ARGO’s Post

Learn how to build a #MachineLearning model that will help you classify whether credit card payments are fraudulent. #AI #NeuralNetworks

Leveraging AI And Machine Learning To Detect And Prevent Fraud Leveraging AI And Machine Learning To Detect And Prevent Fraud Fraud poses a significant threat across various sectors, causing financial losses and damaging reputations. With the rapid advancement of technology, artificial intelligence (AI) and machine learning (ML) have emerged as powerful tools to combat fraud. These technologies offer innovative solutions that help detect and prevent fraudulent activities efficiently and effectively. https://lnkd.in/gZ5Ct-gK

AI Mistakes Are Very Different Than Human Mistakes . We need new security systems designed to deal with their weirdness by Bruce Schneier, Nathan E. Sanders https://lnkd.in/dGjesz7x ... It’s not the frequency or severity of AI systems’ mistakes that differentiates them from human mistakes. It’s their weirdness. AI systems do not make mistakes in the same ways that humans do.

In the world of AI and machine learning, 'Precision' and 'Recall' are critical metrics to understand, and to help you balance the trade-offs between accuracy and completeness of AI-powered solutions. 'Precision' measures the accuracy of the positive predictions made by the model. 'Recall', aka sensitivity, measures the ability of a model to find all the relevant cases within a dataset. Optimizing one often comes at the expense of the other. Therefore, a tailored approach is needed depending on your specific use case and the consequences of high (↑) precision and low (↓) recall, and vice versa. High (↑) precision and (↑) high recall is most desired, though often unlikely to achieve. The infographic below is my best representation of 'Precision' vs. 'Recall' debate, in the context of fraud prevention. Some businesses will prefer 'high precision' while others will focus on achieving 'high recall'. Enjoy the read! #ai #machinelearning #statistics #classification #fraudprevention #falsepositives #howaiworks

As I delve deeper into the study of AI and it's positive effects, I also have come across its negative impacts. One such impact is Adversarial Machine Learning - the malicious manipulation of AI systems. Attackers can introduce subtle changes to input data or the model itself to deceive the AI, causing it to make incorrect or unintended decisions. Adversarial AI has become a growing concern as AI systems are increasingly integrated into critical applications. Researchers and developers are working to develop techniques to protect AI systems from these attacks. To learn more:

AI Mistakes Are Very Different Than Human Mistakes We need new security systems designed to deal with their weirdness. Much of the friction—and risk—associated with our use of AI arise from that difference. Researchers are still struggling to understand where LLM mistakes diverge from human ones. Some of the weirdness of AI is actually more human-like than it first appears. Small changes to a query to an LLM can result in wildly different responses, a problem known as prompt sensitivity. But, as any survey researcher can tell you, humans behave this way, too. The phrasing of a question in an opinion poll can have drastic impacts on the answers. We need to invent new security systems that adapt to these differences and prevent harm from AI mistakes.

What is algorithmic AI red-teaming and how does it impact the security of your GenAI applications? Check out Robust Intelligence's new interactive guide to learn about algorithmic AI red-teaming, an automated prompt injection technique capable of jailbreaking sophisticated LLMs with no human supervision. Visit the interactive guide here: https://lnkd.in/grpck2Qy #AIsecurity #LLMsecurity #LLMs #generativeAI #redteaming #jailbreak #promptinjection

Generative AI acts as both a sword and a shield for payment companies While AI is becoming smarter through learning, it needs to be validated as the vast amount of data required to train AI models is being scraped from the internet with often little or no attribution or permission. Deloitte provides an interesting perspective on the risks of generative AI for payment companies, suggesting that they should supercharge their anti-fraud skillsets with generative AI technologies and third-party data to train their authentication and fraud detection models. https://lnkd.in/gR-8raGN

Check #fraud continues to be a significant threat to #financial institutions (FI), so before it sweeps deeper into your FI's system, it’s paramount you take precautionary #measures. Quinte can help you ace #check fraud #prevention by implementing #Forensic AI, #Machine Learning, transactional analysis, and #image processing techniques backed by an expert-in-the-loop approach to achieve 99.5%+ #accuracy on checks. Get in touch with our #experts today to learn how Quinte can assist you! Shoot your queries at info@quinteft.com #checkfraud #frauddetection #earlydetection #fraudmanagement #fraudmitigation #regulatorycompliance #financialinstitutions #financialservicesindustry #financialcrimeandriskmanagement #quinte #quintefinancialtechnologies #incverified #incverifiedprofile #expertintheloop #experservices 

Sharing a great 12-page GAO product released yesterday that provides a technology assessment on Generative AI common development practices, including testing for vulnerabilities and the limitations of this testing that are used by commercial entities to combat errors: https://lnkd.in/gS5DnkDS If you're fairly new to AI use, like myself, this product provides some great layman-terms explanations of common Generative AI challenges to include data poisoning, jailbreaking, and prompt injections. It also provides some great insight into how commercial developers are trying to counter such challenges, and frames what a large undertaking it presents.