# financial sector Published articles for financial sector. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Attackers Expose Ongoing AI Tool Use Targeting Organizations in Latin America DevFeed: [Attackers Expose Ongoing AI Tool Use Targeting Organizations in Latin America]() Original publisher: [Read original article]() Author: Reese Lewis and Sara McBroom Published: 2026-09-03T10:00:58Z Content type: article Language: en Sources: [Unit 42]() Topics: [Language models](), [AI Chat](), [Incident response]() Tags: [agentic-ai](), [ai](), [chatgpt](), [cl-cri-1131](), [cl-cri-1163](), [claude-code](), [data](), [financial-sector](), [go](), [large-language-models-llms](), [malware](), [nextchat](), [operations](), [phishing](), [shipping-and-transportation](), [socks5](), [socktz](), [threat-research]() ### AI overview The article examines two ongoing intrusion and data-exfiltration campaigns targeting organizations in Latin America. It describes attackers using commercial large language models, proxy infrastructure, phishing, remote-access malware, and operational tooling. ### Source excerpt Explore how attackers targeting Latin American entities use AI for data exfiltration and how basic OpSec errors allow defenders to disrupt operations. The post Attackers Expose Ongoing AI Tool Use Targeting Organizations in Latin America appeared first on Unit 42. ## What NIST's mDL guidance means for the future of digital identity DevFeed: [What NIST's mDL guidance means for the future of digital identity]() Original publisher: [Read original article]() Author: info@1password.com (Daryl Martin) Published: 2026-05-04T00:00:00Z Content type: article Language: en Sources: [Blog on 1Password Blog]() Topics: [Security](), [systems]() Tags: [apple](), [collaboration](), [cryptographic](), [financial-sector](), [government](), [identity](), [news](), [open](), [security]() ### AI overview NIST's draft guidance on mobile driver's licenses describes how government-issued digital credentials can be cryptographically verified and shared selectively. The article explains the roles of issuers, wallets, and verifiers, and presents mDLs as a more privacy-preserving and interoperable alternative to uploading images of physical IDs. ### Source excerpt The latest National Institute of Standards and Technology (NIST) draft guidance on mobile driver's licenses (mDLs) is about more than one use case or credential type. While the draft primarily focuses on the financial sector due to its high-assurance requirements, the bigger takeaway is that government-issued identity can be cryptographically verified and shared more selectively. This provides strong, cryptographically verifiable evidence of identity and shows what a more interoperable digital identity ecosystem could look like 1Password has contributed to the work behind this draft. We believe that identity systems need to be developed through global standards and collaboration across multiple verticals. Open ecosystems scale; closed ones often fail. mDLs replace document uploads with cryptographic verification An mDL is a government-issued verifiable digital credential. It serves as the digital version of your physical driver's license, defined as a highly specified mobile document (mDoc) under international standards. To identify a person with cryptographic trust, the ecosystem relies on three parties: An issuer that signs the credential A wallet that securely stores and presents it A verifier that checks its authenticity A simple real-world example is airport security, where the DMV is the issuer, your Apple Wallet is the wallet, and the TSA is the verifier when you present your mDL. While this might sound more complex than simply flashing a physical ID, the experience can be seamless when implemented well. Historically, users had to upload an image of their driver's license, which exposed their sex, address, weight, and other unnecessary personal data. With an mDL, you securely transmit only the attributes needed for that interaction. For example, you would only expose the state you live in to qualify for services, nothing else in a well defined flow. mDLs turn automated online verification from an image processing problem into a cryptographic verification prob ## The GANfather: Using Malicious GenAI Agents to Combat Money Laundering DevFeed: [The GANfather: Using Malicious GenAI Agents to Combat Money Laundering]() Original publisher: [Read original article]() Author: Ricardo Ribeiro Pereira Published: 2024-10-04T13:49:42Z Content type: article Language: en Sources: [Feedzai]() Topics: [Generative AI](), [synthetic-data](), [Machine learning](), [AI Development](), [data]() Tags: [ai](), [banking](), [data](), [feedzai](), [financial-sector](), [gans](), [genai](), [generative](), [generative-ai](), [legacy](), [machine-learning](), [money-laundering](), [research](), [synthetic](), [synthetic-data](), [transactions]() ### AI overview Feedzai describes a method that uses Generative AI to create synthetic data simulating realistic money-laundering activity. The generated examples are intended to support machine-learning approaches to detection and help identify vulnerabilities in banks' defenses, addressing the limited availability of labeled data. ### Source excerpt Digital systems have become deeply integrated into many aspects of modern life, particularly within the financial sector. While digital banking simplifies day-to-day operations for clients, it also creates new opportunities for malicious actors to exploit these systems. As a result, money laundering has grown particularly prevalent due to this digital expansion. Banks are required to monitor for money laundering activities and issue alerts when suspicious transactions are detected. Typically, monitoring is performed by rules-based legacy systems. A better approach would be to use Machine Learning models, but these usually require labeled data to train, which are mostly unavailable in this use case. To tackle this problem, we employ advanced Generative AI (GenAI) techniques to generate synthetic data that simulates realistic money laundering activities. These synthetic examples help us identify vulnerabilities and strengthen the defense mechanisms used by banks and other financial institutions In this blog post, we will explore the method developed by Feedzai, which leverages GenAI to tackle the challenges of detecting and preventing money laundering in today's digital landscape. This blog post is the first of a series dedicated to the work done on GenAI by Feedzai Research in the last few years. Problem Statement First, let's briefly introduce the concepts behind money laundering and the difficulties that banks face when trying to prevent it. Money laundering is the process of concealing the origins of illegally obtained funds. Criminals cannot directly spend "dirty" money without risking exposure of their illegal activities. Therefore, they want to disguise the origins of funds before using them. Money laundering typically involves three stages: Placement: the money is introduced into the financial system, often in small amounts spread across various banks. Layering: the money launderer moves the funds through a series of transactions, typically across multiple fin ## Introducing the Open FinLLM Leaderboard DevFeed: [Introducing the Open FinLLM Leaderboard]() Original publisher: [Read original article]() Author: Xie; Jimin Huang; Sophia Ananiadou; Xiao-Yang Liu Yanglet; Alejandro Lopez-Lira; Wang; ldruth; Ruoyu Xiang; chenzhengyu; Yangyang Yu Published: 2024-10-04T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [Finance](), [LLMs](), [Human-AI evaluation](), [Natural language processing](), [Open Source]() Tags: [artificial-intelligence](), [benchmarks](), [collaboration](), [community](), [evaluation](), [finance](), [financial-sector](), [fine-tuning](), [leaderboard](), [open-source](), [qa](), [testing](), [text-generation](), [zero-shot]() ### AI overview The article introduces the Open FinLLM Leaderboard, a specialized evaluation framework for financial language models. It evaluates models on finance-specific tasks such as information extraction, sentiment analysis, credit risk scoring, stock forecasting, question answering, text generation, and decision-making, using real-world datasets and metrics including Accuracy, F1 Score, ROUGE, and MCC. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## How to verify, document, & prove compliance with Gremlin DevFeed: [How to verify, document, & prove compliance with Gremlin]() Original publisher: [Read original article]() Author: Gavin Cahill Published: 2024-08-29T00:00:00Z Content type: tutorial Language: en Sources: [Gremlin Blog]() Topics: [Resilience](), [Testing](), [systems]() Tags: [compliance](), [financial-sector](), [how-to](), [operational](), [regulatory](), [requirements](), [resilience]() ### AI overview This article explains how Gremlin can help companies in regulated industries address operational resilience and risk-management requirements. It describes controlled failure simulation, resilience testing, and documenting results for compliance reporting, with examples including DORA, APRA CPS 230, FCA PS21/3, and OSFI Guideline E-21. ### Source excerpt Find out how Gremlin can help companies in regulated industries comply with Operational Resilience requirements like DORA, APRA CPS230, FCA PS21/3, and more.