Leveraging generative AI to detect and prevent impersonation scams within the banking sector

Impersonation scams, also known as imposter scams, represent one of the most prevalent and effective strategies employed by fraudsters to deceive their victims. These scams often involve the perpetrator posing as a trusted individual or organization, such as bank representatives, law enforcement officers, or even family members, in order to manipulate victims into disclosing sensitive information, transferring funds thru banks, or granting access to their personal devices.

Key statistics indicate that impersonation scams are negatively influencing banking industry

• In 2023, Australians made over 601,000+ scam reports compared to the 507,000 in 2022 (an 18.5% increase in reports) with estimated total combined losses for A$ 2.74 billion as per report shared by Australian Competition and Consumer Commission (ACCC) on 1 July 2023.
• In 2023, there were over 856,000 reported cases. Losses from business and government impersonation scams, in particular, rose significantly, reaching $1.1 billion—more than three times the amount reported by consumers in 2020.
• In 2023, the median loss from government impersonators in the US was $1,400. Scammers posing as US Customs and Border Protection inflicted the highest average loss, at $4,200.  
• In the UK, the average loss per victim is accounted to £7,448 due to impersonation scams in 2023.
• Over last 3 years, financial losses due to impersonation scams in Australia have drastically gone up from A$851 m (2020) to A1.8 b (2021) to A$3.1 b (2022) which is slightly reduced to A$ 2.74 billion in 2023.

The rise of impersonation fraud presents a considerable challenge for banking industry

With bank transfers accounted for about 40% of reported losses linked to US government and business impersonators, Impersonation scams significantly impacts banks directly or indirectly. The impact of imposter scams, which often cause serious financial losses, reputation damage, increased operational cost, regulatory and compliance issues and impact on customer relationship and trust.

Growing number of impersonation scams attracted the attention of regulators world over, who are demanding that banks should compensate the victims in banks related impersonation scams. In United Kingdom, the compensation rate for victims is highest amongst all the scam categories. European Union is also taking a similar step, requiring mandatory compensation for victims of bank impersonation scams that involve spoofing in upcoming PSD3 regulation.

To avoid high compensation costs, banks must strengthen their fraud detection and prevention solutions and systems to early detect and prevent impersonation schemes.  

How generative AI can be leverage the detection and prevention of impersonation scams ?

Generative AI and machine learning technologies can significantly contribute to the ability of banks to identify, prevent, and reduce impersonation scams. Banks are actively exploring the implementation of technological solutions developed with Generative AI and machine learning models to enhance their comprehensive fraud detection systems. This approach is considered a crucial strategy in addressing impersonation scams, alongside other potential measures to combat fraud and scams.

Banking industry can leverage generative AI and machine learning models in various ways to detect and mitigate impersonation scams, thereby staying ahead of scammers and fraudsters.

• Real-Time Fraud Detection - Generative AI enables banks and Internet Service Providers to swiftly detect and eliminate impersonation websites. In the year 2023, AI technologies contributed to a 30% decrease in fraud attempts, thereby averting numerous instances of financial loss.
• Swift Support - AI-driven systems allow banks to swiftly deactivate compromised cards and provide replacements. This prompt action is essential, as it can significantly decrease the average time required to resolve a fraud case from several days to mere minutes. 
• Detecting Document Fraud - Generative AI can help in detecting fake documents that are often challenging to identify using traditional manual ways or using machine learning models for document frauds such fake bank statements, government documents etc. 
• Advanced Customer Authentication – Generative AI has the potential to enhance sophisticated customer authentication methods, including behavioral biometrics, as well as voice and facial recognition systems. 
• Enhanced Customer Education - Artificial intelligence has the capability to provide tailored notifications and alerts, as well as educational materials, assisting customers in identifying and steering clear of scams. Financial institutions have noted a 40% rise in customer participation in educational initiatives driven by AI. 
• Pattern Simulations - By generating synthetic samples that resemble real-life cases, Gen AI boosts the attention signal for core detection tools. This approach adds robustness to the deception model, enabling it to detect not only patterns but also similar attacks that could be missed using traditional methods​​​​.
• Enhance Collaboration – Collaborating with various government agencies, cybercrime prevention departments, and telecommunications providers through the use of artificial intelligence can enhance fraud detection by facilitating the exchange of threat intelligence. In 2023, this collaborative effort resulted in a 25% rise in successful scam detections in Australia. 
• Enhanced Fraud Prevention Models – Generative AI and machine learning models can assist in simulating possible scam scenarios to anticipate and prevent impersonation scams. Additionally, the AI model can continue to develop by examining scam strategies, thereby enabling the bank to remain one step ahead of fraudsters.

Conclusion

A singular solution is insufficient to effectively combat impersonation scams. A comprehensive strategy that integrates the enhancement and fortification of banks' fraud detection and prevention systems with advanced technology, the education of both employees and consumers, and the promotion of collaboration among banks, government agencies, and telecommunications service providers is essential to significantly reduce the incidence of impersonation scams.

Banks must prioritize investments in the creation and implementation of a comprehensive array of machine learning models, generative AI technologies, and behavioral intelligence solutions, ensuring their seamless integration with existing fraud detection systems. This strategy aims to enhance data synthesis, improve the effectiveness of fraud detection, reduce false positive rates, and increase the ability to identify suspicious activities. These technological advancements will facilitate the swift identification and resolution of impersonation attacks, thereby mitigating financial and reputational losses for banks and, most importantly, safeguarding consumer confidence.