Navigating Financial Futures: Credit Scoring and Credit Risk Modeling Training Course

Introduction

In the intricate landscape of modern finance, the ability to accurately assess and manage credit risk is paramount for the stability and profitability of financial institutions. Credit decisions, whether for individual loans or large corporate portfolios, rely heavily on sophisticated analytical tools that can predict the likelihood of default and quantify potential losses. Traditional methods, while foundational, are increasingly augmented by advanced statistical and machine learning techniques to provide a more granular and dynamic understanding of borrower creditworthiness.

This intensive training course is meticulously designed to equip participants with a comprehensive and practical understanding of credit scoring and credit risk modeling. From mastering the fundamental concepts of probability of default (PD), loss given default (LGD), and exposure at default (EAD) to applying cutting-edge statistical and machine learning algorithms for scorecard development, validation, and stress testing, you will gain the expertise to build and implement robust credit risk frameworks. This empowers you to make data-driven lending decisions, optimize portfolio performance, and ensure compliance with evolving regulatory standards.

Target Audience

  • Credit risk analysts and managers in banks, financial institutions, and FinTech companies.
  • Loan officers and underwriters.
  • Data scientists and quantitative modelers in finance.
  • Risk management professionals involved in Basel and IFRS 9 compliance.
  • Portfolio managers and asset allocators.
  • Internal auditors and model validators.
  • Academics and graduate students (Master's and PhD) in finance, econometrics, or statistics.
  • Consultants specializing in financial risk management.

Duration: 10 days

Course Objectives

Upon completion of this training course, participants will be able to:

  • Understand the fundamental concepts of credit risk and its key components: PD, LGD, and EAD.
  • Grasp the principles and methodologies of application and behavioral credit scoring.
  • Analyze various statistical and machine learning techniques for building credit risk models.
  • Comprehend the process of validating and backtesting credit scoring and risk models.
  • Evaluate the impact of macroeconomic factors and conduct stress testing for credit portfolios.
  • Develop practical skills in using software tools (e.g., Python, R) for credit risk modeling.
  • Navigate regulatory requirements (e.g., Basel, IFRS 9) and their implications for model development.
  • Formulate robust credit risk management strategies and contribute to sound lending decisions.

Course Content

  1. Introduction to Credit Risk and the Credit Cycle
  • Defining credit risk: probability of default, loss given default, exposure at default
  • The credit risk management cycle: origination, monitoring, collection, recovery
  • Types of credit risk: retail, corporate, sovereign
  • Regulatory landscape: Basel Accords (I, II, III) and IFRS 9/CECL
  • Expected Loss (EL) vs. Unexpected Loss (UL) and regulatory vs. economic capital
  1. Data Foundations for Credit Risk Modeling
  • Data requirements for credit scoring and risk modeling
  • Sources of credit data: internal (application, behavioral), external (credit bureaus, alternative data)
  • Data quality, cleaning, and preprocessing techniques
  • Variable selection and transformation: WoE (Weight of Evidence), IV (Information Value)
  • Segmentation strategies for different portfolios (e.g., retail vs. corporate, secured vs. unsecured)
  1. Credit Scoring: Application and Behavioral Models
  • Application Scoring: assessing creditworthiness of new applicants
  • Behavioral Scoring: evaluating existing customer behavior for credit limit adjustments or cross-selling
  • Key concepts: scorecards, cutoff points, good-bad definitions
  • Expert judgment models vs. statistical models
  • The role of credit bureaus and credit ratings
  1. Statistical Models for Probability of Default (PD)
  • Logistic regression: theory, estimation, and interpretation of odds ratios
  • Linear Discriminant Analysis (LDA) and other classification techniques
  • Decision Trees and Ensemble Methods (Random Forests, Gradient Boosting)
  • Measuring predictive accuracy: Confusion Matrix, ROC curves, CAP curves, KS statistic
  • Model calibration and population stability index (PSI)
  1. Modeling Loss Given Default (LGD) and Exposure at Default (EAD)
  • Loss Given Default (LGD): definition, types of LGD (workout LGD, market LGD)
  • Factors influencing LGD: collateral, seniority, recovery rates
  • Modeling approaches for LGD: regression-based, two-stage models, beta regression
  • Exposure at Default (EAD): definition and credit conversion factors (CCF)
  • Modeling approaches for EAD: regression-based, simulation methods
  1. Model Validation, Backtesting, and Monitoring
  • Importance of model validation: assessing accuracy, stability, and robustness
  • Types of validation: in-sample vs. out-of-sample, hold-out samples, cross-validation
  • Backtesting PD models: binomial test, Vasicek test
  • Backtesting LGD and EAD models
  • Ongoing model monitoring: population stability, characteristic analysis, performance tracking
  1. Regulatory Frameworks and Compliance (Basel & IFRS 9)
  • Overview of Basel II/III Pillars: Minimum Capital Requirements, Supervisory Review, Market Discipline
  • Internal Ratings-Based (IRB) approach for PD, LGD, EAD
  • IFRS 9 and Expected Credit Loss (ECL): implications for provisioning
  • Differences between regulatory and economic capital
  • Stress testing requirements under Basel and IFRS 9
  1. Stress Testing and Scenario Analysis
  • Purpose of stress testing: assessing resilience to adverse economic conditions
  • Methodologies for stress testing: historical scenarios, hypothetical scenarios, sensitivity analysis
  • Macroeconomic factors and their impact on PD, LGD, EAD
  • Stress testing approaches: top-down, bottom-up, hybrid
  • Interpreting stress test results and implications for capital planning
  1. Portfolio Credit Risk Modeling
  • Aggregating individual credit risks to portfolio level
  • Modeling correlation and dependence between defaults (e.g., using factor models, copulas - briefly introduced)
  • Calculating portfolio credit loss distributions
  • Risk contributions and diversification benefits in credit portfolios
  • Managing concentrations and setting limits
  1. Advanced Topics and Emerging Trends
  • Machine Learning in credit risk: AI ethics, explainability (XAI), fairness in scoring
  • Alternative data for credit scoring: mobile data, social media, behavioral data
  • Credit risk in new lending models: P2P lending, digital lenders
  • Low Default Portfolios (LDPs): challenges and specific modeling approaches
  • Climate-related financial risks and their integration into credit risk models
  • Survival analysis for credit risk (time-to-default models)
  • Practical implementation of credit risk models in Python/R.

CERTIFICATION

  • Upon successful completion of this training, participants will be issued with Macskills Training and Development Institute Certificate

TRAINING VENUE

  • Training will be held at Macskills Training Centre. We also tailor make the training upon request at different locations across the world.

AIRPORT PICK UP AND ACCOMMODATION

  • Airport pick up and accommodation is arranged upon request

TERMS OF PAYMENT

Payment should be made to Macskills Development Institute bank account before the start of the training and receipts sent to info@macskillsdevelopment.com

For More Details call: +254-114-087-180

 

Navigating Financial Futures: Credit Scoring And Credit Risk Modeling Training Course in Kenya
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