The world is paying attention to AI. Following President Biden’s executive order and the UK AI Safety Summit, which brought together leaders from around the world, machine learning is the latest hot topic. From technology companies developing their own versions of AI chatbots to large retailers deploying ML to provide shopping recommendations, everyone wants their piece of the pie and will be in the market by 2024. It is expected to increase to 66.62 billion people in 2020. This makes sense given the potential of generative AI. The subset of machine learning is unlimited, and more industries are expected to implement AI into their businesses. As this democratization increases, so do the risks posed by AI. It will become much easier for individuals to attempt fraud using techniques such as deepfakes and advanced algorithms. However, companies are not immune.
In fact, far from it. Generative AI and machine learning can prevent fraud just as easily as a fraudster can use her AI to commit fraud. These models have immense power to learn customer behavior, detect deepfakes, verify critical documents, and more.
Identification
The power of machine learning begins the moment a customer is onboarded. From personal information to biometrics, there are many assets to track during customer interactions. Additionally, as the number of customers increases, it can become difficult to keep up with the influx of data. Problems that machine learning solves. All information is independently verified and compared to known databases to ensure the validity of the information and prevent fake sign-ups. AI will also make it easier to analyze large numbers of her IP addresses and other digital footprints at once, and implement extensive background checks in case scammers slip through. Additionally, companies can use Know Your Customer (KYC) strategies to develop advanced identity verification methods that can verify the identity and risk of potential customers.
CTO and co-founder of Sumsub.
Deepfake detection
As mentioned earlier, there is a dark side to generative AI. Deepfakes have quickly become one of the most accessible and effective ways to spread misinformation, outright lies, or fabricated evidence. In fact, a recent report shows that the number of deepfakes increased by 1740% across industries in North America in 2023 compared to 2022. Fortunately, ML and generative AI can prevent this. Deepfakes are not foolproof and leave clear signs that things are not what they seem. There are certain visual artifacts created by deepfakes that cannot be seen in real media. These include inconsistent facial expressions, distortions, and other unnatural movements. In fact, some artifacts are invisible to the human eye. Machine learning algorithms can identify deepfakes by looking for specific characteristics brought about by their creation process. Similarly, industry-wide collaboration on AI is key to developing models that can detect even the most convincing deepfakes.
Document confirmation
Document fraud is rampant. In 2022, the FTC’s Consumer Surveillance Network received 5.1 million reports, 46% of which were fraudulent. However, generative AI can be trained to analyze commonly forged documents and look for inconsistencies. These models extract qualities that indicate forgery, such as watermarks, stamps, and other obvious signatures. By comparing passports, driver’s licenses, or over 14,000 other forms of identification with reference data, you can uncover instances of fraud and flag forged documents to refuse submission. The majority of official documents are provided by signature, which is a historically common method of fraud. Similar to fraud, machine learning compares signed documents to reference signatures. The algorithm goes far beyond the surface level and analyzes stroke patterns and pressures (characteristics characteristic of authentic signatures) to quickly identify and flag tampering.
transaction monitoring
As e-commerce and other online payment methods become more popular than ever, transaction fraud is bound to occur. Fraudsters who use stolen card numbers to make purchases end up incurring costly chargeback requests that ultimately cost the company. These add up quickly and depending on the deal he can reach over $100 per company, draining valuable time and resources from companies in the process. ML once again comes to the rescue, dramatically increasing the odds of detecting counterfeit attempts and purchases. Machine learning can also be used to combat the recent trend of financial speculation. In money muling, seemingly innocent individuals known as money mules are recruited to transfer illegally obtained funds. Algorithms can process and detect anomalies in individual transactions, customer profiles, and even historical trends. By training on known fraudulent transactions, the model identifies patterns that indicate transaction fraud, financial fraud, and more.
Analyze, track and attack
AI is essentially a machine that constantly performs tasks. Analyze your data, track it against existing information, and flag any cause for concern. Companies can use this to their advantage when dealing with fraud. Scammers and legitimate customers alike often engage in a practice known as promotional abuse fraud, where individuals misuse a company’s promotional materials, such as referral vouchers, to get more than their fair share. Masu. AI stops this by tracking IP addresses, device fingerprints, and user behavioral footprints to prevent multiple accounts from being created all from one place. Even when accounts contain authentic information that is difficult to detect, AI’s ability to check the digital footprint for evidence is unparalleled.
Businesses are not immune to ML and generative AI. This technology is more advanced than ever and could be an extremely useful tool to combat the significant increase in fraud that businesses are facing. By utilizing the above techniques, businesses can be confident that they are on the front lines of protection from fraudsters. As the saying goes, if you can’t beat them…you know the rest.
We’ve featured the best encryption software.
This article is produced as part of TechRadarPro’s Expert Insights channel, featuring some of the brightest minds in technology today. The views expressed here are those of the author and not necessarily those of his TechRadarPro or Future plc. If you’re interested in contributing, find out more here. https://www.techradar.com/news/submit-your-story-to-techradar-pro