Senior Consultant - Risk Analytics - Credit Risk Team

Consultant - Risk Analytics - Credit Risk Team/ Wells Fargo

Risk Analytic Consultant :

Relevant keywords/business areas- Credit Risk Strategy development, Policy Analytics, Acquisition strategy, Approve/Decline, Credit Limit/Line Assignment, Credit Line increase/decrease, Portfolio Management, Approval criteria, Portfolio Management, Portfolio Analytics, Pricing, Personal Loans/Cards/Mortgages/Auto, Top-Up Strategy, Balance Transfer, Loan on cards

Tools - SAS, SQL, Tableau, Python, R

SAS is Mandatory

About the Role :

- The incumbent would functionally contribute as an SME to the Business Cards Fraud risk team and lead large and complex projects.

- Analyze portfolio performance to identify risk factors at an early stage

- Analyze the performances and derive data-driven insights for creating risk strategies in acquisition and portfolio management

- Develop analytical tools ranging from customer segmentation to scorecard development

- Monitor the performance of different acquisition vintages as well as the roll rate of different past due/default buckets to define portfolio performance trend

- Support the development of instrumentation across underwriting, portfolio management, and collections

- Analysis of opportunity and risk of new product offering

- Perform other related duties as required and assigned

- Assesses and predicts risk and performance through business analysis and/or modeling. Establishes effective policies, processes, and tools to identify and manage risks. Uses predictive sciences for developing future-ready solutions.

- Analyzes big data and understand/monitor trends to provide actionable insights across a range of risk analytics initiatives.

- Develop new and enhance existing models for managing fraud risk, payment risk, credit bust outs, and credit abuse. Evaluate new data sources and attributes, internal and external from extensive case reviews for efficacy in models.

- Builds risk rating methodologies using advanced analytics approaches including statistical modeling and machine learning techniques, not limited to Random Forest, Decision Trees, Segmentation, and/or Time-series modeling. Expert in statistical software such as SAS, SQL, Python, and E-miner, among others.

- Develops quantitative/qualitative models for forecasting losses in supporting portfolio planning, loan loss provisioning, or new account acquisitions.

- May provide input for CCAR, Basel, and other regulatory submissions.

- Develop complex programming models to extract data and/or manipulate databases such as ORACLE, and Teradata.

- Develops comprehensive monitoring frameworks and dashboards and provide statistically sound diagnostic evaluation of any emerging or unexpected risk areas to enhance intelligence and facilitate faster decision-making.

- Reports on asset quality, portfolio trends, and credit policy exceptions across various credit, vintage, product, offer, channel, and industry segments using visualization tools such as Excel VBA, Tableau, and/or SAS Visual Analytics.

- Ensures resolution of matters requiring attention (MRA) from outside regulators, Audit, Corporate Model Risk, or internal review teams.

- May partner with other business units, Audit, Legal, regulators, and industry partners on risk related topics.

- Leads the implementation of complex initiatives with moderate to high risk for the line of business

- Lead project teams and may mentor but do not manage other team members.

- To be effective in this position, you will need to have a deep understanding of credit from an acquisitions perspective, a broad and strategic perspective on risk management, practice in the test and learn discipline for strategy development, and be fluent in both the key technical tools of credit risk decisions and sound credit judgment.

Required Qualifications :

- Overall experience of around 3.5-8 years in a similar role with at least 3-8 years of unsecured lending experience in analytic functions

- Bachelor's degree or higher in a quantitative field such as applied mathematics, statistics, engineering, finance, economics, econometrics, or computer sciences

- Experience in credit risk analytics

- Advanced SAS, Macros knowledge, R, Python

- Advanced degree in statistics/finance /engineering/other quantitative disciples

- Strong Knowledge of MS Office Tools / Segmentation Tools / Decision Trees

- Strong technical skills and problem-solving skills

Desired Qualifications :

- A strong track record and business knowledge in the financial services sector with a deep understanding of credit card credit or fraud risk from a full-cycle perspective, including testing and control within a large financial institution.

- Master in a Quantitative or Advanced Analytics discipline

- This oversight extends to all phases of a loan's life cycle, including origination, underwriting, risk analysis, approval, documentation, boarding, monitoring, loss recognition, modification, and collection activities.