulation and supervision.

20h (10 days)

€1,375-€795

Online

English

Next edition: March 8-19, 2027

Early bird deadline: January 20, 2027

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Banking Regulation and Supervision

Evidence — From Credit Scores to Systemic Risk

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Who is this Course for?

Banking Regulation and Supervision

Evidence — From Credit Scores to Systemic Risk

Course overview

Bank regulation and supervision are undergoing significant change as regulators implement new capital requirements and address emerging risks linked to bank failures, artificial intelligence, and climate change. Understanding how to interpret the empirical evidence behind these developments is increasingly important for professionals working in banking, supervision, and financial policy.

This course introduces the empirical methods and public data used in bank regulation and supervision. Each block focuses on a practical banking or supervisory question, pairing it with an empirical method and a public dataset from sources such as the European Banking Authority (EBA), the US Federal Deposit Insurance Corporation (FDIC), and the International Monetary Fund (IMF).

The emphasis is on interpretation rather than programming. Participants learn to understand what models do, assess identification strategies, recognize potential weaknesses, and interpret analytical results from a regulatory and supervisory perspective. Code is provided in Stata and Python through Google Colab, allowing participants to focus on the analysis and its implications.

The course also introduces the responsible use of AI-assisted coding with public data, emphasizing validation, transparency, and reproducibility. By combining current regulatory questions with empirical methods and real-world data, it provides participants with practical tools for engaging with the evidence behind modern bank regulation and supervision.

No coding background is required. Provided Stata and Python code allows participants to focus on understanding and interpreting models rather than writing them from scratch.

This course may be taken alone or together with the other courses in the program to give an in-depth view of the banking industry.

Faculty

Discover what makes this a unique learning opportunity

1

One question, one method, one real dataset: Every block pairs a regulatory question with an empirical method and a public dataset; participants reproduce themselves, not abstract theory, not a coding boot camp.

2

Supervisory judgment is the deliverable: The goal isn't to build the model, it's to read it the way a supervisor does: spotting weak identification, recognizing when a bank's risk picture isn't believable, knowing where a model misleads.

3

Taught on the regulatory frontier: Built around live issues, Basel finalization, the 2023 bank failures, macroprudential policy and current research, including the instructor's own 2026 work.

4

The real datasets supervisors use: Hands-on with EBA, FDIC Call Reports, and IMF databases, so skills transfer straight to the ECB/SSM, national supervisors, bank risk functions, and consultancies.

5

No coding required: Code is provided in Stata and Python; the emphasis is on understanding and validating models, which opens the course to strong analysts who don't program daily.

6

Interactive Online Format: Flexible schedule, ideal for professionals.

Who is this course for?

Professionals in central banks, supervisory authorities, and international financial institutions (ECB/SSM, national supervisors, IMF) who need to read, validate, and challenge the empirical models behind prudential decisions — not build them from scratch
Risk, regulatory, and analytics staff in commercial banks and consultancies who want to connect credit-risk and capital models to the supervisory logic that judges them
Master’s and PhD students and researchers in economics, finance, and public policy looking to ground their work in the current datasets and contemporary debates of banking regulation (Basel finalization, the 2023 turmoil, macroprudential policy)

Learning outcomes

By the end of this course, participants will be able to:

Judge when a piece of empirical evidence credibly identifies a causal effect in banking and when it doesn’t, using tools like difference-in-differences and the credit-register design
Build, evaluate, and interpret a credit-scoring (probability-of-default) model, and understand what it captures and what it misses
Trace how a credit-risk model feeds into regulatory capital, and read RWA density as a supervisory diagnostic in the IRB-versus-standardized debate
Assess whether capital and liquidity requirements actually change bank behaviour, and interpret the fragility indicators (interest-rate risk, unrealized losses) behind episodes like SVB
Use the public databases that supervisors rely on (EBA, FDIC, IMF) to measure systemic risk and evaluate macroprudential tools

Key topics for Evidence — From Credit Scores to Systemic Risk

Take a look at the themes covered in the next edition of this banking Executive Education course.

Each topic is accompanied by specific learning objectives and empirical applications.

Empirical Analysis in Banking

Learning objectives

Understand the main empirical challenges involved in studying banking and financial intermediation
Develop intuition for identification strategies commonly used to distinguish correlation from causal effects
Learn how to critically interpret empirical evidence in banking

Topics

Why empirical analysis in banking is difficult: selection, reverse causality, omitted variables, and endogeneity
The counterfactual and the logic of causal inference
Difference-in-differences and event-study approaches
Fixed-effects approaches and the distinction between credit supply and credit demand.
Overview of alternative identification strategies used in empirical banking
The role, advantages, and limitations of different types of banking data

Empirical Applications

Applications will illustrate how empirical methods are used to answer relevant questions in banking. Participants will work with banking and firm-level information to estimate empirical models, implement approaches such as difference-in-differences, and interpret coefficients, tables, and graphical results
Particular attention will be paid to the economic meaning of the estimates, the credibility of the identification strategy, and the conclusions that can be drawn from the analysis

Credit Risk and Credit Scoring

Learning objectives

Understand how empirical models are used to assess credit risk
Interpret the outputs and performance of credit-risk models
Understand the strengths and limitations of quantitative credit assessment

Topics

Credit-risk assessment and probability of default
Credit-scoring models and their interpretation
Model evaluation: discrimination, calibration, and classification
Hard and soft information in lending
Model limitations, information asymmetries, and relationship lending
Implications for lending decisions and supervision

Empirical Applications

Applications will illustrate how credit-risk models are estimated and evaluated using borrower- or firm-level information. Participants will work with probability-of-default models and standard measures of predictive performance
Particular attention will be paid to interpreting model outputs, understanding where models perform well or poorly, and relating the results to lending decisions and supervision

Credit Risk, Regulation, and Bank Capital

Learning objectives

Understand how measures of credit risk translate into regulatory capital requirements
Compare alternative regulatory approaches to measuring bank risk
Interpret empirical evidence on risk measurement and regulatory incentives

Topics

From credit risk to risk-weighted assets and regulatory capital
Internal-model and standardised approaches
The role of model-based regulation
Differences in measured risk across banks and portfolios
Incentives, model risk, and the supervisory interpretation of regulatory capital measures

Empirical Applications

Applications will connect credit-risk estimates to regulatory capital and risk-weighted assets. Participants will examine how alternative assumptions or risk estimates affect capital requirements and may compare risk measures across banks or portfolios
The focus will be on understanding the economic and regulatory interpretation of the results and the incentives created by model-based regulation

Bank Risk, Financial Stability, and Macroprudential Policy

Learning objectives

Understand the main sources of bank fragility and how they interact
Interpret empirical measures of capital, liquidity, market, and systemic risk
Understand the rationale and empirical assessment of macroprudential policies

Topics

Bank capital, risk-taking, and credit supply
Liquidity risk and bank funding
Interest-rate risk and balance-sheet vulnerabilities
Bank runs and episodes of banking distress
From individual bank risk to systemic risk
Empirical approaches to measuring financial fragility and systemic risk
Banking crises and contagion
Macroprudential regulation and its effects
Selected contemporary applications in financial stability

Empirical Applications

Applications will use bank-, market-, firm-, and macro-financial information to study bank fragility, systemic risk, and the effects of regulatory policies. Participants may estimate empirical models, compare banks or episodes of financial distress, and analyse the effects of policy changes
The objective is to connect quantitative evidence with questions of bank risk, financial stability, and macroprudential supervision

References

Take a look at the list of references which may help you prepare for this course.

The references are organized by topic.

Empirical Analysis in Banking

Khwaja, A. & Mian, A. (2008), “Tracing the Impact of Bank Liquidity Shocks,” American Economic Review, 98(4).
Jiménez, G., Martín-Oliver, A., Peydró, J.-L., Toldrà-Simats, A. & Vicente, S. (2026), “Do
Bank Branches Matter? Evidence from Mandatory Branch Closings,” working paper.
Laeven, L. & Valencia, F. (2020), “Systemic Banking Crises Database II,” IMF Economic Review.
Degryse, H., Kim, M. & Ongena, S. (2009), Microeconometrics of Banking: Methods, Applications, and Results, Oxford University Press.

Credit Risk and Credit Scoring

Altman, E. (1968), “Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy,” Journal of Finance, 23(4).
Jiménez, G., Martín-Oliver, A., Peydró, J.-L., Toldrà-Simats, A. & Vicente, S. (2026), “Do Bank Branches Matter? Evidence from Mandatory Branch Closings,” working paper.
Baesens, B., Rösch, D. & Scheule, H. (2016), Credit Risk Analytics: Measurement Techniques, Applications, and Examples in SAS, Wiley.

Credit Risk, Regulation, and Bank Capital

Behn, M., Haselmann, R. & Vig, V. (2022), “The Limits of Model-Based Regulation,” Journal of Finance, 77(3).

Plosser, M. & Santos, J. (2018), “Banks’ Incentives and Inconsistent Risk Models,” Review of Financial Studies, 31(6).

Van Gestel, T. & Baesens, B. (2009), Credit Risk Management: Basic Concepts, Oxford University Press.

Systemic Risk and Macroprudential Policy

Jiménez, G., Ongena, S., Peydró, J.-L. & Saurina, J. (2017), “Macroprudential Policy, Countercyclical Bank Capital Buffers, and Credit Supply,” Journal of Political Economy, 125(6).

Gropp, R., Mosk, T., Ongena, S. & Wix, C. (2019), “Banks’ Response to Higher Capital Requirements,” Review of Financial Studies, 32(1).

Jiang, E., Matvos, G., Piskorski, T. & Seru, A. (2024), “Monetary Tightening and U.S.
Bank Fragility in 2023,” Journal of Financial Economics.
Adrian, T. & Brunnermeier, M. (2016), “CoVaR,” American Economic Review, 106(7).
Cerutti, E., Claessens, S. & Laeven, L. (2017), “The Use and Effectiveness of Macroprudential Policies,” Journal of Financial Stability, 28.
Laeven, L. & Valencia, F. (2020), “Systemic Banking Crises Database II,” IMF Economic Review.

Freixas, X., Laeven, L. & Peydró, J.-L. (2015), Systemic Risk, Crises, and Macroprudential Regulation, MIT Press.

Data and Tools

The course combines empirical methods with applications using banking, firm-level, market, and macro-financial data
Participants will see how data can be organised and used to estimate empirical models, evaluate credit-risk models, implement approaches such as difference-in-differences, and construct informative tables and figures
Statistical software will be used to illustrate selected applications and to connect the economic questions discussed in class with the underlying empirical analysis
Participants will examine how estimates are obtained, how model outputs should be interpreted, and how the assumptions underlying an empirical strategy affect the conclusions that can be drawn
A variety of publicly available and commonly used data sources may be introduced throughout the course. Modern computational tools may also be used to support data analysis, validation, and reproducibility

Why should you attend BSE Executive Education courses?

All BSE Executive Education courses are taught to the same high standard as our Master’s programs.

1

Network with like-minded peers from around the world

2

Short courses allow you to learn without a big time commitment

3

Try something new and expand your knowledge and career prospects, or advance your thesis

Admissions

Considering taking part in BSE Banking Executive Education course? Check you meet the requirements below.

Next edition: March 8-19, 2027

Early bird deadline: January 20, 2027

Requirements

Candidates are assessed on an individual basis according to their professional or academic background

Prerequisites include

Basic familiarity with statistics and regression (what a coefficient and a p-value mean), at the level of a quantitative undergraduate or master’s course
A working interest in banking, finance, or regulation. Prior exposure to financial or prudential concepts is helpful but not required; key ideas are introduced as needed
The course is designed to be accessible to strong analysts and professionals who are comfortable reading quantitative output but do not program daily

Get up to speed with the latest developments in the Banking industry in a short time.

Course schedule

The times listed are Central European Time (CET). Compare with your time zone on time.is .

Instructors, topics, and schedules are subject to change.

Week 1

Time

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Tue

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Wed

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Thu

5
Fri

14:00-16:00

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Practical

Lecture

Practical

Lecture

Week 2

Time

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Tue

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Wed

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Thu

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Fri

14:00-16:00

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Certificate and Fees

Certificate

Participants who attend at least 80% of the course will receive a Certificate of Attendance free of charge. Participants will not be graded or assessed during the course.

Fees

A 10% discount applies when the confirmation payment is completed on or before the announced Early Bird deadline.

Multiple course discounts are available. Find out more information in our Fees and Discounts pdf.

Fees for courses in other Executive Education programs may vary.

Course

Evidence — From Credit Scores to Systemic Risk

Modality

Online

Total Hours

20

ECTS

0

Regular Fee

€1,375

Reduced Fee*

€795

* Reduced Fee applies for PhD or Master’s students, Alumni of BSE Master’s programs, and participants who are unemployed.

FAQ

Need more information? Check out our most frequently asked questions.

Are the sessions recorded?

Sessions will be recorded and videos will be available for a month once the course has finished.

How much does each Executive Education course cost?

Fees for each course may vary. Please consult each course page for accurate information.

Are there any discounts available?

Yes, BSE offers a variety of discounts on its Executive Education courses. See more information about available discounts or request a personalized discount quote by email .