Exploring Core Concepts of Credit Risk Management
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Chapter 1: Understanding Credit Risk
In recent times, there has been a noticeable emphasis on the latest algorithms within the data science sphere. Daily, we encounter innovative methodologies that enhance the efficiency of our predictive models. However, amidst this wealth of information, it's crucial for data scientists to grasp the business context of the models they develop. My experience in the credit risk sector has prompted me to write a series of articles detailing the key terminologies that anyone interested in creating a credit risk model should familiarize themselves with.
What is Credit?
When entity X borrows a specific amount A from entity Y, entity X is recognized as the borrower while entity Y is the lender. The amount A is the credit extended by the lender to the borrower.
What is Credit Risk?
Credit risk emerges for entity Y if entity X fails to repay the borrowed amount. This concept can be linked to personal banking experiences. Banking systems fundamentally operate on two principles: customers deposit their savings, for which banks pay interest, making it attractive for customers to use savings accounts. Subsequently, banks lend a portion of these deposits to individuals or entities at interest rates, thus ensuring profitability. For this system to function sustainably, banks must carefully assess the risk associated with each borrower. Low-risk borrowers are less likely to default on payments, while high-risk borrowers have a greater chance of defaulting.
When a borrower defaults, the bank faces credit risk. To mitigate this risk, various regulatory bodies oversee banking operations, ensuring compliance with a set of regulations. International banks, in particular, face more rigorous scrutiny than those operating solely within a single country.
The Basel Committee on Banking Supervision stands out as a major regulatory authority, having established three Basel Accords, which set forth global banking regulations.
Chapter 2: The Basel Accords
The Basel Accords consist of three series of regulations (Basel I, II, & III) designed to enhance banking standards.
Fundamentals of Credit Risk
This video delves into the foundational aspects of credit risk management, essential for professionals in the field.
Basel I Accord
Introduced in July 1988, Basel I aimed to identify credit risk, marking a significant advance in banking regulations. It mandated that banks classify their assets based on risk levels—0%, 10%, 20%, 50%, and 100%. International banks were required to maintain a minimum capital ratio of 8% of their risk-weighted assets (RWA). For example, a bank with RWA of $10 million must hold at least $0.8 million in capital.
A bank’s capital comprises Tier 1 and Tier 2 capital. Tier 1 includes primary assets like equity and disclosed reserves, while Tier 2 encompasses undisclosed reserves and supplementary capital.
Basel II Accord
Launched in December 2007, Basel II introduced enhancements to Basel I. Key focus areas included risk-sensitive capital allocation, improved disclosure requirements for transparency, effective alignment of capital requirements, and ensuring proper risk quantification by banks.
This accord established three pillars:
- Minimum capital requirements based on a bank’s actual risk of economic loss, determined through RWA.
- Supervisory Review Process, where supervisors assess banks’ activities to ensure they hold adequate capital.
- Market Discipline, emphasizing stringent disclosure requirements to allow market participants to evaluate a bank's capital adequacy.
Credit Risk Introduction
This video introduces the fundamental concepts of credit risk, crucial for aspiring credit risk analysts.
Basel III Accord
Launched in July 2013, Basel III established stricter guidelines following the 2008 financial crisis. Notable features included requirements for banks to maintain minimum common equity tiers, raising the Tier 1 capital requirement to 6%, and introducing a 3% leverage ratio.
IFRS 9
IFRS 9, effective January 1, 2018, addresses financial instrument accounting. Unlike its predecessor, IAS 39, it focuses on expected loss rather than actual loss, necessitating models to compute both 12-month and lifetime expected losses.
Key Concepts in Credit Risk Modeling
Expected Loss (EL) can be defined as the anticipated average loss a bank expects over a year. It is calculated using the formula:
EL = Probability of Default (PD) * Loss Given Default (LGD) * Exposure At Default (EAD).
In conclusion, understanding these foundational concepts of credit risk is vital for anyone looking to delve deeper into this field. Stay tuned for further discussions on this topic!
References:
- Developing Credit Risk Models Using SAS® Enterprise Miner™ and SAS/STAT by Iain J. Brown
- Investopedia for relevant definitions
- Wikipedia for general information
Happy Learning, Happy Growing!