Assistant Manager - Credit Risk Expected Credit Loss (ECL)
Mumbai, Maharashtra, India · Full Time
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- Experience
- 3–6 yrs
- Salary
- —
- Openings
- 1
- Posted
- 3 hours ago
- Work mode
- In office
- Education
- CA / FRM / CFA / MBA (Finance) / Master's in Statistics, Economics, Mathematics or related field
- Resume
- Required to apply
Where you'll work
Job description
Role Summary
The position of Assistant Manager in Credit Risk ECL focuses on aiding the creation, validation, implementation, and supervision of Expected Credit Loss models under IFRS 9/Ind AS 109 frameworks.
Primary Duties
- Assist in the creation and refinement of IFRS 9/Ind AS 109 ECL models.
- Execute ECL computations and perform impairment evaluations at the portfolio level.
- Examine and track crucial risk parameters such as Probability of Default (PD), Loss Given Default (LGD), and Exposure at Default (EAD).
- Conduct monitoring activities related to model efficacy, including back-testing and sensitivity assessments.
- Participate in validation of models and address findings from validation exercises.
- Assess the impact of macroeconomic assumptions and incorporate forward-looking adjustments.
- Develop risk-related analytics, management summaries, and detailed documentation for models.
- Collaborate with stakeholders from business units, finance, audit, and regulatory compliance departments.
- Provide support for audits internal and external, focusing on ECL frameworks.
- Ensure adherence to regulatory mandates and accounting standards.
Required Qualifications & Experience
- Qualifications such as Chartered Accountant (CA), Financial Risk Manager (FRM), Chartered Financial Analyst (CFA), MBA specializing in Finance, or a Master's degree in Statistics, Economics, Mathematics, or comparable fields.
- Between three to six years of professional experience in Credit Risk, IFRS 9, ECL, Risk Analytics, or Model Validation domains.
- Comprehensive knowledge of PD, LGD, EAD metrics and the methodologies of Expected Credit Loss.
- Hands-on experience with analytical tools including SAS, SQL, Python, or R.
- Understanding of banking products and principles of credit risk management.