Senior Fraud Analyst

Role - Associate Sr Manager

JOB PURPOSE

The Fraud Risk Manager contributes as a Fraud SME and will support data, ML models and Gen AI technology and reporting analysts of Investigation & Loss Mitigation, as well as operate as an individual contributor on strategic initiatives. The person in this role will maintain effectiveness of investigation tools and technologies, data processes and reporting that delivers robust and timely data insights to Fraud Policy, Fraud Analytics, Fraud Digital and Risk Mitigation. The successful candidate thrives in a fast-paced, high-performance team environment were creativity and common-sense result in great solutions. This person should be passionate on Fraud - self-motivated, highly driven, quick and proactive thinker who has the passion needed to respectfully challenge the status quo and leverage global best practices.

Hands-on experience in Analytics and Data Science.

Model Development and exposure to Fraud detection.

Good Understanding of LLMs and other new techniques

Data deep dive, Insight generation, Dashboard reporting and presentation skill

End-to-end project delivery and stakeholder management.

1. PRINCIPAL ACCOUNTABILITIES

Data Analytics & Business Insights

Identify valuable data sources and automate collection processes.

Analyze large amounts of information to discover trends and patterns.

Combine models through ensemble modeling.

Present information using data visualization techniques.

Propose solutions and strategies to business challenges.

Collaborate with engineering and product development teams.

Create and enforce policies for effective data management.

Assist with reports and data extraction when needed.

Formulate techniques for quality data collection to ensure adequacy, accuracy and legitimacy of data.

Identify the troubleshoot data-related problems and work for the modifications to rectify the same.

2. System Validations

Monitoring fraud trends, investigating emerging threats and quickly developing and deploying new strategies to manage those threats.

Build predictive models and machine-learning algorithms.

Analyze structural requirements for new software and applications.

Optimize new and current database systems.

Monitor and analyze information and data systems (Indicator Management, Loss ratio pattern) and evaluate their performance to discover ways of enhancing them (new technologies, upgrades etc.)

Review of existing control environment and assess effectiveness against known fraud schemes. Identify control gaps and create insightful analysis to support the implementation of new controls.

Provide insight as Subject Matter Expert (SME) on Process and control fraud leakages.

Develop complex technical rules / scenarios to maximize the effectiveness of fraud detection and prevention programs. Analyze fraud detection alerts and assess need for further investigation.

Research / Survey activity regarding Fraud and Risk

Analytics related to claims / Dashboard reporting / Presentation Department / Content writing / Framework preparation.

Present information using data visualization techniques.

Manage and optimize processes for data intake, validation, mining and engineering as well as modelling, visualization and communication deliverables.

People Management

Work collaboratively with a diverse team of operational agents, fraud risk manager, data analysts / scientists, business insight analysts, subject matter experts

Support to Team members to Locate and define new process improvement opportunities.

Build, develop and maintain data models, reporting systems, data automation systems, dashboards and performance metrics support that support key decisions.

Oversee the design and delivery of reports and insights that analyze business functions and key operations and performance metrics.

Educational Qualifications

M.Sc in Statistics, Economics or Engineering degree or Professional education in Data Science

Good Knowledge of Python, R, SQL, MS Excel, and SQL database & coding

At least a bachelor’s degree in mathematics, Computer Science, Engineering, Economics, Statistics