Reporting to the Head of Credit Risk, you will be focused on the analysis and reporting of credit risk metrics including key performance indicators of the retail lending portfolio. This will also cover the development and enhancement of periodic reporting and MIS for the broader business.
Job Responsibilities:
Design, build, implement and maintain credit risk reports.
Analyse and report portfolio KPIs to the Bank's senior management, ensuring information is accurate.
Analysis of credit risk data against past and expected trends to sufficiently explain key portfolio variance / performance drivers.
Portfolio analysis, monitoring and reporting.
Perform system UATs for credit risk-related systems (with a focus on reporting tools).
Employ advanced analytics and data visualisation to support credit portfolio. management and business needs.
Conduct regular portfolio performance analyses including limit utilisation and to identify segments for credit limit review.
Work with Collections using customer repayment behaviour to identify the possibility of fraudulent applications, and escalate such cases to the Fraud team for investigation.
Assist in building predictive models using machine learning to facilitate credit decisioning and early warning.
Deploy, monitor, and maintain the models on production credit systems.
Work with data engineers to construct data pipelines to integrate new data feeds.
Advance the discussion on governance and assurance regarding the use of artificial intelligence and machine learning in credit risk applications.
Provide analytical / statistical data to support recommendation to enhance credit policies, scoring, segmentation, simulation techniques and management actions to be taken.
Provide inputs on test scenarios and using outcomes, provide recommendations to changes in parameter settings as well as changes in policies.
Proactively engage the Credit Risk Managers on portfolio shape, trends and abnormalities in the portfolio movements.
Assist in ongoing Credit Risk enhancement projects (vendor selection and management, systems implementations, etc.) .
Our Ideal Candidate
Bachelor's or postgraduate degree, preferably in Data Science, Computer Science, Mathematics or an equivalent quantitative area.
At least three years of relevant experience in banking (preferably in a risk, business or product role for a retail lending portfolio). At least five years experience preferred.
Familiarity with the Singapore retail lending market and regulatory environment strongly preferred.
Proficient with MS Office suite or equivalent. (Familiarity with Google Workspace preferred.)
Proficiency in machine learning frameworks, Python and SQL preferred.
Ability to produce high quality analysis using structured and unstructured data.
An enthusiastic team player who can figure it out, get stuff done, have fun and is excellent in communication and stakeholder management.
