Learn from Sumsubers: What strategies can I employ to combat AI-generated scams?
In today's digital age, fraudsters are constantly evolving their tactics, and AI-generated fraud has become a significant threat across politics, entertainment, and businesses. To combat this, businesses are turning to AI and machine learning (ML) to proactively prevent fraud and adapt to new trends.
This week, Sumsub, a leading provider of identity verification and fraud prevention solutions, is launching a bi-weekly Q&A series to address frequently asked questions about regulatory compliance, verification, and automated solutions. The series aims to provide valuable insights into the latest strategies for fighting AI-generated fraud.
The Q&A session will feature the Head of Partnership at Sumsub, Thomas Taraniuk, who will share his expertise on the topic. Questions can be submitted to Sumsub's Instagram and LinkedIn accounts before the event.
So, how can businesses effectively use AI and ML to prevent AI-generated fraud?
Firstly, real-time transaction monitoring with ML models that analyze transactional patterns and anomalies is crucial. These models, trained on historical labeled data, can detect anomalies and flag suspicious behaviour in real time, preventing fraudulent transactions or account takeovers.
Secondly, data orchestration and cross-verification play a vital role. By combining data from multiple vendors, such as identity verification services, credit bureaus, and social media, businesses can identify inconsistencies and enhance fraud detection beyond any single source's limits.
Thirdly, AI-driven biometric and behavioural authentication provides dynamic protection against credential theft and account takeover. By analysing biometrics and user behaviour, businesses can replace vulnerable static passwords with more secure authentication methods.
Fourthly, trust scoring and customer profiling help assess transaction risk and detect fraud attempts before processing. Building customer profiles and assigning trust scores based on historical data can help businesses identify high-risk transactions and take appropriate action.
Lastly, incorporating feedback loops is essential for improving detection accuracy over time. Feeding data from chargebacks, customer complaints, and declined transactions back into AI algorithms helps them learn and adapt, improving their ability to detect fraud.
A layered multi-defense approach, often referred to as the "Swiss Cheese Model," is also crucial. Stacking multiple fraud prevention mechanisms reduces the chances that fraud attempts evade all protections, increasing overall system resilience.
Industries such as SaaS, insurance, financial services, and travel have successfully deployed these strategies to scale fraud prevention and reduce losses associated with AI-assisted fraud. Financial institutions, in particular, emphasise ML-based anomaly detection combined with integrated AML/KYC data for holistic fraud management.
For those interested in learning more about Sumsub's Fraud Prevention Solution, more information can be found here. Join us for this week's Q&A to gain insights into the latest strategies for fighting AI-generated fraud and ask your questions directly to our expert, Thomas Taraniuk. The Q&A session will be posted on The Sumsuber and social media every other Thursday.
Businesses can leverage AI and ML to fortify their cybersecurity and financial frameworks by implementing strategies such as real-time transaction monitoring with ML models, data orchestration and cross-verification, AI-driven biometric and behavioral authentication, trust scoring and customer profiling, and feedback loops. Additionally, adopting a layered multi-defense approach ("Swiss Cheese Model") increases overall system resilience against AI-generated fraud. This approach has proven effective in various industries, particularly financial services, emphasizing ML-based anomaly detection and integrated AML/KYC data for comprehensive fraud management.
