Fraud Risks in Lending: Detection and Prevention

Introduction

Early this year, we hosted an insightful session at our Sydney office on fraud and corruption in the banking and finance space. The session brought together a diverse group of professionals from the legal, financial and accounting sectors to explore the challenges and evolving landscape of organisational fraud and corruption within the banking and finance industry.

As we know, fraud is becoming more sophisticated and harder to detect. From synthetic identities to the use of artificial intelligence (AI), the methods used by fraudsters are evolving in both complexity and scale. This article explores the most common types of fraud affecting the banking and financial sector, the impact of emerging technologies like AI and key considerations for lenders. We also share proactive strategies to identify and reduce fraud risk.

A closer look at lending fraud

Fraud in the banking and finance sector occurs in many forms. Below are the most common types of fraud faced by lenders:

Fraud tactics are evolving

The rapid rise of AI has increased the volume and sophistication of fraud and also the capabilities of fraudsters. Whilst fraudsters continue to perpetuate fraud using traditional techniques, the following are examples that are shaping today’s lending‑fraud landscape:

1.  Generative AI for forged loan documents: Fraudsters can now use AI to create highly realistic fake pay slips, IDs and income statements making it difficult to detect forgery;

2.  Voice‑cloning & real‑time impersonation: Fraudsters are now using voice-cloning programs to impersonate an individual enabling them to deceive voice-verification systems during loan applications or customer service calls;

3.  Rise in fraud-as-a-service business models; Sophisticated fraudsters are providing the necessary tools and services making it easier for less experienced fraudsters to commit fraud.

Detection and prevention

As fraud schemes continue to become more advanced with the use of AI, it is important to implement a combination of AI tools and robust manual procedures.

Implementing a fraud risk management framework

Establish a dedicated fraud risk framework that defines governance, roles and responsibilities, detection processes, escalation protocols, and incident response plans.

Implementing policies and staff training

It is important to establish policies and conduct regular staff training on fraud red flags, emerging types of fraud and when to escalate suspicious activity. Practical training helps staff develop intuition for identifying inconsistencies or questionable borrower behaviour for example:

·         mismatched or inconsistent documentation;

·         individuals unwilling to provide original documents;

·         unusual transaction patterns or rushed settlement requests; and

·         unusual broker behaviour or repeated dealings with known fraud risks.

Implementing strong verification and compliance measures

1. Know Your Customer (KYC): Use multi-factor authentication and biometric verification alongside traditional ID checks.

2. Verification of Identity (VOI): Conduct rigorous in-person or digital ID verification supported by document certification and audit trails.

3. Digital Signatures: Use validated and encrypted e-signature systems to prevent tampering or forgery of loan documentation.

4. AML/CTF Alignment: Ensure compliance with anti-money laundering (AML) and counter-terrorism financing (CTF) obligations and implement responsible lending practices

Implement AI tools for fraud-detection

Use machine learning tools to detect anomalies, flag potential fraud patterns, and assess borrower risk. When AI is integrated with human oversight, these tools can improve fraud detection and the likelihood of fraud being committed.

Impact of fraud

Fraud not only threatens financial loss it also poses serious legal consequences. A critical issue for lenders is the principle of indefeasibility of title which is the legal protection that allows a mortgagee to retain their interest in a property, even if fraud has occurred.

Legal safeguards such as requiring independent legal advice certificates from individuals and documenting the verification process can provide evidence of due diligence if a fraud-related dispute arises. To maximise the chances of recovery, it is important to:

·         Act quickly on fraud detection

·         Refer cases promptly to law enforcement

·         Cooperate with other banks to track illicit transfers

As fraud techniques evolve and the more AI tools become available to fraudster, the banking and finance sector faces a growing need for awareness. AI has created both new vulnerabilities and new opportunities to enhance detection. By implementing strong governance, adopting AI fraud prevention tools and training staff, lenders can better protect themselves and their customers from the threat of fraud.

As always, get in touch with any member of the fraud & corruption team at Bartier Perry if you would like more information.

Authors: Adam Cutri, Rezwan Attai & Karunya Vetcha

This publication is intended as a source of information only. No reader should act on any matter without first obtaining professional advice.