palam delhi fraud analyst Jobs – 139 Open Jobs Today

Senior Manager
EXL Service —Gurugram, Haryana
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Anaplan —Gurugram, Haryana
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Netrika, Consulting and Investigations —Gurugram, Haryana
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Sr. Analyst - Enterprise Risk Advisory 5B
Genpact Enterprise Risk Consulting LLP —Gurugram, Haryana
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Real Living Estates —Delhi, Delhi
Reconcile bank accounts and company accounts. Manage utility bills and tenant chargebacks. The candidate will work remotely from India and support our property…
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Assistant Vice President-Risk Management-Fraud Prevention & Detection
EXL Service —Gurugram, Haryana
Develop and continuously evolve knowledge assets including capability decks, frameworks, case studies etc. to strengthen the fraud offering.

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Senior Manager

EXL Service -
Gurugram, Haryana

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Job details

Full-time
8 days ago

Qualifications

Azure
Databases
SQL
AWS
Analysis skills
Machine learning
Distributed systems
Neo4j
Fraud prevention and detection
Communication skills
Banking
Graph databases
Python
Analytics

Full job description

Job Description: Manage risk analytics projects and ensure alignment with business goals. Ensure adherence to risk analytics policies and procedures. Provide strategic guidance on risk analytics practices. Lead a team of risk analysts. Build, Validate and Monitor Fraud Models.

Responsibilities: Manage risk analytics projeBuild predictive models for real-time fraud detection systems.

Evaluate and optimize existing ML models for performance, scalability, and explainability.
Apply deep learning and advanced analytics for behavior analysis and risk profiling.
Analyze transaction data to identify patterns, anomalies, and fraud trends.
Perform exploratory data analysis (EDA) to understand customer behavior and potential fraud vulnerabilities.
Translate data-driven insights into actionable recommendations for leadership and stakeholders.
Collaborate with fraud strategy and operations teams to enhance fraud prevention frameworks.
Validate and monitor predictive models for real-time fraud detection systems using valid monitoring metrics/KPI’s.
Prepare technical documents related to fraud models and model validation. Validate the models using various techniques and KPIs, out of time validation etc.
Set up monthly/quarterly and annual monitoring for the models using valid monitoring metrics/KPI’s.
Perform Root Cause Analysis in case of deterioration of model performance/data issues.
Evaluate and optimize existing ML models for performance, scalability, and explainability.
Apply deep learning and advanced analytics for behavior analysis and risk profiling as part of
Analyze transaction data to identify patterns, anomalies, and fraud trends.
Perform exploratory data analysis (EDA) to understand customer behavior and potential fraud.
Translate data-driven insights into actionable recommendations for leadership and stakeholders.
Collaborate with other teams like fraud strategy to enhance fraud prevention frameworks.cts. Ensure adherence to policies. Provide strategic guidance. Lead a team.
Hands on experience in Graph Knowledge Database / Graph Neural Networks based solutions.
Handle the requests coming from client on Graph Knowledge Databases
Provide solutions and mentor the team working with her/him on the graph solutioning

Qualifications: 3-5+ years of experience in analytics preferably in Banking and Financial Services
A minimum of 3 years of hands-on experience working on monitoring and validation of Machine Learning models to solve analytical use cases.
Solid understanding of banking products, fraud types (e.g., account takeover, synthetic fraud, identity theft), and transaction systems.
Knowledge of various statistical techniques used in analytics (regression, ML Models, Monitoring Metrics like KS, PSI, CSI, MAPE, Confusion Metrics etc.)
Excellent problem-solving and analytical skills, with the ability to work on complex projects and deliver high-quality results.
Proficiency in programming languages such as Python, and experience with ML related libraries.
Experience with large-scale data processing and distributed computing frameworks is a plus.
Strong communication skills, both written and verbal, with the ability to convey complex ideas to diverse stakeholders.
At least 3 to 4 years of experience in Machine Learning and have hands on experience in Graph Knowledge Database / Graph Neural Networks based solutions.
Hands-on experience in Python, and SQL. Good Knowledge on Classic ML algorithms.
Working in any Fintech/Payments/Banking environment in fraud domain.
Candidate should have worked priorly on at least one solution like AWS Neptune, Neo4J, Azure Cosmos Graph DB or any graph database.
Experience in Snowpark/Pyspark is a plus.

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