Senior AI/ML Fraud Specialist

To lead the integration of AI/ML technologies into telecom fraud management by combining deep fraud risk expertise with advanced analytics. The role will design, implement, and oversee next-generation fraud detection frameworks, ensuring protection against evolving fraud risks while safeguarding revenue and customer trust.

Context / Background

Fraud Management is a critical control function within telecom that mitigates revenue loss, protects brand image, and safeguards customer experience by ensuring:

• Fraud is minimized across all revenue streams, with effective prevention controls.

• Regulatory obligations and stakeholder expectations are consistently met.

• Traditional fraud management methods are enhanced with AI/ML-driven models to proactively detect fraud, adapt to emerging threats, and maintain compliance with industry standards.

Role Accountabilities

Overview

• Lead design and deployment of AI/ML-based fraud detection models for telecom fraud scenarios (e.g., SIM box, subscription fraud, roaming fraud, bypass fraud, OTT fraud).

• Drive innovation in fraud strategy through predictive analytics, anomaly detection, and automation.

• Configure, manage, and optimize Fraud Management Systems (Subex, Mobileum, WeDo, or similar).

• Build, train, and maintain ML models for anomaly detection, predictive scoring, and automated alerts.

• Collaborate with IT, Data Warehouse, and Business stakeholders to integrate AI/ML into fraud monitoring tools.

• Mentor fraud analysts in advanced AI techniques and fraud risk management.

• Ensure regulatory compliance and industry best practices in fraud detection.

• Conduct shift-based monitoring (24x7x365) of fraud alerts and system activity.

• Evaluate new products/services for fraud risks prior to launch.

• Monitor audit logs and ensure effective audit trails on provisioning platforms.

• Distinguish fraud from bad debt and fine-tune Fraud Management Systems for accuracy.

• Analyze anomalies, identify root causes, and recommend resolutions.

• Develop preventive strategies and controls for emerging fraud risks.

• Provide fraud risk guidance across Billing, Credit Control, Sales, Customer Service, and Network functions.

Business Impact

• Protect company revenue by reducing fraud-related losses.

• Build scalable AI/ML frameworks to future-proof fraud management.

• Enhance customer trust by minimizing fraud-related service disruptions.

Costs & Profitability

• Costs borne by Fraud Management Department under Group Finance.

Problem Solving

• Detect emerging and complex fraud schemes using AI/ML.

• Translate fraud risks into technical AI/ML solutions.

• Independently resolve fraud detection/prevention issues with strict timelines.

• Address both technical and business aspects of fraud control.

Planning & Organizing

• Manage AI/ML model lifecycle: design, training, validation, deployment.

• Align fraud prevention strategies with IT, Data Warehouse, and Risk teams.

• Ensure timely fraud detection and closure of fraud issues without compromising quality.

• Use risk-based prioritization for new fraud threats.

Key Relationships & Decision Making

Teamwork & Leadership

• Mentor fraud analysts and act as Subject Matter Expert (SME) in AI-driven telecom fraud.

• Collaborate with OSS, BSS, IT, Marketing, Sales, Customer Service, Finance, OG, and RAFM functions.

Communication & Influence

• Develop strong cross-functional relationships to achieve departmental objectives.

• Negotiate deadlines and align fraud prevention activities across departments.

Decision Making

• Lead design and deployment of AI/ML fraud solutions.

• Influence investment in fraud management tools and AI platforms.

• Make operational decisions in fraud detection; escalate strategic issues to management.

Key Performance Indicators (KPI)

• Identification of fraud risks and design of effective controls.

• Reduction in fraud-related revenue leakage.

• Accuracy, efficiency, and timeliness of AI/ML fraud detection models.

• Speed of detecting new fraud patterns.

• Cost savings through automation and optimized processes.

• Compliance with regulatory and audit standards.

• Timeliness and quality of fraud-related reporting.

Experience, Qualifications & Skills

Minimum Experience & Knowledge

• 10+ years in telecom fraud management covering multiple fraud types.

• Hands-on experience with Fraud Management Systems (Subex, Mobileum, WeDo, etc.).

• Deep knowledge of telecom technologies (GSM, LTE, 5G, roaming, interconnect, billing, mediation).

• Proven expertise in AI/ML (supervised/unsupervised learning, anomaly detection, predictive modeling).

Minimum Entry Qualifications

• Bachelor’s degree in Telecommunications, Computer Science, Data Science, or related field.

• Advanced certifications preferred (AI/ML, CFE, CFCA, Data Science, or Fraud Risk Management).

Technical Skills

• AI/ML tools: Python, R, SQL, TensorFlow, PyTorch.

• Big Data platforms: Hadoop, Spark, Databricks.

• Cloud AI platforms: AWS, Azure, GCP.

• Telecom fraud systems: Subex ROC, Mobileum RAID, WeDo RAID.

• Strong understanding of telecom OSS/BSS, IN nodes, provisioning, mediation, and billing systems.

Non-Technical Skills

• Strong interpersonal and communication skills.

• Strategic thinking and innovation mindset.

• Leadership and mentoring capabilities.