Credit Risk Modeling & Database Manager - Vice President

Role Description

SMFL is seeking a Credit Risk Modeling & Database Manager. Credit risk models play a crucial role in credit decisioning by helping financial institutions evaluate the creditworthiness of borrowers. These models quantify the potential risks associated with lending, enabling informed decisions and mitigating potential losses. They are essential for assessing the probability of default, estimating potential losses in the event of default, and determining appropriate interest rates.

Credit risk models can be based on financial statement analysis, default probability, or machine learning techniques. They are used to determine the level of credit risk associated with extending credit to a borrower, ensuring that lenders make informed decisions and manage their credit risk exposures effectively.

The process of credit risk modeling involves data collection, preprocessing, modeling techniques, model validation, and compliance with regulatory requirements. It is a structured approach that ensures the models are robust, transparent, and high performing, supporting better decision-making and compliance with evolving standards.

SMFL utilize two types of models: the Quick Decision Model (Scorecard) and G
- Grade model which is based on a Probability of Default Model (Moody’s’ Risk Calc) in determining the creditworthiness of borrowers. Credit Risk Modeling Analyst role is to monitor, validate and maintain credit risk models to predict default probability and potential losses and to be in compliance with SMBC Model Risk Governance Policy and to be in compliance with Regulatory Guidance SR 11-7.

• Monitoring and managing SMFL credit models and adhere to the Model Risk Management Policy of SMBC and SR 11-7 Fed Guidance

• Analyzing complex data sets to identify patterns and trends
  - Development data vs Actual data

• Presenting findings to stakeholders and making recommendations based on the data

• Monitoring and validating model performance and updating models as needed and preparing Model Monitoring reports in collaboration with SMFL HQ

• Staying current with economic conditions and regulatory changes that may impact risk models

• Collaborating with other departments, such as Finance and Operations, to understand the company’s risk exposure

• Documenting all processes and methodologies used in risk modeling for transparency and future reference

• Providing training and guidance to other team members on risk modeling techniques and strategies

• Ensuring compliance with all industry regulations and standards for risk modeling

• Monitor the gradings process such as internal Risk rating /JP and US Reg classification ratings, update applicable scorecards, monitor limits, oversee data accuracy to ensure compliance with business and institutional guidelines and ensure data integrity in the credit systems.

• Manage database for model monitoring purposes specifically for G grade model and if required for QDM scorecard.

• Prepare and present updates on Portfolio Reviews to business management and Risk when requested and support the portfolio analysis trends and reporting and analytics.

• Participate in new credit products approval and grading methodologies at the beginning.
  
  E.g., Structured finance leases, Synthetic Leases etc.

• Work with Internal Control in managing and monitoring the Model Risk Management and Risk Appetite metrics for Credit and escalating the breachers to Senior Management.

• Participate in Policy and Procedure updates related to Credit Risk Models and grading and amendment annually and approval process.

• Develops close working partnerships with the Leasing sales team, operations and other internal company staff to ensure open communication channels are available to handle issues associated with data-driven requests. Responds to calls and requests for information from the team.

• Able to design databases that are efficient, reliable, and scalable. Should be capable of logical and physical design of databases and be able to implement best practices in different database architectures. Should be able to perform data analysis and inform decision-making based on data interpretation and visualization. Should be proficient…