# financial-fraud Published articles for financial-fraud. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Benchmarking LLMs in Real-World Applications: Pitfalls and Surprises DevFeed: [Benchmarking LLMs in Real-World Applications: Pitfalls and Surprises]() Original publisher: [Read original article]() Author: Jean Alves Published: 2025-11-25T15:31:00Z Content type: article Language: en Sources: [Feedzai]() Topics: [LLM evaluation / benchmarking](), [benchmarking](), [Generative AI](), [Security, Privacy and Abuse Prevention]() Tags: [benchmarking](), [computer-vision](), [evaluation](), [financial-fraud](), [fraud-prevention](), [hallucinations](), [large-language-models](), [llms](), [machine-learning](), [trust]() ### AI overview This article explains how Feedzai's ScamAlert uses Generative AI to identify interpretable scam signals from submitted screenshots, and why systematic evaluation and benchmarking are needed to assess consistency, explanations, performance, and limitations such as hallucinations. ### Source excerpt By Jean V. Alves and Ferran Pla Fernández Moving beyond binary classification provides novel insights. In the real world, scams rarely present themselves in black and white. Fraudsters exploit nuance, impersonate legitimate brands, and mask malicious intent with seemingly ordinary behavior. That's why Feedzai has launched ScamAlert (patent pending), a Generative AI-based system innovating on the current paradigm of scam prevention, in response to this growing challenge. Traditional detection systems treat the problem as a binary choice: scam or not a scam, often outputting an estimated "scam likelihood" measure. This value, even if accurate, doesn't tell users why something is risky or what they should watch out for, leaving them with little guidance on how to stay safe. A potential scam SMS The binary approach can often suffer from a lack of context. While a text message may look suspicious in a vacuum (e.g., a payment request via a less safe method) the user may have other reasons to believe in its legitimacy, such as a past history of such requests. Consequently, an incorrect risk estimate based on missing context may lead users to distrust the system's abilities. A traditional binary classification system outputs only a risk estimate ScamAlert, on the other hand, makes judgements on what it knows. Users submit a screenshot of the suspected scam, and ScamAlert identifies observable red flags, patterns or behaviors that are often associated with fraud, such as suspicious links or spelling errors. This approach empowers the user with interpretable insights into the detected risk signals, instead of a vague numeric value. This places the user in the driver's seat, by presenting them with the facts and enhancing their awareness and judgment. To fully understand a systems' ability to perform this task, we pair this labelling approach with a rigorous evaluation and benchmarking protocol. We test the consistency of model outputs for the same input; the model's ability t ## Feedzai TrustScore: Enabling Network Intelligence to Fight Financial Crime DevFeed: [Feedzai TrustScore: Enabling Network Intelligence to Fight Financial Crime]() Original publisher: [Read original article]() Author: Sofia Guerreiro Published: 2025-07-25T11:59:49Z Content type: article Language: en Sources: [Feedzai]() Topics: [Network](), [Artificial Intelligence](), [AI Models]() Tags: [ai](), [ai-models](), [banking](), [feedzai](), [financial](), [financial-fraud](), [fraud](), [fraud-detection](), [genai](), [machine-learning](), [network](), [network-intelligence](), [report](), [research](), [scams]() ### AI overview This blog post introduces Feedzai TrustScore, part of the Feedzai IQ solution, as an AI-based approach to financial fraud detection. It describes how network intelligence and aggregated knowledge from Feedzai's processed events are intended to help detect threats earlier and adapt to changing fraud patterns. ### Source excerpt By Sofia Guerreiro, Ricardo Ribeiro Pereira, Iker Perez, Jacopo Bono Detecting financial fraud is like finding a moving needle in a shifting haystack. Fraud accounts for a tiny fraction of financial transactions, often less than 0.1%. At the same time, fraudsters are constantly adapting their tactics to evade detection. And this happens within a live and dynamic environment, where financial behaviors and technologies are changing over time. In short, this is an exceptionally difficult problem for financial institutions. With the rise of digital banking and new technologies like GenAI, this problem is becoming even more challenging. In fact, fraud schemes are spreading faster, growing more sophisticated, and becoming increasingly coordinated across geographies. Meanwhile, many financial institutions still rely on detection systems that are either too rigid (e.g., relying on handcrafted rules) or too narrow (e.g., dependent on custom AI models), and neither option can keep up with the pace of change. At Feedzai, we've developed a new approach. In this blog post, we introduce Feedzai TrustScore, part of the Feedzai IQ™ solution. This is a state-of-the-art AI solution that combines the aggregated knowledge from our network of $8.02T yearly processed events to detect threats earlier, adapt continuously, and safeguard financial systems with speed and precision. Traditional Fraud Detection: Catching a Moving Target With a Fixed Net Every year, millions of people fall victim to financial fraud or scams, with a recent report revealing an average loss of over $2,000 per victim. To protect their customers, banks and other financial institutions have made great efforts towards detecting these suspicious transactions, sometimes temporarily blocking them, conducting internal reviews, and contacting the account owner to validate the activity. However, this is an inherently adversarial task, as malicious actors adapt to whatever detection system the banks have in place. These playe ## "Show Me What's Wrong!": Enhancing Fraud Detection Analysis by Combining Charts and Text DevFeed: ["Show Me What's Wrong!": Enhancing Fraud Detection Analysis by Combining Charts and Text]() Original publisher: [Read original article]() Author: Beatriz Feliciano Published: 2024-11-22T18:35:14Z Content type: article Language: en Sources: [Feedzai]() Topics: [Transactions](), [data](), [synthetic-data](), [Tool]() Tags: [data](), [data-analysis](), [data-visualization](), [financial-fraud](), [fraud](), [fraud-detection](), [fraud-investigation](), [image](), [interface](), [research](), [synthetic-data](), [transactions]() ### AI overview The article describes a fraud-analysis tool that combines charts and text to help analysts review suspicious financial transactions. It explains that tabular review can make it difficult to identify patterns and anomalies within a 1-to-5-minute review window, and presents an interface using synthetic data to help analysts prioritize investigation areas. ### Source excerpt Every year, millions of people fall victim to financial fraud. In 2023, the losses tied to this type of crime were estimated at US$159 billion just in the US, with some people losing all of their retirement savings to scammers. However, the impacts of this issue stretch beyond someone's finances. It can also impact a victim's life in many dimensions. Detecting and quickly acting upon suspicious transactions is essential to tackle this problem. Finding Fraud Through Data Tables To review the data of alerted transactions, analysts look at information in tabular format (similar to what is presented in Figure 1), scrolling through it to assess past activity patterns of the alerted person and comparing those with the alerted event. "How much money was spent on average on past transactions?" or "Is that significantly different from the amount on the current alert?" are some questions they might try to answer during their review. Figure 1: Image of a table that analysts typically use to review the data of alerted transactions. The issue with this approach is that finding groups of patterns and anomalies in tabular data can be overwhelming since it requires an increased cognitive load from analysts to interpret the data effectively. This becomes even more complex since these professionals must review and classify the alerted transaction in a short time -- between 1 and 5 minutes. Revamping the analysis To solve this problem, we present a tool that combines charts and text to guide the analysis of financial transactions. As presented in Figure 2, the tool (populated with synthetic data) is divided into three regions that provide different levels of information detail -- from the most high-level to the most detailed. The goal is that the analyst can scan the charts and prioritize their review towards specific areas of the alert. Figure 2: Proposed interface composed of multiple regions: the Knowledge Area Console (A) to detect suspicious areas of the analysis; the Knowledge Are