Training and Education

Our modeling courses are meticulously crafted to provide students with unparalleled expertise and practical experience under the guidance of real-world risk management experts. These courses are designed to reflect the best practices and cutting-edge techniques employed by the banking industry today. Here's a detailed breakdown of our approach:

Course Features

Expert-Led Instruction
The course is curated, taught, and overseen by seasoned risk management specialists with extensive hands-on experience in commercial banking. These instructors bring invaluable industry insights and practical knowledge to the classroom, ensuring that students receive top-tier instruction.
Industry Best Practices
Our curriculum is meticulously crafted to align with the most current and effective models utilized within the banking sector. By focusing exclusively on industry best practices, students gain a comprehensive understanding of the methodologies and techniques that drive success in risk management.
Structured Learning Format
Each course is structured into two distinct components. The first part comprises in-depth lectures delivered by industry experts, covering theoretical concepts, case studies, and real-world examples. The second part involves practical application, where students engage in hands-on exercises to reinforce their understanding and skills.
Hands-On Modeling Exercises
Students participate in practical exercises designed to replicate real-world banking data environments. By working with datasets that closely resemble those found in actual banking operations, students gain valuable experience in model construction, validation, and interpretation. This hands-on approach enhances their analytical capabilities and prepares them for the complexities of real-world risk management scenarios.

Course Design

Tailored Curriculum
Our courses are uniquely designed to cover a comprehensive range of topics relevant to risk management in the banking sector. From credit risk modeling to market risk analysis, each module is carefully crafted to provide students with a deep understanding of industry-standard methodologies and techniques.
Expert Instruction
Our courses are taught by seasoned practitioners with extensive experience in risk management within the banking industry. These instructors bring a wealth of knowledge and real-world insights to the classroom, enriching the learning experience with practical examples, case studies, and industry anecdotes
Hands-on Learning
A key feature of our courses is the emphasis on hands-on learning through modeling exercises. In part one of each course, students attend lectures delivered by practitioners, where they gain theoretical knowledge and insights into industry best practices. In part two, students engage in modeling exercises using specially curated datasets that mirror real-world banking environments. This hands-on approach allows students to apply their theoretical knowledge in a practical setting, honing their analytical skills and decision-making abilities.
Realistic Datasets
The datasets used in our modeling exercises are specifically designed to reflect the complexities and nuances of banking data environments. These datasets incorporate a diverse range of financial metrics, market variables, and risk factors, allowing students to explore and analyze real-world scenarios. By working with realistic datasets, students gain valuable experience in data manipulation, model building, and performance evaluation, preparing them for the challenges they will encounter in their future careers.

In-Dept Training

Best-Practice Risk Management Models in Commercial Banks

Overview of Risk Management Models in Commercial Banks
Market based PIT PD, TTC and TiC Credit Rating
Credit rating/Score card
PD/LGD/EAD/EL loss model (CCR: counterparty)
PD Term Structure & Loss Model
Transition Matrix & Loss Model/decomposition of transition matrix
Correlations: Asset correlation, default, PD & LGD, EL, StDev
Concentration & Diversity Score (UL) and (Regulatory) SF model/capital implied concentration model
CCAR:Commercial/wholesale Portfolio Loss Forecast: Conditional transition matrix approach
CCAR: Consumer/retail Portfolio Loss Forecast: Age-Period-Cohort (APC) approach
Expected Credit Losses
EC parameter model (PD/LGD/EAD/Correlation)
EC model simulation model
Copula model
Capital Allocation Model
Risk Management & Model Validation Process Workout
AML & Anti-fraud models - Threshold tuning

Course Information

This course offers a comprehensive exploration of risk management models in commercial banks, covering market-based credit rating, transition matrices, correlation analysis, and stress testing. Participants delve into critical topics such as expected credit losses, economic capital modeling, and copula theory. With a focus on practical applications, it equips professionals with the skills needed to navigate challenges in risk management effectively.

Overview of Risk Management Models in Commercial Banks

This course provides a comprehensive introduction to risk models in commercial banks, offering essential background knowledge and business insights for subsequent model studies.

Market-based PIT PD, TTC and TiC Credit Rating

Understanding how information affects stock prices and extracting credit information for company credit assessment are central to this model. It improves upon the KMV method, addressing issues like EDF rating instability and the conversion of EDF PIT ratings into TTC ratings by incorporating TiC rating theory.

Credit Rating/Score Card Models

This module covers the core of the Basel Accord, emphasizing Through-The-Cycle ratings and cross-cycle data. Various rating methods and their unification through Time-Consistent (TiC) rating theory are explored, along with practices for rating conversions and comparisons.

PD/LGD/EAD/EL Loss Model (CCR: Counterparty)

Unlike stress testing and economic capital models, the basic parametric models for credit ratings in the Basel Accord have different methodologies. These models, constructed from practical explorations rather than theoretical assumptions, serve as fundamental blocks for further analysis.

PD Term Structure & Loss Model

Previously overlooked, the term structure of default rates now forms the core structure of new ECL and stress testing models. This module addresses the scarcity of trainings focused on default rate term structures and the potential inaccuracies when using certain default rates for multi-period analysis.

Transition Matrix & Loss Model/Decomposition of Transition Matrix

The transition matrix, a vital tool for monitoring credit changes, is explored in depth. A unique matrix decomposition method is introduced, revolutionizing past approaches and providing a new pathway for subsequent applications.

Correlations: Asset Correlation, Default, PD & LGD, EL, StDev

Correlations play a crucial role in portfolio risk models, aggregating risk but often being unobservable. This module selects methods and relationships for asset and default correlations, PD and LGD correlations, loss correlations, and their connection to loss volatility.

Concentration & Diversity Score (UL) and (Regulatory) SF Model/Capital Implied Concentration Model

Risk concentration can lead to unforeseen losses and directly impact capital ratios. This module explores methods to measure and monitor risk concentration, addressing the conflict between capital calculation and accounting standards.

CCAR: Commercial/Wholesale Portfolio Loss Forecast

This module focuses on the commercial credit model within the Fed's CCAR stress testing. It introduces a unique conditional transition matrix approach developed by PFPA, suitable for stress testing and unifying different modeling methods.

CCAR: Consumer/Retail Portfolio Loss Forecast

Examining the consumer credit model in the Federal Reserve's CCAR stress testing, this module navigates the complexities of predicting losses over two time axes. It integrates various impacts, including macroeconomic indicators, calendar time, and age, for accurate loss predictions.

Expected Credit Losses (ECL)

Balancing historical experience with scenario-based predictions, the ECL model seeks to comply with accounting standards while maintaining stability, preferably not entirely procyclical.

EC Parameter Model (PD/LGD/EAD/Correlation)

The Basel economic capital's risk parameter model differs significantly from rating risk and stress testing models, requiring extensive simulations and correlation models between risk parameters.

EC Model Simulation Model

This module delves into the simulation of economic capital, emphasizing the need for careful study of credit types, risk characteristics, and appropriate simulation approaches.

Copula Model

With different credit portfolios requiring different loss calculation methods, a Copula approach is essential for superimposing non-linear capital distributions. This module introduces unique Copula theory research and practical modeling experiences.

Capital Allocation Model

Addressing conflicts between capital non-additivity and accounting standards, this module introduces a passage from linearity to nonlinearity for capital allocation, providing stability without negative capitals.

Risk Management & Model Validation Process Workout

Recognizing the crucial role of model risk, this module emphasizes understanding and monitoring model risks, a top priority for regulators worldwide.

AML & Anti-Fraud Models - Threshold Tuning

Anti-money laundering regulations present another regulatory priority, creating new employment opportunities. This module introduces quantitative analyses to aid in understanding AML regulations, distinct from common anti-fraud analyses.