# AI-Powered Risk and Fraud Detection

> Use machine learning and analytics to detect fraud and anomalies earlier, and run a data-driven fraud risk programme.

- **Provider:** Afriskora Training Solutions
- **Category:** Governance, Risk & Compliance
- **Duration:** 3 days
- **Formats:** classroom (cities across Africa), live virtual (Zoom/Teams), in-house for teams
- **Certificate:** Afriskora Certificate of Completion, verifiable online
- **Booking:** request dates and a quote; invoice and EFT payment
- **Web page:** https://afriskora.co.za/courses/governance-risk-compliance/ai-powered-risk-and-fraud-detection
- **Request dates:** https://afriskora.co.za/request-training?course=ai-powered-risk-and-fraud-detection

## Overview

Fraudsters use technology, so detection must too. This course shows risk, audit and fraud professionals how analytics and machine learning detect anomalies in payments, procurement, payroll and claims, how to build fraud detection rules and models, manage alerts and false positives, and combine technology with investigation skills. It also covers AI-enabled fraud such as deepfakes and synthetic identities.

## Who should attend

- Fraud and forensic investigators
- Internal auditors and risk managers
- Compliance and AML officers
- Finance and payments managers

## Learning outcomes

- Identify fraud schemes that analytics can detect
- Apply rules, anomaly detection and machine learning
- Manage alerts, thresholds and false positives
- Recognise AI-enabled fraud and deepfakes
- Build a data-driven fraud risk programme

## Course outline

### Module 1: Fraud analytics foundations

- Fraud risk assessment
- Data sources
- Red-flag rules
- Benford's law and duplicate testing

### Module 2: Machine learning for fraud

- Anomaly detection
- Supervised fraud models
- Network and link analysis
- Alert management

### Module 3: New threats and programme

- Deepfakes and synthetic identities
- Cybersecurity basics for fraud teams
- Investigation workflow
- Fraud programme roadmap