OPERATIONAL RISK MODELING (ORM)

With the mounting sophistication and complexity of financial institutions, mitigating and avoiding operational risk has become a primary concern for organizations and major regulatory bodies. To mitigate any operational risk, an extensive and pragmatic approach is needed, that asks for strong knowledge of the fundamentals of statistics and current regulatory norms, policies and procedures.

ABOUT OPERATIONAL RISK MODELING

Operational risk modeling helps to measure, monitor and mitigate the threat of direct or indirect loss that may result due to any poor or failed internal processes or may arise due to external events outside the control of the organization’s management’s decisions.

WHAT WILL I LEARN ?

To be able to mitigate any operational risk, the need to have a strong structure covering aspects of statistics and the regulatory norms will be used and taught.

TARGET AUDIENCE

The ideal candidate profiles for this course are the:

• Professionals involved in the domain of Risk Management, mainly those who focus lies upon credit risk models for their institutions

CAREER BENEFITS

• Creates a solid base of statistical concepts necessary for Risk Management jobs
• Provides an edge in the fastest growing industry of Risk Management
• Covers an extensive coverage of the implications of implementation of BASEL II framework
• Provides great exposure to excel in the field of Operational Risk

PRE- REQUISITES

Being adept with various aspects of Microsoft Excel (preferably Excel 2007 or 2010) and operational risk concepts is a prerequisite for this program.

COURSE CURRICULUM

PART 1: Introduction into the concept of operational risk modeling
PART 2: Fundamental concepts for probability distributions
PART 3: Modeling the number of losses on Excel using Frequency Distributions
PART 4: Modeling the size of losses on Excel using Severity Distributions
PART 5: Understanding the Fitting of distributions to data using concepts of Parameter Estimation
PART 6: Learning to Examine the Goodness of Fit
PART 7: Learning to Aggregate loss Modeling
PART 8: Learning to Aggregate ‘Value at Risk’ numbers for Business Lines & Event Types
PART 9: Learning about precautions while implementing EVT/GPD to avoid and evade overestimation of capital needs
PART 10: Understanding the Confidence level for the likely parameters
PART 11: Learning to use External data for Operational Risk (estimation of Value at Risk)
PART 12: Learning to combine capital estimates and numbers, based on the  internal and external data obtained and the scenario analysis
PART 13: Understanding how to use the software(‘R’- an open-source ORM software) for the purpose of Operational Risk Modeling