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Hourly Credit Risk Modeling information

What is hourly credit risk modeling?

Hourly credit risk modeling is the process of assessing and predicting the likelihood of a borrower defaulting on their financial obligations, with risk evaluated and updated on an hourly basis. This approach is often used by financial institutions and fintech companies that require real-time credit risk analysis for instant lending decisions or ongoing portfolio monitoring. By utilizing real-time data and advanced analytics, hourly credit risk modeling enables lenders to respond quickly to changes in a borrower's financial behavior or external market conditions. This leads to more accurate risk assessments and helps institutions manage their exposure more effectively.

What is the difference between Hourly Credit Risk Modeling vs Credit Analyst?

AspectHourly Credit Risk ModelingCredit Analyst
Primary FocusDeveloping and implementing credit risk models to assess borrower riskAnalyzing credit data to evaluate creditworthiness of individuals or companies
Required SkillsStatistical analysis, modeling, programming, financial analysisFinancial analysis, credit report review, communication skills
Work EnvironmentFinancial institutions, consulting firms, often project-basedBanks, lending institutions, credit departments
CertificationsOften requires CFA, FRM, or similar certificationsTypically requires finance or accounting degrees; certifications like CFA are common

Hourly Credit Risk Modeling involves creating quantitative models to predict credit risk, often requiring advanced statistical and programming skills. Credit Analysts focus on evaluating individual credit data to make lending decisions. While both roles require financial knowledge and may share certifications, their core responsibilities differ: one is model development, the other is credit evaluation.

What are the key skills and qualifications needed to thrive as an Hourly Credit Risk Modeler, and why are they important?

To thrive as an Hourly Credit Risk Modeler, you need strong quantitative skills, a background in finance, economics, mathematics, or statistics, and experience with credit risk principles. Familiarity with statistical software such as SAS, R, or Python, as well as knowledge of risk modeling frameworks and regulatory requirements, is typically required. Analytical thinking, attention to detail, and effective communication are crucial soft skills for interpreting data and presenting findings to stakeholders. These skills are essential for accurately assessing credit risk, supporting sound decision-making, and ensuring regulatory compliance in financial institutions.

How does an Hourly Credit Risk Modeling professional typically collaborate with other departments within a financial institution?

Hourly Credit Risk Modeling professionals often work closely with teams such as underwriting, data analytics, and IT to ensure credit risk models are accurate and actionable. They may participate in cross-functional meetings to discuss model performance, share insights from data analysis, and implement feedback from business stakeholders. Collaboration is key, as their models directly influence lending decisions, risk management strategies, and regulatory compliance. Regular communication with colleagues helps ensure that risk models stay aligned with evolving business needs and regulatory requirements.
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Hourly Credit Risk Modeling jobs near you
Risk Analyst II - Credit Risk Analytics

Risk Analyst II - Credit Risk Analytics

GM Financial

Fort Worth, TX • On-site

Full-time

Retirement

Posted 4 days ago

GM Financial rating

7.7

Company rating: 7.7 out of 10

Based on 38 frontline employees who took The Breakroom Quiz

72nd of 140 rated vehicle equipment hire

Job description

Job Description
Why Credit Risk Analytics?
GM Financial is the wholly owned captive finance subsidiary of General Motors and is headquartered in Fort Worth, U.S. We are a global provider of auto finance solutions, with operations in North America, South America and the Asia Pacific region. Through our long-standing relationships with auto dealers, we offer attractive retail financing and lease programs to meet the needs of each customer. We also offer commercial lending products to dealers to help them finance and grow their businesses.
At GM Financial, our team members define and shape our culture - an environment that welcomes new ideas, fosters integrity and creates a sense of community and belonging. Here we do more than work - we thrive.
Our Purpose: We pioneer the innovations that move and connect people to what matters
About the role:
Risk Analyst II - Credit Risk Analytics is responsible for analyzing credit risk exposure related to consumer and commercial loan and lease acquisition activities. The position involves conducting analysis to mitigate credit risk, create and monitor credit policy, measure credit execution and credit structure. In addition, this position will be responsible for monitoring credit performance by region, credit center, and dealership.
Responsibilities
In this role, you will:
  • Utilize data mining and advanced spreadsheet skills to quantify credit risk related to loan and lease origination activities
  • Summarize findings, develop recommendations and present analyses to management in a clear, concise, convincing, and actionable format
  • Employ best practices of data analysis and validation to ensure data results are accurate
  • Assist in the creation, maintenance and monitoring of origination credit policies, procedures and lending stipulations
  • Understand how change to origination credit policy and lending environment can impact loan/lease volume as well as overall credit performance
  • Proactively monitor and report relevant changes in origination trends and portfolio performance to management
  • Conduct ad hoc research projects incorporating project design, data collection and analysis, summarization of finding, and presentation of results

Qualifications
What makes you an ideal candidate?
  • Demonstrated understanding of data warehouses, data mining, reporting, data analysis and data visualization techniques
  • Advanced with Microsoft Excel, PowerPoint, and Word
  • Experience with respect to data analysis and spreadsheet modeling and/or reporting
  • Experience with coding (SAS or SQL preferred) for data mining and manipulation
  • Querying skills and knowledge in a data warehouse environment
  • Demonstrated quantitative skills
  • Ability to interact collaboratively and proactively with internal customers
  • Capable of managing multiple projects, including ability to coordinate and balance numerous tasks in a time-sensitive environment, under pressure, meeting deadlines
  • Understanding the metrics utilized in monitoring the performance of a consumer or commercial lending portfolio is a plus

Experience:
  • 2-4 years' experience working with complex Excel workbooks, querying large multi-table datasets, data analysis, and data presentation; the qualified candidate will also be able to demonstrate proficiency with the following tools: SAS and/or SQL, Microsoft Excel, PowerPoint, and Word Req
  • 2-4 years' experience in consumer and/or commercial loan and lease origination analysis Pref
  • Bachelor's Degree Finance, Economics, Mathematics, Business, Business Analytics, MIS, or other quantitative field; degrees in non-quantitative fields considered with adequate work experience Required
  • Master's Degree Finance, Economics, Mathematics, Business, Business Analytics, MIS, or other quantitative field Preferred

What We Offer: Generous benefits package available on day one to include: 401K matching, bonding leave for new parents (12 weeks, 100% paid), tuition assistance, training, GM employee auto discount, community service pay and nine company holidays.
Our Culture: Our team members define and shape our culture - an environment that welcomes innovative ideas, fosters integrity, and creates a sense of community and belonging. Here we do more than work - we thrive.
Compensation: Competitive pay and bonus eligibility.
Work Life Balance: Flexible hybrid work environment, 2 days a week in the office.

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