Credit Risk Fraud Analyst Jobs in Connecticut (NOW HIRING)

**If the final candidate is not near one of the above referenced locations, there might be a possibility for a remote arrangement.

Provides experienced support in the development and analysis of quantitative/econometric behavioral models used for credit risk, interest rate risk and liquidity risk management, as well as balance sheet and capital planning. Supports more experienced analysts and management in data analysis, model development efforts and ad-hoc analysis as needed. Provides guidance and direction to less experienced personnel as needed.

• With experienced skillset, assist in researching and developing quantitative behavioral models used for credit risk, interest rate risk and liquidity risk management, as well as balance sheet and capital planning, including but not limited to, loan delinquency, default and loss models, loan prepayment and utilization models, deposit attrition models and financial instrument valuation methods.
• Prepare, manage and analyze large customer loan, deposit and/or financial data sets for statistical analysis in Structured Query Language (SQL) or similar tool to properly specify and estimate econometric models to understand customer or Bank behavior for purposes of credit, interest rate, liquidity or stressed capital risk management. Understand the context of the Bank's data and businesses to ensure properly developed models.
• Run regressions (including time series and logistic regression), programming routines and other econometric analyses to specify models using appropriate statistical software; communicate results, including graphic and tabular forms, to fellow team members, Treasury management and Bank-wide stakeholders, including the business lines and Risk Management colleagues to demonstrate key risk drivers and dynamics of model output.
• Execute models in production environment; communicate analytical results to Bank-wide stakeholders.
• Track portfolio performance, model performance, campaign tracking and risk strategy results. Incorporate observations and data into existing models to improve predictive results. Identify deviations from forecast/expectations and explain variances. Identify risk and/or opportunities.
• Develop and maintain satisfactory model documentation, including process narratives and performance monitoring guidelines to serve as reference source.
• Provide financial analysis and data support to other groups/departments across the Bank as required. Support engagements with colleagues in Model Risk Management for model validation exercises.
• Provide guidance and direction to less experienced personnel regarding all aspects of data and financial analysis and development and management of predictive statistical models.
• Conduct business in compliance with regulatory guidance including SR (Supervision and Regulation Letters) 10-1, SR 10-6, SR 11-7, Enhanced Prudential Standards, etc. Adhere to applicable compliance/operational/model risk controls and other second line of defense and regulatory standards, policies and procedures.
• Understand and adhere to the Company's risk and regulatory standards, policies and controls in accordance with the Company's Risk Appetite. Identify risk-related issues needing escalation to management.
• Promote an environment that supports belonging and reflects the M&T Bank brand.
• Maintain M&T internal control standards, including timely implementation of internal and external audit points together with any issues raised by external regulators as applicable.
• Complete other related duties as assigned.

The position serves as an experienced analyst in the use of statistical programming languages to analyze Bank datasets and development, implementation and maintenance of behavioral models. It is important for the position to communicate with clear narratives, compelling data visualization and technical precision, both in-person and in writing, to enable audiences to understand the analyses and forecasts. The position partners and collaborates with colleagues in related functions, including Credit Risk Management, Asset Liability and Liquidity Management, Model Risk Management and business lines to implement and understand models for Bank use. This role is highly technical in nature and requires demonstrated attention to detail, execution and follow-up on multiple initiatives with Treasury and across the Bank. The ability to identify, analyze, rationalize and communicate complex business, data and statistical problems and recommend corresponding solutions is a key factor of success in this role.

Not Applicable

• Bachelor's degree and a minimum of 1 years' proven quantitative behavioral modeling experience, or in lieu of a degree, a combined minimum of 5 years' higher education and/or work experience, including a minimum of 1 years' proven quantitative behavior modeling experience
• Minimum of 1 years' on-the-job experience with pertinent statistical software packages (SAS, Python, Stata, R)
• Strong Python skills required
• Model development experience required, including familiarity with logistic regression and linear regression
• Minimum of 1 years' on-the-job experience with data management environment, such as SQL Server Management Studio
• Minimum of 1 years' experience in managing and analyzing large data sets and explaining results of analysis through concise written and verbal communication as well as charts/graphs

Education and Experience Preferred:

• Masters' of Science or Doctorate degree in Statistics, Economics, Finance or related field in the quantitative social, physical, or engineering sciences, with proven coursework proficiency in statistics, econometrics, economics, computer science, finance or risk management
• Minimum of 2 years' statistical analysis programming experience
• Credit model development experience; Consumer portfolio model development experience highly preferred
• One (1) or more years of on-the-job Python programming experience
• Fluency and high proficiency in econometric/statistical techniques, especially time-series analysis, panel data methods and logistic regression
• Experience in balance sheet management and mathematical modeling of financial instruments offered by banks
• Knowledge and familiarity with key aspects of model risk management and model validation, including SR-11-7 guidance on model risk management
• Proven track record for being able to work autonomously and within a team environment
• Demonstrated leadership skills
• Strong desire to learn and contribute to a group