As Gen AI gained traction, so did social engineering scams. In some cases, consumers voluntarily handed over payment data as Gen AI enables scams by providing real-time conversation in dozens of languages.
Social engineering scams are transforming the fraud landscape, says a new report by global data and tech company Experian. Nearly 74% of respondents in Experian’s survey believe the biggest fraud threat is social engineering scams. Fraudsters now have technology at their fingertips. Sixty-nine percent of the survey’s respondents agree public access to GenAI is increasing fraud threat.
Not many people realise that fraudsters also look at your social media engagements and posts before targeting you. The report says a huge variety of Authorised Push Payment (APP) fraud and money mule recruitment originates via social media channels. More than half of respondents (56%) struggle to identify if Gen AI has been involved in a fraud attack, and therefore find it difficult to quantify the impact of GenAI on their fraud losses.
About 85% of businesses in India agree that GenAI has permanently altered the fraud landscape, increasing its complexity and sophistication. GenAI has also enabled the ‘industrialisation of fraud,’ where fraudsters create and deploy fake identities, deepfakes, and other fraud tactics on a large scale. It says that to counter this, businesses must adopt advanced AI-driven fraud prevention tools, integrate multiple security solutions, and adopt smarter fraud orchestration strategies—ensuring better detection, stronger security, and reduced costs.
Shail Deep, chief operating officer, Experian EMEA & APAC, said: “As we look ahead, the integration of ML-based fraud prevention is a necessity for businesses to fight back against today’s sophisticated fraud threat. Our research indicates that 85% of fraud experts believe the fraud landscape has been dramatically changed by Generative AI. As a result, over 52% have seen an increase in losses from fraud attacks in the past year.”
For businesses, behavioural biometrics, shared consortium data and integrating AML with fraud decisioning are the top areas for investment in the next year. The report highlights that 48% of Indian businesses face challenges in implementing ML models due to insufficient training data, and 60% report a lack of quality data. Developing effective in-house ML models is complex, thus, customisable off-the-shelf ML models can expedite deployment and value realisation, it adds.