Large Language Models (LLMs) have rapidly transformed the landscape of artificial intelligence, enabling natural language interfaces and dynamic orchestration of software components. However, their reliance on probabilistic inference limits their effectiveness in domains requiring strict logical reasoning, discrete decision-making, and robust interpretability. This paper investigates these limitations and proposes a neurosymbolic approach that augments LLMs with logic-based reasoning modules, particularly leveraging Prolog predicates and composable toolsets. By integrating first-order logic and explicit rule systems, our framework enables LLMs to decompose complex queries into verifiable sub-tasks, orchestrate reliable solutions, and mitigate common failure modes such as hallucination and incorrect step decomposition. We demonstrate the practical benefits of this hybrid architecture through experiments on the DABStep benchmark, showing improved precision, coverage, and system documentation in multi-step reasoning tasks. Our results indicate that combining LLMs with modular logic reasoning restores engineering rigor, enhances system reliability, and offers a scalable path toward trustworthy, interpretable AI agents across complex domains.

Knowing which features of a multivariate time series to measure and when is a key task in medicine, wearables, and robotics. Better acquisition policies can reduce costs while maintaining or even improving the performance of downstream predictors. Inspired by the maximization of conditional mutual information, we propose an approach to train acquirers end-to-end using only the downstream loss. We show that our method outperforms random acquisition policy, matches a model with an unrestrained budget, but does not yet overtake a static acquisition strategy. We highlight the assumptions and outline avenues for future work.

M&A activities are pivotal for market consolidation, enabling firms to augment market power through strategic complementarities. Existing research often overlooks the peer effect, the mutual influence of M&A behaviors among firms, and fails to capture complex interdependencies within industry networks. Common approaches suffer from reliance on ad-hoc feature engineering, data truncation leading to significant information loss, reduced predictive accuracy, and challenges in real-world application. Additionally, the rarity of M&A events necessitates data rebalancing in conventional models, introducing bias and undermining prediction reliability. We propose an innovative M&A predictive model utilizing the Temporal Dynamic Industry Network (TDIN), leveraging temporal point processes and deep learning to adeptly capture industry-wide M&A dynamics. This model facilitates accurate, detailed deal-level predictions without arbitrary data manipulation or rebalancing, demonstrated through superior evaluation results from M&A cases between January 1997 and December 2020. Our approach marks a significant improvement over traditional models by providing detailed insights into M&A activities and strategic recommendations for specific firms.

Datasets, the cornerstone of modern machine learning (ML) systems, have been increasingly sold and purchased for different ML pipelines [2]. Several data marketplaces have emerged to serve different stages of building ML-enhanced data applications. For example, NASDAQ Data Link [3] offers financial datasets cleaned and structured for model training, Amazon AWS data exchange [4] focuses on generic tabular datasets, and Databricks Marketplace [5] integrates raw datasets and ML pipelines to deliver insights. The data-as-a-service market size was more than 30 billions and is expected to double in the next five years [6]. While the data marketplaces are increasingly expanding, unfortunately, data acquisition for ML remains challenging, partially due to its ad-hoc nature: Based on discussions with real-world users, data acquirers often need to negotiate varying contracts with different data providers first, then purchase multiple datasets with different formats, and finally filtering out unnecessary data from the purchased datasets.

Acquiring banks that use artificial intelligence (AI) to monitor merchants on their platforms say the technology has yielded many significant benefits that help boost their businesses' bottom lines. A new report, AI In Focus: Gaining Ground On Merchant Monitoring, a PYMNTS and Brighterion collaboration that surveyed 104 executives from acquiring banks, found that the main benefit is improved operational efficiencies that help drive down costs. Ninety percent of these acquirers cite improved operational efficiencies as one of the benefits of using AI, and 18% say it is the chief benefit. Many acquirers also say that AI has helped them tackle several key barriers holding back their profitability, including fraud, low transaction volumes, short stays by merchants and the need for manual review. The use of AI also helps acquirers differentiate themselves from their competitors.

Sudhir Jha, senior vice president and head of Mastercard's Brighterion unit, told Karen Webster in the most recent On the Agenda discussion that artificial intelligence (AI) can strengthen credit and risk management and broaden its value well beyond simply improving day-to-day operations. But to get there, enterprises need a bit of guidance. "What used to be cutting-edge technology five years ago is no longer cutting edge," he said, and enterprises that try to keep up with the rapid changes in data science and analysis on their own can be quickly overwhelmed. The enterprise that starts with regression and pattern analysis solutions might scale rapidly and find benefit from neural networks. For banks, acquirers and healthcare payments executives, he said, using vendors' AI-based solutions help to avoid undue losses from fraud, the abuse and misallocation of funds and poor underwriting decisions.

The prevalence of fraud has risen rapidly as criminals have sought to take advantage of a unique pandemic-induced combination of financial and health threats which have made people more vulnerable to scams. The National Cyber Security Centre, the UK's cybersecurity agency, revealed last month that it has taken down more scams in the last year than in the previous three years combined, fuelled in particular by coronavirus and NHS-themed fraud attempts. Though some may associate the rise of fraud with cybercriminals becoming more sophisticated, the reality is many old scams are among the most prevalent, with many people still falling for phishing attacks. Younger consumers have become well-versed on how to spot such scams, but the growth of digital activity among older generations, forced to shop online during the pandemic, has opened opportunities for fraudsters to target a far less tech-savvy demographic. A main entry point to scam consumers is when they are making a payment.

While many fintech platforms focus on risk assessment, Brighterion has been solely dedicated to AI-powered decisioning for over 20 years. With a sharp focus on financial irregularities, Brighterion's AI decision-making algorithms provide real-time detection in financial fraud, credit risk, healthcare fraud, waste and abuse, and money laundering (AML). The role of artificial intelligence is taking top billing in the search for software that detects fraud and credit risk. Legacy solutions like rules-based decisioning are hard pressed to stay ahead of bad actors as fraud evolves and becomes more sophisticated. Machine learning rises to the top for its ability to learn from complex and widely varied data.

Since 2012, over 250 companies involved in artificial intelligence have been acquired. Over half of these have been in the last two years - with the vast majority engaged in some aspect of machine learning. Efficient independent learning, machine or human, employs a feedback loop to generate solutions and an evaluation of those solutions leading to better solutions the next time around. Control of any part of that feedback loop is a valuable resource to enable AI, that is, to make smarter systems and enable the systems to function better. These are the two sides of the AI Enablement trend.

As buyout multiples have climbed and capital piles up in private investment funds, acquirers have been looking for a competitive edge in their hunt for acquisitions. For some firms, that has meant doing more advanced data analysis in an effort to better understand, value and integrate a business into their portfolio. And that is bringing computer science and M&A communities closer than ever. Deal making has always required rigorous number crunching and sophisticated excel modelling, but increasingly it's also involving the use of "unstructured data" that don't format neatly in a spreadsheet, such as census figures, social media inputs, image files and even weather patterns. New technology can help acquirers such as pension funds and private-equity buyers when they're prospecting for deals as well as in the due-diligence and integration phases of the deal.