Vice President - Fraud Business Analytics, Citi

Citi

Location:
India, Bengaluru

Category:
Finance

Contract Type:
Employment contract

Salary:

Not provided

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Job Description:

We are seeking a highly skilled and analytically driven Business Analytics professional specializing in the Fraud domain. This role involves building predictive models, segmenting customer behaviors and transactions, and providing actionable insights to significantly reduce fraud losses while minimizing impact on legitimate customers. The role requires strong expertise in statistical modeling and machine learning.

Job Responsibility:

Design, develop, validate, and deploy statistical models, with a strong focus on Logistic Regression and Decision Tree algorithms, to identify and predict fraudulent activities across various financial products and channels
apply advanced segmentation techniques (e.g., clustering, rule-based segmentation) to stratify customer populations and transaction behaviors, enabling more precise fraud risk assessment and targeted prevention strategies
translate model outputs and analytical insights into actionable fraud rules and strategies within fraud detection systems, continuously monitoring their performance and tuning for optimal balance between fraud capture and false positive rates
establish robust monitoring frameworks for all deployed models and strategies, regularly assessing their effectiveness, recalibrating models, and performing A/B testing to drive continuous improvement
proactively analyze large and complex datasets to identify new fraud patterns, emerging threats, and vulnerabilities, providing rapid analytical responses to evolving fraud typologies
conduct extensive data exploration and feature engineering to identify powerful variables and attributes that enhance the predictive power of fraud models
partner closely with Fraud Operations, Risk Management, Product, Technology, and Data Engineering teams to understand business requirements, integrate analytical solutions, and ensure data quality and availability
prepare and present clear, concise, and compelling reports, dashboards, and presentations on model performance, fraud trends, and strategic recommendations to both technical and non-technical stakeholders, including senior leadership.

Requirements:

15+ years of experience in a fraud analytics, business/risk analytics, or similar quantitative role, with a proven track record of applying statistical modeling techniques in financial services
demonstrated hands-on experience developing and deploying predictive models using Logistic Regression and Decision Tree algorithms
strong expertise in various segmentation methodologies and their application in risk or fraud analytics
proficiency in statistical programming languages such as Python (with libraries like scikit-learn, Pandas, NumPy, SciPy) or R for data manipulation, statistical analysis, and model building
experience with other machine learning algorithms relevant to fraud detection (e.g., Random Forests, Gradient Boosting, SVMs, clustering)
familiarity with big data technologies (e.g., Spark, Hadoop) and cloud platforms (AWS, Azure, GCP)
prior experience in the financial services, e-commerce, or payments industry with specific knowledge of fraud typologies (e.g., card fraud, account takeover, application fraud, synthetic identity)
experience working with fraud detection platforms and rule engines
advanced SQL skills for complex data extraction and analysis from large relational and non-relational databases
experience with data visualization tools (e.g., Tableau, Power BI, Qlik Sense) to create actionable dashboards and reports
solid understanding of model validation techniques, performance metrics (e.g., AUC, Gini, Precision-Recall), and their interpretation
strong analytical, problem-solving, and critical thinking skills, with an ability to work with ambiguous data and derive meaningful insights
excellent communication and presentation skills, capable of explaining complex analytical concepts to diverse audiences
experience analyzing large datasets
applying mathematical, statistical and quantitative analysis techniques to perform complex analyses and data mining.

Nice to have:

Experience with other machine learning algorithms relevant to fraud detection (e.g., Random Forests, Gradient Boosting, SVMs, clustering)
familiarity with big data technologies (e.g., Spark, Hadoop) and cloud platforms (AWS, Azure, GCP)
experience with data visualization tools (e.g., Tableau, Power BI, Qlik Sense)
prior experience in the financial services, e-commerce, or payments industry with specific knowledge of fraud typologies (e.g., card fraud, account takeover, application fraud, synthetic identity).

What we offer:

Programs and services for your physical and mental well-being including access to telehealth options, health advocates, and confidential counseling
access to learning and development resources to broaden and deepen skills and knowledge
generous paid time off packages
resources to manage financial well-being and plan for the future.

Additional Information:

Job Posted:
September 11, 2025

Employment Type:

Fulltime

Work Type:

Hybrid work

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Vice President - Fraud Business Analytics