Fraud Analytics and Innovation Senior Analyst

Bank of America•Fort Worth, TX

About The Position

At Bank of America, the Fraud Analytics and Innovation Senior Analyst performs complex analysis and modeling to minimize fraud loss exposure while limiting negative impacts to the customer experience. The role evaluates data to assess potential and emerging fraud risks, identifies opportunities to reduce billing dispute fraud losses, and develops mitigation strategies that drive measurable results. The analyst uses tools such as Excel, SAS, SQL, Tableau, and relational databases to analyze financial and portfolio trends, support fraud strategies, evaluate performance, and recommend policy or procedural changes. The role partners across Claims, Risk, Product, Technology, and Operations to develop and execute initiatives, and owns performance reporting and regular updates to senior stakeholders. It also monitors post-implementation results and uses them to identify further opportunities for optimization. Bank of America describes an in-office culture intended to support collaboration, engagement, and career development, with flexibility based on role responsibilities and business needs. The position is listed as first shift, United States of America, and 40 hours per week.

Requirements

  • Experience with SQL, SAS, and/or Tableau for data analysis and reporting.
  • Strong analytical and critical-thinking skills, including the ability to translate complex data into actionable strategies.
  • Ability to engage and influence people at all levels, from analysts to senior executives, to build advocacy and drive change.
  • Self-motivation and ability to work independently in a fast-paced environment and deliver results quickly.
  • Experience in fraud strategy, claims, disputes, risk management, or data analytics within financial services.
  • Strong written, verbal, and interpersonal communication skills.

Nice To Haves

  • Bachelor’s degree in business, analytics, risk, finance, or a related field.
  • Experience with card products, payment networks, billing disputes or chargebacks, claims processes, and associated fraud strategies.
  • Process excellence background and/or experience automating or streamlining operational workflows.
  • Experience developing, implementing, or measuring fraud prevention and loss mitigation strategies.
  • Experience collaborating with global or offshore delivery teams.

Responsibilities

  • Perform complex analysis of financial models, market and financial data, and portfolio trends to understand product performance and improve portfolio risk, profitability, forecasting, and operational performance.
  • Use Excel, SAS, SQL, Tableau, and relational databases to provide analytical support for strategies and help ensure goals are met.
  • Evaluate data to assess potential and emerging fraud risks and create mitigation strategies.
  • Propose policy and procedural changes within segmentation structures to produce optimal results.
  • Recommend ways to help the business achieve desired outcomes and make informed decisions using data analysis.
  • Lead coordination of product performance reports and updates for senior management.
  • Analyze data and performance trends to identify opportunities to reduce fraud losses and improve customer experience.
  • Challenge and improve policies, processes, strategies, and controls to balance client experience, risk, and financial impact.
  • Develop and evaluate fraud mitigation strategies, including opportunities, expected benefits, operational impacts, and measures of success.
  • Partner with Claims, Risk, Product, Technology, and Operations to align on strategy and drive execution of initiatives.
  • Own performance reporting and provide regular senior-stakeholder updates on loss trends, initiatives, and results.
  • Identify emerging fraud risks, patterns, and industry trends and translate findings into actionable strategies.
  • Identify opportunities and issues, develop solutions, and drive them through implementation.
  • Measure strategy performance after implementation and use results to identify further opportunities for optimization.
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