Business Operations Analyst II - Fraud

Conduent

19h•Hybrid

About The Position

The Business Operations Analyst II – Fraud plays a critical role in protecting the organization by transforming transactional data into actionable insights that prevent, detect, and mitigate fraud risk. In this role, you will analyze complex transaction data to identify varying fraud attack vectors, understand underlying vulnerabilities, and drive effective mitigation strategies in a collaborative, team-based environment. You will partner closely with various teams to monitor fraud strategy deployments, track evolving trends, and evaluate the effectiveness of fraud detection tools and controls. The ideal candidate combines strong analytical skills with a deep understanding of fraud patterns, a proactive mindset, and a passion for continuous improvement in a fast-paced, risk-focused environment.

Requirements

Bachelor’s degree in Business Administration, Mathematics, Computer Science, Economics, Statistics, or a related field and one (1) year of relevant experience; or equivalent professional experience with a minimum of three (3) years in a related role.
Advanced proficiency in SAS and SQL, with the ability and willingness to learn additional analytical tools and technologies.
Demonstrated experience designing and developing interactive dashboards, reports, and visualizations using tools such as Power BI or Tableau to effectively communicate insights in a clear, visually compelling manner.
Proven experience evaluating, analyzing, and visualizing large, complex, or disparate datasets.
Maintains a high level of attention to detail to identify anomalies, discrepancies, and potential fraud indicators within transactional data.
Experience in a reporting or business analyst role, supporting leadership teams through metrics, data analysis, and reporting.
Strong data management skills using tools such as Excel and SQL.
Demonstrated experience in fraud detection, prevention, or analysis within a dynamic or fast-paced business environment.
Ability to identify and define data analytics requirements and structure data from multiple source systems to create, maintain, and enhance reporting solutions.
Strong analytical skill set, including data mining, evaluation, interpretation, and visualization.
Experience with technical writing, including development of queries, reports, documentation, and presentations.
Proven ability to present and explain data, findings, and metrics to non-technical audiences.
Excellent written and verbal communication skills.

Nice To Haves

Familiarity with analytics platforms that incorporate machine learning capabilities.
Have a master’s degree, CFE or other recognized fraud certification.
Experience with AI technologies.

Responsibilities

Extract, integrate, and organize data from multiple disparate systems to deliver clear, actionable views of operational performance.
Design and develop comprehensive operational dashboards aligned with business objectives, providing insights and recommendations.
Evaluate service levels, staffing models, volumes, policies, procedures, financial performance, audit and compliance requirements, quality metrics, and urgent operational issues; recommend process improvements as needed.
Build and maintain strong working knowledge of the organization’s structure, operational processes, and applicable policies, regulations, and governance requirements.
Proactively analyze data to answer key questions from stakeholders or out of self-initiated curiosity with an eye for what drives business performance.
Identify preventative measures by detecting variances, trends, inconsistencies, and potentially suspicious activity through data analysis.
Partner with stakeholders to understand requirements, gather data from diverse sources, and perform business analysis to deliver quantitative insights via reports, dashboards, and presentations.
Analyze and synthesize qualitative and quantitative data to create compelling narratives and provide strategic recommendations on complex issues.
Translate analytical findings into actionable outcomes, including enhancements to predictive models, operational processes, or policy decisions.