Financial Services Authority

Behavior-Driven Development (BDD) frameworks like Cucumber and Behave align business requirements with executable tests but remain costly to author and maintain, especially for legacy or evolving systems [1], [2]. We present a novel LLM-driven approach, leveraging LLaMA to reverse-engineer requirements from schemas, API payloads, and partial tests to automatically generate Gherkin feature files with corresponding step definitions. Generated artifacts are drafted into automated pull requests and merged into the repositories via human review [3]. Our proposed method when tested across two enterprise systems, over six sprints, reduced authoring time by about 75%, expanded negative test coverage by 30%, and achieved cost savings ranging from $7,725 in conservative adoption to over $40,000 in high-volume adoption, maintaining infrastructure overhead costs below 3%. Beyond efficiency gains, the approach lowers barriers for teams new to BDD and demonstrates a practical, cost-effective path for industry adoption while opening avenues for research in adaptive prompting, coverage-aware generation, and retrieval-augmented workflows. [3].

Control charts are fundamental tools in Statistical Process Control (SPC), employed to monitor and improve production processes. This study aims to evaluate the performance of the Maximum Exponentially Weighted Moving Average (Max-EWMA) chart by incorporating Auxiliary Variables (AV) and Multiple Measurements (ME). This study also evaluating the covariate method, a multiple measurement framework, and scenarios involving linearly increasing variance. The evaluation focuses on the analysis of both Type I and Type II error rates, using simulation-based methodologies. The results indicate that the Multiple Measurement approach consistently outperforms the Covariate method, exhibiting lower Type II error rates and higher robustness in detecting process shifts. An increase in parameter A enhances the chart’s sensitivity to mean shifts, whereas parameter B shows negligible influence. Additionally, linearly increasing variance contributes to improved detection capability, particularly under conditions of high correlation. Overall, the Multiple Measurement method demonstrates strong effectiveness and reliability across a variety of conditions, underscoring its practical utility in process control applications.

Object detection sits at the core of contemporary computer vision, powering everything from security cameras to driverless cars. In this work, we put Torchvision’s latest detection models under the microscope, while Comet’s rocksolid experiment-tracking keeps score. Drawing on the finely annotated Penn-Fudan Pedestrian dataset, we guide the reader through every step-weight initialization, data wrangling, training routines, and evaluation protocols-examining each model’s knack for adaptation and generalization. A battery of trials teases apart the strengths and shortcomings of marquee architectures such as Mask RCNN, Fast RCNN, Faster RCNN, and RetinaNet. Key yardsticks-mean Average Precision, mean Average Recall, and the trusty F1 score-are logged and visualized in painstaking detail with Comet, revealing each network’s quirks and capabilities. The discussion then turns pragmatic, outlining best practices for deployment and lifecycle management and leaning on Comet’s artifact logging to streamline the hand-off from lab to production. Altogether, the study offers a clear, end-to-end framework that equips researchers and engineers to navigate the real-world maze of object-detection tasks with confidence.

significant influence would be the one on the financial services and corporate governance of the companies. Romania will see an increased market efficiency, more foreign investment, and greater customer protection in the key sectors including non-banking financial sectors. Therefore, with OECD membership, a more predictable economic environment would eventually render Romania an attractive destination for investment. Reciprocally, it will help in economic growth, reduced market volatility, and trust in the corporate sector. In short, higher standards of corporate governance and financial services will have Romania stand in a better position in the global chain and integrate into global markets in a much better way, thereby increasing its economic resilience. Romania already confirmed several times the attachment to the set of values and principles of the OECD. Romania also promotes its profile as a candidate involved in the debates at the OECD level but also active in the projects developed and implemented by the OECD through the added value it can bring to the Organization at the regional level.

The rapid integration of artificial intelligence (AI) and machine learning (ML) technologies into enterprise operations has fundamentally altered the vendor risk landscape. Organizations increasingly rely on third-party AI vendors for mission-critical processes such as fraud analytics, credit decisioning, underwriting, customer segmentation, and transportation route optimization. Traditional vendor risk methodologies, designed around static information systems, fail to account for model-dependent variability, training data sensitivity, algorithmic opacity, and the dynamic behaviors inherent to continuously learning systems. This research develops a comprehensive assessment framework explicitly tailored to the characteristics of AI vendors. Drawing from more than two hundred vendor assessments performed across the banking, insurance, and transportation sectors, the study identifies four emergent risk domains uniquely associated with AI service providers: model transparency risks, data lineage and provenance uncertainties, algorithmic bias vulnerabilities, and drift susceptibility associated with real-time or periodic retraining cycles. The proposed hybrid conceptual-quantitative framework integrates governance evaluation, model assurance, fairness testing, explainability analysis, and continuous monitoring. The methodology introduces novel mechanisms for assessing proprietary models without requiring model access and presents a composite risk scoring model for quantifying AI-specific vendor exposures. Empirical validation at a Fortune 500 financial services organization demonstrates that the framework enables materially improved risk visibility, more defensible regulatory compliance, and more accurate long-term vendor management strategies. This work provides the first end-to-end, AI-focused vendor risk assessment methodology capable of operating under real-world constraints of opacity and limited disclosure, addressing a significant and urgent gap in third-party risk management practices.

Numerous pharmaceutical and industrial chemicals are classified as endocrine-disrupting chemicals (EDCs) that interfere with hormonal homeostasis, leading to developmental disorders and other pathologies. The synthetic estrogen 17α-ethynylestradiol (EE2) is used in oral contraceptives and other hormone therapies. EE2 and other estrogens are inadvertently introduced into aquatic environments through municipal wastewater and agricultural effluents. Exposure of male fish to estrogens increases expression of the egg yolk precursor protein vitellogenin (Vtg), which is used as a molecular marker of exposure to estrogenic EDCs. The mechanisms behind Vtg induction are not fully known, and we hypothesized that it is regulated via DNA methylation. Adult zebrafish were exposed to either dimethyl sulfoxide or 20 ng/L EE2 for 14 days. Messenger RNA (mRNA) expression and DNA methylation were assessed in male zebrafish livers at 0, 0.25, 0.5, 1, 4, 7, and 14 days of exposure; and those of females were assessed at 13 days (n ≥ 4/group/time point). To test the persistence of any changes, we included a recovery group that received EE2 for 7 days and did not receive any for the following 7 days, in the total 14-day study. Methylation of DNA at the vtg1 promoter was assessed with targeted gene bisulfite sequencing in livers of adult male and female zebrafish. A significant increase in vtg1 mRNA was observed in the EE2-exposed male fish as early as 6 h. Interestingly, DNA methylation changes were observed at 4 days. Decreases in the overall methylation of the vtg1 promoter in exposed males resulted in levels comparable to those in female controls, suggesting feminization. Importantly, DNA methylation levels in males remained significantly impacted after 7 days post-EE2 removal, unlike mRNA levels. These data identify an epigenetic mark of feminization that may serve as an indicator of not only estrogenic exposure but also previous exposure to EE2. Environ Toxicol Chem 2024;43:1547-1556. © 2024 SETAC. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

The IFSCA, is India's newest regulator. It is the regulator for securities, banking, insurance, finance including sustainable finance, pension and other ancillary services for IFSCs in India. This article, part of a series, is a brief introduction to the IFSCA.