Recent advancements in Retrieval-Augmented Generation (RAG) have enabled Large Language Models to answer financial questions using external knowledge bases of U.S. SEC filings, earnings reports, and regulatory documents. However, existing work lacks systematic comparison of vector-based and non-vector RAG architectures for financial documents, and the empirical impact of advanced RAG techniques on retrieval accuracy, answer quality, latency, and cost remain unclear. We present the first systematic evaluation comparing vector-based agentic RAG using hybrid search and metadata filtering against hierarchical node-based systems that traverse document structure without embeddings. We evaluate two enhancement techniques applied to the vector-based architecture, i) cross-encoder reranking for retrieval precision, and ii) small-to-big chunk retrieval for context completeness. Across 1,200 SEC 10-K, 10-Q, and 8-K filings on a 150-question benchmark, we measure retrieval metrics (MRR, Recall@5), answer quality through LLM-as-a-judge pairwise comparisons, latency, and preprocessing costs. Vector-based agentic RAG achieves a 68% win rate over hierarchical node-based systems with comparable latency (5.2 compared to 5.98 seconds). Cross-encoder reranking achieves a 59% absolute improvement at optimal parameters (10, 5) for MRR@5. Small-to-big retrieval achieves a 65% win rate over baseline chunking with only 0.2 seconds additional latency. Our findings reveal that applying advanced RAG techniques to financial Q&A systems improves retrieval accuracy, answer quality, and has cost-performance tradeoffs to be considered in production.

As Artificial Intelligence becomes increasingly central to corporate strategies, concerns over its risks are growing too. In response, regulators are pushing for greater transparency in how companies identify, report and mitigate AI-related risks. In the US, the Securities and Exchange Commission (SEC) repeatedly warned companies to provide their investors with more accurate disclosures of AI-related risks; recent enforcement and litigation against companies' misleading AI claims reinforce these warnings. In the EU, new laws - like the AI Act and Digital Services Act - introduced additional rules on AI risk reporting and mitigation. Given these developments, it is essential to examine if and how companies report AI-related risks to the public. This study presents the first large-scale systematic analysis of AI risk disclosures in SEC 10-K filings, which require public companies to report material risks to their company. We analyse over 30,000 filings from more than 7,000 companies over the past five years, combining quantitative and qualitative analysis. Our findings reveal a sharp increase in the companies that mention AI risk, up from 4% in 2020 to over 43% in the most recent 2024 filings. While legal and competitive AI risks are the most frequently mentioned, we also find growing attention to societal AI risks, such as cyberattacks, fraud, and technical limitations of AI systems. However, many disclosures remain generic or lack details on mitigation strategies, echoing concerns raised recently by the SEC about the quality of AI-related risk reporting. To support future research, we publicly release a web-based tool for easily extracting and analysing keyword-based disclosures across SEC filings.

To responsibly develop Generative AI (GenAI) products, it is critical to define the scope of acceptable inputs and outputs. What constitutes a "safe" response is an actively debated question. Academic work puts an outsized focus on evaluating models by themselves for general purpose aspects such as toxicity, bias, and fairness, especially in conversational applications being used by a broad audience. In contrast, less focus is put on considering sociotechnical systems in specialized domains. Yet, those specialized systems can be subject to extensive and well-understood legal and regulatory scrutiny. These product-specific considerations need to be set in industry-specific laws, regulations, and corporate governance requirements. In this paper, we aim to highlight AI content safety considerations specific to the financial services domain and outline an associated AI content risk taxonomy. We compare this taxonomy to existing work in this space and discuss implications of risk category violations on various stakeholders. We evaluate how existing open-source technical guardrail solutions cover this taxonomy by assessing them on data collected via red-teaming activities. Our results demonstrate that these guardrails fail to detect most of the content risks we discuss.

The U.S. Securities and Exchange Commission (SEC) will seek input on whether digital customer engagement innovations used by financial firms should be governed by existing rules or may need new ones, commission chair Gary Gensler told Reuters. While the SEC's thinking on the subject is at an "early stage," its rules may need updating to account for an artificial intelligence-led revolution in predictive analytics, differential marketing and behavioral prompts designed to optimize customer engagement, he said. The SEC plans to launch a sweeping consultation in coming days that could have major ramifications for retail brokers, wealth managers and robo-advisers, which increasingly use such tools to drive customers to higher-revenue products. I really believe data analytics and AI can bring a lot of positives, but it means we should look back and think about what does this mean for user interface, user engagement, fairness and bias," said Gensler. "What does it mean about rules written ...

When Imran Khan became prime minister of Pakistan in August 2018, the country's economy faced twin deficits – foreign exchange reserves were …

In 2016, a third of surveyed Americans told researchers they believed the government was concealing what they knew about the "North Dakota Crash," a conspiracy made up for the purposes of the survey by the researchers themselves. This crash never happened, but it highlights the flaws humans carry with them in deciding what is or is not real. The internet and other technologies have made it easier to weaponize and exploit these flaws, beguiling more people faster and more compellingly than ever before. It is likely artificial intelligence will be used to exploit the weaknesses inherent in human nature at a scale, speed, and level of effectiveness previously unseen. Adversaries like Russia could pursue goals for using these manipulations to subtly reshape how targets view the world around them, effectively manufacturing their reality.

TYSONS, VA – Appian (NASDAQ: APPN) today announced the latest version of the Appian Platform. The new release of the low-code application development platform increases the speed and business impact of low-code automation for developers, administrators, and end-users. The latest version delivers enhancements to Appian AI, further-expansion of Appian's Connected Systems architecture, integrated Health Check in every application, and simplified DevOps, making it easier than ever to develop, deploy, change, and manage Appian applications. Appian AI, a fast way to add best-of-breed artificial intelligence to any Appian application, now offers Google Cloud Translation as a Connected System. Customers can enable any app to detect languages and translate text with no coding. In addition, this release provides an updated Google Cloud Vision Connected System which now offers integration with Optical Character Recognition (OCR).

Certain statements in this release concerning our future growth prospects are forward-looking statements, which involve a number of risks, and uncertainties that could cause actual results to differ materially from those in such forward-looking statements. The risks and uncertainties relating to these statements include, but are not limited to, risks and uncertainties regarding fluctuations in our earnings, revenue and profits, our ability to generate and manage growth, intense competition in IT services, our ability to maintain our cost advantage, wage increases in India, our ability to attract and retain highly skilled professionals, time and cost overruns on fixed-price, fixed-time frame contracts, client concentration, restrictions on immigration, our ability to manage our international operations, reduced demand for technology in our key focus areas, disruptions in telecommunication networks, our ability to successfully complete and integrate potential acquisitions, liability for damages on our service contracts, the success of the companies in which we make strategic investments, withdrawal of fiscal governmental incentives, political instability, war, legal restrictions on raising capital or acquiring companies outside India, unauthorized use of our intellectual property, and general economic conditions affecting our business and industry. Additional risks that could affect our future operating results are more fully described in our filings with the United States Securities and Exchange Commission. We may, from time to time, make additional written and oral forward-looking statements, including statements contained in the company--s filings with the Securities and Exchange Commission and our reports to shareholders. We do not undertake to update any forward-looking statement that may be made from time to time by us or on our behalf.--

In this May 7, 2018, file photo Microsoft CEO Satya Nadella looks on during a video as he delivers the keynote address at Build, the company's annual conference for software developers in Seattle. Microsoft is paying more than $25 million to settle federal corruption charges involving a bribery scheme in its Hungary office and three other foreign subsidiaries, the U.S. Securities and Exchange Commission said Monday, July 22, 2019. NEW YORK – Microsoft is paying more than $25 million to settle federal corruption charges involving a bribery scheme in Hungary and other foreign offices. The U.S. Securities and Exchange Commission said Microsoft will pay about $16.6 million to settle charges that it violated the Foreign Corrupt Practices Act. While the case centered on Hungary, the SEC said it also found improprieties at Microsoft offices in Saudi Arabia, Thailand and Turkey.

DURHAM – A Durham-based financial management company has raised $770,000, according to a filing with the Securities and Exchange Commission. Trill, which uses artificial intelligence to manage clients' finances, plans to raise an additional $1.23 million, or a total of $2 million. The company did not indicate its intentions for the raised money. Previously, NC Biz News covered other fundraising by the finance company. In October 2017, the company raised $370,000 in a push to raise $1 million.