Risk Associate

About Credit Saison India

Established in 2019, CS India is one of the country’s fastest growing Non-Bank Financial Company (NBFC) lenders, with verticals in wholesale, direct lending and tech-enabled partnerships with Non-Bank Financial Companies (NBFCs) and fintechs. Its tech-enabled model coupled with underwriting capability facilitates lending at scale, meeting India’s huge gap for credit, especially with underserved and under penetrated segments of the population.
Credit Saison India is committed to growing as a lender and evolving its offerings in India for the long-term for MSMEs, households, individuals and more. CS India is registered with the Reserve Bank of India (RBI) and has an AAA rating from CRISIL (a subsidiary of S&P Global) and CARE Ratings.
Currently, CS India has a branch network of 45 physical offices, 1.2 million active loans, an AUM of over US$1.5B and an employee base of about 1,000 people.
Credit Saison India (CS India) is part of Saison International, a global financial company with a mission to bring people, partners and technology together, creating resilient and innovative financial solutions for positive impact.
Across its business arms of lending and corporate venture capital, Saison International is committed to being a transformative partner in creating opportunities and enabling the dreams of people.
Based in Singapore, over 1,000 employees work across Saison’s global operations spanning Singapore, India, Indonesia, Thailand, Vietnam, Mexico, Brazil.
Saison International is the international headquarters (IHQ) of Credit Saison Company Limited, founded in 1951 and one of Japan’s largest lending conglomerates with over 70 years of history and listed on the Tokyo Stock Exchange. The Company has evolved from a credit-card issuer to a diversified financial services provider across payments, leasing, finance, real estate and entertainment.

Basic Qualifications:

- Bachelors or Master's degree in, Statistics, Economics, Computer Science, or other Engineering disciplines
- 3+ years of experience working in Data science/Risk Analytics/Risk Management with experience in building the models/Risk strategies or generating risk insights
- Proficiency in SQL and other analytical tools/scripting languages such as Python or R- Deep understanding of statistical concepts including descriptive analysis, experimental design and measurement, Bayesian statistics, confidence intervals, Probability distributions
- Proficiency with statistical and data mining techniques
- Proficiency with machine learning techniques such as decision tree learning etc. Should have an experience working with both structured and unstructured data
- Fintech/Retail lending experience is preferred

Key Responsibilities:

- Conduct Portfolio Analysis and Monitor Portfolio delinquencies at a micro level, identification of segments, programs, locations, and profiles which are delinquent or working well for various co-lending partnerships
- Form, manage and execute the policies around risk management for all Co-lending partnerships across products
- In conjunction with business team, conduct thorough risk assessments to ensure we onboard the right partners that meet our strategic goals and objectives.
- Develop credit strategies across the customer lifecycle (acquisitions, management, fraud, collections, etc.)
- Identify trends by performing necessary analytics at various cuts for the Portfolio
- Provide analytical support to various internal reviews of the portfolio and help identify the opportunity to further increase the quality of the portfolios of different co-lending partners
- Work with Product team and engineering team to help implement the Risk strategies - Work with Data science team to effectively provide inputs on the key model variables and optimize the cut off for various risk models
- Create a deep level understanding of the various data sources (Traditional as well as alternate) and optimum use of the same in underwriting - Should have good understanding about various unsecured credit products - Should be able to understand the business problems and help convert them into the analytical solutions