Distance Education Executive Development Programme in Applied Credit Risk Analytics IIM Kashipur

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celearning.edu.in
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Executive Development Programme in Applied Credit
Risk Analytics from IIM Kashipur
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INTRODUCTION
Credit Risk quantification has emerged as a very
important component to a firms financial well-
being. This course provides training on the usage
of tools used in quantification of credit risk
and problems related to credit risk management.
The course is full of hands-on and implementation
of tools and techniques using data. The course
will provide the practitioners perspective in
measuring various dimensions of credit risk. It
attempts to strike a balance between
institutional details, theoretical foundations,
and practical applications. The course will
extensively make use of MS Excel mainly and for
some models R will be used.
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OBJECTIVE
To predict the likelihood of default, lenders
leverage historical data to guess how a consumer
will behave in the future. Traditionally, credit
risk models look for behavioral patterns in
factors ranging from payment history to current
level of indebtedness to average length of credit
history. This is typically measured as a persons
FICO score, an analytically derived score that
assesses credit risk to help lenders determine
whether a consumer is a good candidate for a loan
and what interest rate is most appropriate. The
rise of analytics and Big Data have helped
enhance the process of credit risk measurement.
By leveraging data, there is less guesswork and
more science behind the ability to predict
whether someone will default on any given loan.
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CAREER OPTIONS

• After the successful completion of the course,
  the student will have ample amount of career
  opportunities such as
• Consumer Credit Analyst Career.
• Corporate Credit Analyst Career.
• Financial Institution Analyst.
• Sovereign Credit Analysts.
• Credit Investment Analyst.

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CURRICULUM

• Probability of Default Models (Discrete Time
  Hazard Models)
• Linear model
• Probit
• Logit
• Complementary log-log model
• PIT (Point-in-Time) and TTC (Through-the cycle)
  estimates
• Reduced form model for probability of default
• LGD Estimation Key concepts in default handling
  clients, default,
• Collateral and exposure Non performing loans
  management
• LGD model methodologies Application uses of LGD
• Exposure at Default Regulatory perspective on
  EAD, EAD
• Modelling Credit Line Models.
• Expected and unexpected credit loss.

• Basics of Credit Risk
• Exploratory Analysis
• Empirical Distribution
• Location measures
• Dispersion measures
• Third and fourth moments
• Joint empirical distribution
• Correlation, Sampling
• Hypothesis testing
• Descriptive statistics.
• Credit Scoring Models
• Logit Model for Loan Default Analysis (both
  individual and corporate loan)
• Decision Tree for Loan Default Analysis
• Altman Z Score (For Manufacturing Firm)
• Credit Scoring Models for Private Firm
• Non-Manufacturing Firm and Indian Firms
• Credit VaR for Non-Tradable loans

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CONTACT US
For Admissions/Students queries Send an email
at info_at_distancelearning.edu.in Or You could
call us at Mobile [phone] Whatsapp 91
8010230510 Website https//www.distancelearning.e
du.in/