Introduction

The alternative lending sector, from peer-to-peer platforms to online microlenders, offers speed and accessibility that traditional banking often lacks. However, this agility also exposes these platforms to unique and complex fraud risks. Unlike traditional institutions with decades of historical data, alternative lenders often operate with limited information, making them prime targets for identity fraud, synthetic fraud, and fraudulent loan stacking. Effective fraud risk mitigation in alternative lending is no longer a luxury but a fundamental requirement for sustainable growth and profitability. This Fraud Fortress: Fraud Risk Mitigation in Alternative Lending training course is a 10-day intensive program designed to equip you with the advanced skills to build a resilient fraud detection and prevention framework.

This course will guide you through the latest strategies and technologies used to combat fraud, from initial application screening to post-disbursement monitoring. You will learn to leverage alternative data sources, implement sophisticated machine learning models, and create a layered defense system that can adapt to new fraud typologies. By the end of this training, you will be able to design and deploy a comprehensive fraud management system, protecting your portfolio from financial losses and safeguarding your platform's reputation in a highly competitive market.

Duration: 10 Days

Target Audience

• Fraud analysts
• Risk management professionals
• Data scientists in fintech
• Underwriting and credit officers
• Alternative lending platform managers
• Compliance officers
• Product managers for lending products
• Financial technology professionals
• Internal auditors
• Operations and legal professionals

Objectives

• Understand the types of fraud in alternative lending.
• Identify the key vulnerabilities in the lending lifecycle.
• Learn to utilize traditional and alternative data sources.
• Master fraud detection techniques, including machine learning.
• Develop a multi-layered fraud prevention strategy.
• Implement robust identity verification processes.
• Understand the role of network analysis in fraud detection.
• Build a framework for continuous fraud monitoring.
• Learn about legal and ethical considerations.
• Create a comprehensive fraud risk mitigation plan.

Course Modules

Module 1: The Alternative Lending Landscape

• An overview of the alternative lending ecosystem
• The key business models and their differences
• The growth drivers and market dynamics
• The role of technology in alternative lending
• Why alternative lending is a target for fraudsters

Module 2: Typologies of Fraud

• Identity theft and synthetic identity fraud
• Application fraud and loan stacking
• First-party vs. third-party fraud
• Collusion and organized fraud rings
• Post-disbursement fraud and scams

Module 3: Foundational Data for Fraud Detection

• The importance of traditional credit data
• Leveraging alternative data sources (e.g., social, mobile)
• The role of digital footprint analysis
• Data from a fraud consortium
• Integrating data from multiple sources

Module 4: Fraud Detection Tools and Techniques

• An introduction to rule-based systems
• The use of machine learning models (e.g., fraud scoring)
• Anomaly detection algorithms
• Real-time vs. batch processing
• The role of link analysis and network graphs

Module 5: Identity Verification and Authentication

• The importance of strong identity verification
• Biometric authentication and its applications
• Knowledge-based authentication (KBA)
• Document verification and digital forgery detection
• Challenges with verifying digital identities

Module 6: Application Fraud Prevention

• The importance of a robust application screening process
• Detecting inconsistencies in application data
• The use of velocity checks and behavioral analytics
• IP address and device fingerprinting
• Building a fraud scorecard

Module 7: Behavioral Analytics

• Analyzing user behavior and patterns
• Identifying deviations from normal activity
• The use of clickstream and navigation data
• Using behavioral data for passive authentication
• Building a behavioral fraud profile

Module 8: Machine Learning for Fraud Detection

• The types of models used (e.g., neural networks, decision trees)
• Feature engineering for fraud detection
• Training and validation of fraud models
• The importance of model explainability
• Continuous model retraining and updating

Module 9: Post-Disbursement Fraud Monitoring

• The need for ongoing portfolio monitoring
• Identifying red flags in repayment behavior
• The use of early warning systems
• Monitoring for loan stacking and hidden relationships
• Triggering alerts for suspicious activity

Module 10: Capstone Project Part 1: Analysis

• Analyzing a provided dataset for fraud patterns
• Identifying key indicators of fraud in a case study
• Developing a hypothesis for a fraud detection model
• Selecting and preparing data for analysis
• Presenting the initial findings

Module 11: Capstone Project Part 2: Model Building

• Building a simple fraud detection model
• Implementing a rule-based system and a machine learning model
• Testing the model's performance on the dataset
• Comparing the accuracy of different approaches
• Documenting the model and its parameters

Module 12: Capstone Project Part 3: Strategy

• Presenting the full fraud mitigation strategy
• Discussing how the model fits into a broader framework
• Proposing a plan for implementation and monitoring
• Q&A and peer feedback session
• Receiving expert recommendations and insights

Module 13: Best Practices and Governance

• Building a fraud risk management framework
• The role of governance and policies
• Fostering a culture of fraud awareness
• Collaborating with law enforcement and industry peers
• The legal and ethical implications of data use

Module 14: Case Studies and Emerging Trends

• An analysis of real-world fraud cases
• The rise of synthetic fraud and countermeasures
• The impact of emerging technologies on fraud (e.g., AI)
• The role of data consortia in collaborative defense
• Future outlook for fraud prevention in fintech

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Training Venue

The training will be held at our Skills for Africa Training Institute Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.

Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Skills for Africa Training Institute certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Terms of Payment: Unless otherwise agreed between the two parties’ payment of the course fee should be done 10 working days before commencement of the training.