Manager - Fraud Intelligence and Analytics.Risk at MTN

Responsibilities

Fraud Pattern Discovery & Modelling

• Analyse financial transaction flows (P2P, merchant payments, cash-in/out, wallet funding, withdrawals) to identify fraud patterns, typologies of unauthorized transfers, mule account networks, and high-velocity transactional attacks and any other relevant abnormal behaviours.
• Translate observed fraud schemes into data-driven patterns, features, and model hypotheses.

Design and build machine-learning models to detect and predict:

• Impersonation and account takeover fraud (SIM swap, social engineering, identity misuse)
• Transaction laundering and agent gaming (commission abuse, float cycling, abnormal cash flows, collusive transactions)
• Cyber-enabled fraud (bots, scripted attacks, app tampering)
• Insider and collusive fraud patterns across users, agents, and merchants
• Perform deep-dive analysis on historical fraud cases to identify emerging patterns and modus operandi
• Continuously refining models based on emerging fraud trends and evolving attacker behaviour.

Advanced Analytics & Machine Learning

Develop supervised and unsupervised models for fraud detection, including:

• Anomaly detection (e.g., Isolation Forest, Autoencoders, LOF)
• Sequence and behaviour-based models for transaction timelines and event chains
• Pattern-mining approaches to uncover recurring fraud structures
• Apply statistical analysis and explainable AI techniques to interpret model outputs and support operational decisions.
• Evaluate model performance using appropriate fraud metrics (precision, recall, false positives, financial impact).

Transaction Data Analysis & Feature Engineering

Engineer predictive features from multi-source financial and behavioral data:

• Transactional features: amount, frequency, velocity, time-of-day patterns, beneficiary history, geographic mismatch flags.
• Device & network intelligence: IMEI/IMSI/ICCID associations, device fingerprinting, VPN/proxy detection, IP reputation, location spoofing signals.
• Behavioral biometrics: typing dynamics, navigation patterns, session timing, interaction cadence.

Risk Engine Integration & Decision Systems

• Design scoring frameworks and decision logic for production risk engines, blending model scores with rule-based signals.
• Define and calibrate risk thresholds, alert triggers, and automated actioning (block, step-up auth, review).
• Implement and optimize real-time fraud controls such as geo-fencing, velocity checks, SIM-swap logic, and session risk scoring.

Qualifications

Education

• Bachelor’s degree in computer science, Data Science, Statistics, Mathematics, or a related field.
• MBA and/or master’s degree is advantageous
• Preferred: Master’s degree in data Analytics, Machine Learning, or Cybersecurity

Experience

• 5 years or more experience in fraud analytics, data science, or risk modeling roles
• At least 3 years’ experience within a non-traditional FinTech /ICT/Digital/Fraud Management environment
• Proven track record of building and deploying ML models for fraud detection

Apply Before 03/18/2026