“SINCE the onset of the COVID-19 pandemic early last year, financial institutions have accelerated their digital transformation programmes. Many customers have embraced using online channels for everything from applying for loans and buying goods, to performing international transfers and other high value transactions. This has seen branch visits and ATM transactions reducing considerably over the past 18-months,” says Marcin Nadolny, Head of EMEA Banking & Insurance Fraud at SAS.
Cyber fraud, digital payments fraud, identity theft, and employee embezzlement are all on the increase. In fact, the pandemic has seen the fraud and financial crime landscape shifting to become even more technology-driven than in the past. Cybercrime-as-a-service, digital fingerprints for sale, SIM swapping, social engineering, malicious use of AI, and digital skimming even when cards are not present are just some of the new styles of attacks.
Data and analytics have become key tools to combat the surge in financial-related crimes. AI, and specifically machine learning, can provide financial institutions with automated algorithms that incorporate a cross-channel view of customer behaviour, help to spot complex fraud trends and reduce false positives in parallel. Information about devices, the geolocation of users, and even behavioural biometrics are playing the role of additional fuel for analytics.
Grozdana Maric, Head of CEMEA Fraud & Security Intelligence at SAS, agrees that fraud detection and investigation can be significantly supported by AI and machine learning technologies.
“Fraud risk is escalating for financial institutions and other business. Using the technology and analytics to address all types of fraud becomes an increasing need, allowing for more sophisticated detection and investigation methods, reduced costs, and increased efficiencies,” she says.
“But this does not mean introducing more authentication. Instead, it is about incorporating stronger authentication into the environment. Admittedly, it is becoming more complex to authenticate users without causing delay in the convenience consumers are seeking from digital channels. Things like 3D Secure authentication, one-time passwords, biometric security measures, and tokens can all be considered to increase security without impeding the flow of the customer experience,” says Maric.
Exploring connections and interactions between people to catch more fraud becomes increasingly important in the connected landscape. Through this network analytics driven by AI and machine learning, organisations can better identify suspicious communities, organised crime groups, collusion between employees and customers, and even direct and indirect links to known fraud cases.