Credit Risk Analyst

4 weeks ago

Navi Mumbai Mumbai City Mumbai, India Skillventory Full time
Job Description

We are seeking a detail-oriented and analytical Credit Risk Analyst to join our team. The ideal candidate will specialize in credit risk modeling, particularly in Expected Credit Loss (ECL) under CECL/IFRS 9, and the development and validation of PD (Probability of Default) and LGD (Loss Given Default) models. This role is critical in driving business growth, optimizing risk through analytical solutions, and ensuring compliance with regulatory standards.

Roles & Responsibilities:

- Credit Risk Assessment & Modelling: Use statistical techniques to design advanced analytical scorecards for customer acquisition and lifecycle management. Analyze financial data and borrower behavior to assess creditworthiness.
- Risk Analytics: Focus on Ind AS, ECL, BASEL, and IFRS9 Modeling. Develop, validate, and implement credit risk models for ECL and PD/LGD estimation.
- Data Analysis: Compile and analyze macroeconomic and borrower-specific data for model inputs. Conduct portfolio analysis and deep dives on various segments and cohorts.
- Machine Learning: Use Machine Learning algorithms for advanced analytics and data augmentation through external data providers.
- Cross-functional Collaboration: Collaborate with risk, credit, finance, and data teams to align risk models with business needs and provide insights to optimize credit decisions.
- Implementation & Monitoring: Work with business users to implement and monitor analytical solutions via an IT platform.

Skills Required:

- Strong understanding of CECL/IFRS 9 frameworks and credit risk metrics.
- Proficiency in statistical software such as SAS, R, or Python.
- Experience in designing advanced analytical scorecards and using Machine Learning algorithms for analytics.
- Knowledge of Expected Credit Loss (ECL), PD (Probability of Default), and LGD (Loss Given Default) model development and validation.
- Excellent analytical, problem-solving, and communication skills.

QUALIFICATION:

- Bachelor's or Master's degree in Finance, Economics, Statistics, or a related field from a premium institute.

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