Alessio Faccia

Strategy Inc. developed from an enterprise analytics company into a listed company with a Bitcoin-dominant treasury structure funded mainly through common equity, preferred stock, and convertible debt. The case offers an observable instance of how a concentrated crypto-asset position interacts with financial reporting, cash commitments, and shareholder exposure. The study uses a descriptive, exploratory single-case design and public evidence from Strategy's 2025 Form 10-K, its Form 10-Q for the six months ended 30 June 2026, related Form 8-K disclosures, and Accounting Standards Update 2023-08. Descriptive ratios and transparent static price scenarios connect reported accounting data with treasury decisions. Bitcoin represented 94.5% of total assets at 30 June 2026. The company recorded a $22.77 billion unrealised digital-asset loss during the first half of 2026, equal to 43.3% of period-end total assets and 1.03 times the combined $22.13 billion of long-term debt and preferred claims. Financing activities supplied $13.77 billion of cash and operating activities supplied $9.85 million, so external financing supplied 95.8% of investment cash. A reserve of $3.75 billion at 24 July 2026 covered approximately 26 months of two specified contractual commitments, preferred dividends, and interest, and not total liquidity. Static price scenarios show Bitcoin market value covering long-term debt between 5.06 and 12.65 times and the combined senior claims between 1.53 and 3.81 times at prices from $40,000 to $100,000, before taxes, transaction costs, and market impact. Common-share growth of 24.7% increased dilution, while Bitcoin per assumed diluted share rose 8.1% because holdings grew faster; that measure records gross exposure per share and not shareholder value. The study specifies a case-derived analytical framework comprising asset concentration, coverage of specified contractual commitments, financing dependence, per-share exposure, and static asset-coverage scenarios, and states five propositions for comparative testing. The framework and its scenario outputs remain unvalidated beyond this single case, do not measure financial resilience or distress probabilities, and are neither a decision tool nor a predictive model, with application elsewhere requiring firm-specific specification, calibration, and empirical validation.

Mortgage lenders need evidence on how prediction models reproduce past decisions and distribute approvals across applicant groups. We study 34,012 Delaware mortgage records from 2024 and compare logistic regression with flexible tree-based models. Three findings answer the research questions. First, the selected flexible model predicts recorded lending decisions more closely and reduces average squared probability error by about 30% relative to penalised logistic regression. Second, racial differences persist when race, sex and ethnicity remain outside the model inputs. At a decision threshold chosen to reproduce the overall historical approval rate, the model approves about 66% of Black applicants and 78% of White applicants. Third, checks of probability reliability, missing debt information and decision thresholds identify issues for audit review. The available evidence does not establish individual reasons for denial. Independent mortgage banks account for 56.0% of sample records, and 57.5% record use of at least one automated underwriting system. The analysis concerns historical decisions and provides no evidence on subsequent loan repayment. Lenders and regulators gain a practical sequence for testing models, reviewing group differences and requesting supporting decision records before operational use.

Machine-readable reporting standards provide shared data infrastructure, while firm-side reporting associations may vary with technological disclosure. The study examines the US Inline XBRL phase-in using 1450 Form 10-K firm years from 250 CIKs over fiscal years 2014–2024. SEC filing metadata, XBRL facts, amendments, accounting controls and full-primary-document text form the analytical panel. Preferred models date exposure from the first periodic report whose SEC metadata identify Inline XBRL use and include firm and year fixed effects with firm-clustered inference. Observed entry is non-random, 41 firms are left-censored for event-time analysis, 82 firms lack observed entry, and later periods offer limited untreated support. The design therefore estimates conditional associations, not a mandate effect. None of the four reporting indicators support H1 after Holm adjustment at the 5 per cent level. Dictionary-defined AI-related disclosure breadth, described as disclosed AI orientation, yields no preferred moderation evidence, and the intangible-intensity and innovation-disclosure tests also remain insignificant after adjustment. A fully specified fixed pre-entry moderator produces two suggestive interactions with Holm-adjusted p = 0.080, without rejection at 5 per cent. Extending asset-threshold and control-omission tests across the main outcomes does not overturn that threshold-based inference. Custom-tag leads reject a flat pre-entry path. The findings distinguish disclosure breadth from verified operational capability and quantify remaining uncertainties rather than establish exact zero.

Federal Reserve press conferences convey policy path information and uncertainty beyond formal decisions. This study tests whether transcript-derived risk language predicts the magnitude of financial market responses after policy surprises and event characteristics enter the model. The dataset contains 93 official press conference transcripts from April 2011 to June 2026, with 90 scheduled events in the primary sample. A Q&A lexical risk index combines standardised frequencies of negative, uncertainty, and weak modal terms from the Loughran–McDonald dictionary. The outcome is an equal weight composite of absolute S&P 500 returns, two- and ten-year Treasury yield changes, and US dollar returns during a 70 min press conference window. OLS models use HC3 standard errors, asset-specific regressions, influence analysis, permutation testing, and leave-one-out cross-validation. Q&A lexical risk does not predict larger responses. The full-model coefficient equals −0.063 (p = 0.444), incremental R² equals 0.0034, and prediction error rises by 0.60% after adding the index. Policy surprise magnitude remains the strongest predictor. The 90-event sample limits precision for small effects, with an approximate 80% minimum detectable effect of 0.230 response index units. The estimates describe conditional association and incremental predictive content. They do not test acoustic delivery, realised intraday volatility, or a causal communication effect.

Digital financial reporting depends on identity services, enterprise systems, cloud platforms, automated controls and system-generated evidence. Cybersecurity weaknesses therefore enter external audit when a governance condition or control deficiency affects a material reporting process, an assertion, a disclosure, an estimate or the reliability of audit evidence. This article develops a non-deterministic control-to-assertion framework through a structured integrative review. The search, completed on 16 July 2026, covered English-language journal work published from 2000 to 15 July 2026 through Google Scholar and publisher search services. The final analytic set contains 32 peer-reviewed journal articles, four institutional sources and two public company filings used for worked application. The revision separates organisation-level cybersecurity governance deficiencies from process-level cyber control deficiencies. It also locates the model against COSO, COBIT 2019, NIST CSF 2.0, IT general control methods and relevant International Standards on Auditing. Existing sources provide taxonomies for governance, internal control, security outcomes and audit procedures. The new framework supplies the missing translation route between those taxonomies: governance condition, control state, financial reporting dependency, assertion-level misstatement risk, audit-evidence reliability, audit response and reassessment. Compensating, detective and corrective controls might interrupt or reduce the route, so no governance deficiency automatically produces a control failure or a material misstatement. Two worked documentary applications, The Clorox Company and MGM Resorts International, show how public incident facts enter account, assertion, evidence and procedure analysis. The framework does not estimate incident probability, expected loss or a cyber risk score. It provides a file-ready reasoning structure for entity-specific risk assessment under the auditing standards. Its main contribution lies in the separate treatment of misstatement risk and evidence reliability, followed by a traceable link to accounts, assertions, evidence sources, specialist input and audit procedures.

Machine-readable financial reporting changes how corporate disclosures become visible, verified, and answerable. Inline eXtensible Business Reporting Language (Inline XBRL) combines a human-readable report with embedded structured data, while artificial intelligence expands automated extraction and screening. The study develops Machine-Readable Accountability as a bounded socio-technical construct organised around dynamic visibility, distributed judgement, and responsibility displacement. A qualitative documentary analysis examines four primary archives: regulatory rules, official filing-evidence records, verification materials, and algorithmic-governance documents. Peer-reviewed studies serve as contextual framing and external corroboration. They do not form primary documentary observations. The corpus covers the mature United States Securities and Exchange Commission regime and the European Single Electronic Format from 2020 to July 2026, with earlier sources retained for historical grounding. A seven-family codebook guides analysis of classification, visibility, validation, judgement, audit, artificial intelligence, and accountability. Results arise from coded rules, official filing observations, assurance requirements, and cross-archive role mapping. Structured reporting reduces extraction costs, supports comparison, and permits automated quality checks. It also places institutional weight on taxonomy fit, extension design, validation logic, and software-mediated review. Documentary evidence supports the relocation of judgement across preparers, taxonomy designers, software vendors, auditors, and regulators. Evidence for deliberate narrative optimisation aimed at artificial intelligence remains indirect, so algorithmic answerability remains a bounded theoretical proposition. The framework links market-efficiency research with studies of quantification, professional judgement, and digital governance, and specifies controls for data lineage, extension approval, model documentation, human review, and responsibility assignment.

Financial crimes’ fast proliferation and sophistication require novel approaches that provide robust and effective solutions. This paper explores the potential of quantum algorithms in combating financial crimes. It highlights the advantages of quantum computing by examining traditional and Machine Learning (ML) techniques alongside quantum approaches. The study showcases advanced methodologies such as Quantum Machine Learning (QML) and Quantum Artificial Intelligence (QAI) as potential solutions for detecting and preventing financial crimes, including money laundering, financial crime detection, cryptocurrency attacks, and market manipulation. These quantum approaches may leverage the inherent computational capabilities of quantum computers to overcome limitations faced by classical methods, contingent on continued advances in hardware and error correction. Furthermore, the paper illustrates how quantum computing could support enhanced financial risk management analysis. Financial institutions may improve their ability to identify and mitigate risks through quantum technologies as the field matures. The paper’s primary contribution is a structured three-layer mapping framework connecting financial crime typologies with classical, machine learning, and quantum countermeasures, accompanied by a feasibility assessment of key quantum algorithms. All capability claims are grounded in an explicit discussion of current NISQ-era limitations and a phased roadmap for future experimental validation.

This article examines whether a high-performing fraud detection model can also meet the demands of auditability, documentation, and regulatory transparency. Using the publicly available European credit card fraud dataset of 284,807 transactions, including 492 fraudulent cases, this study compares weighted logistic regression with XGBoost under severe class imbalance. Model performance is assessed through precision, recall, F1 score, ROC AUC, and precision–recall AUC, with particular attention to alert burden and fraud capture. Results show that XGBoost materially outperforms logistic regression in operational terms. While logistic regression achieves slightly higher recall, XGBoost raises precision from 0.061 to 0.562, improves PR AUC from 0.719 to 0.863, and reduces false positives from 1386 to 67. The PR AUC of 0.863 refers to the cross-validated average reported in the model comparison, while the holdout test result reported later in this paper is 0.852. It cuts the review queue from 1476 alerts to 153 while still identifying 86 of 98 fraud cases in the test set. Explainability is then introduced through SHAP, which provides both global feature attribution and transaction-level reasoning. The findings show that SHAP makes the boosted model readable at the level of both overall model behaviour and individual fraud flags, thereby supporting audit review, model validation, and regulatory scrutiny. The article argues that the combination of XGBoost and SHAP offers a stronger fit for auditing than either a weaker but transparent linear model or a stronger opaque classifier. One limit remains, since the dataset contains anonymised principal components rather than original business variables, which restricts semantic interpretation. Even so, the workflow provides a practical bridge between predictive fraud analytics and the demands of explainable, reviewable, and accountable AI in auditing.