Assistant Vice President - Model Risk & Fraud Insights

The role is responsible for creating and owning the model risk management policy for the company. Additionally, this role will develop Fraud insights to enable the Risk function to become the Second line of defence for Fraud management.

Role Accountability

1.Create & own model risk management policy

2.Ensure External validation for Models as per policy

3.Develop ML model expandability framework/ model performance tracking capabilities

4.Manage the model governance processes as per defined the policy

5.Review models across the org and provide inputs and highlight risks prior to model approval

6.Publish Model monitoring MIS & Alerts

7.Establish new model approval thresholds

8.Review new model approval request and recommend for approval/additional information

9.Develop Fraud insights and reporting enabling Risk function to function as Second line of defense for Fraud

10.Develop and refresh stress testing models and publish associated MIS

11.Develop challenger and models, target definitions, early performance indicators

12.Handle and address queries from Auditors on Model governance

Measures of Success (Define the Outcomes expected of the role)

1.Timely and accurate generation of all MIS & Reports

2.Timely review of model approval request

3.Create and maintain model inventory for the organization

4.Timely and accurate updation of process documentation

5.Process Adherence as per MOU

5. Fraud MIS, KPIs, and calendar

Technical Skills / Experience / Certification (If any) 1.Good knowledge of SAS, MS Office, BI tools (SAS VA / Tablueau / Qlikview) etc. Knowledge of R / Python preferred

2.Ability to understand all credit card related data elements

3.Strong understanding of modelling concepts and model monitoring as a line of defense

4.Ability to work in a distributed data storage environment

Competencies critical for role:

Teamwork and Collaboration

Analytical ability

Problem solving skills

Must have Qualification

Master's degree in computer applications, economics, statistics, operations research, mathematics, engineering, business, or related field with a strong quantitative emphasis.

9-12 years of core model build / model monitoring / model diagnostics / evaluation experience.

6-8 years of mid management experience in: Analytics, Automation, Business Intelligence, ML model development or model monitoring.