Risk Engineering (Bulgaria)

The scale and complexity of quantitative modeling within modern financial institutions have transformed the model inventory from a static compliance register into a foundational component of enterprise risk management. This paper reframes the firm-wide model inventory as an enterprise risk system, essential to effective governance under supervisory frameworks such as SR 11-7 and the Basel Committee’s principles for risk data aggregation, and demonstrates that treating the inventory as a passive compliance register introduces structural blind spots that can materially undermine enterprise risk aggregation and capital reliability. This paper develops an audit-grade analytical framework for assessing inventory completeness, classification accuracy, and end-to-end data lineage, and examines how deficiencies in these dimensions propagate into failures of risk aggregation and capital adequacy assessment. The paper further explores technical mechanisms for identifying undisclosed or “shadow” models, architectural requirements for real-time lineage traceability, and the mathematical sensitivity of aggregate risk measures to data quality errors. By integrating the model inventory directly with enterprise risk aggregation frameworks, institutions can convert inventory governance from a compliance obligation into a control mechanism for systemic risk transparency.

Purpose The concept of Green Banking is a paradigm shift in the business ideology of a bank. Based on the Institutional theory, this study aims to examine the influence of stakeholders’ (top management, customer, competitor and community) pressure on Green Banking Adoption in the Pakistan banking industry. It also examines the moderating effect of “green capability” on the relationship between stakeholders’ pressures and Green Banking Adoption. The moderator was derived from the pollution prevention capability of the Natural Resource–Based View (NRBV) of the Firm. Design/methodology/approach This study adopted a quantitative methodology in which data was collected through self-administered questionnaires through the survey method. The population of this study was bank branches in Pakistan, and simple random sampling was used to generate the sample. The unit of analysis was the bank branch, and branch managers were the respondents of this study. Data analysis was carried out using SMART PLS. This study conducted a disjoint two-stage analysis for the higher-order dependent variable, i.e. Green Banking. Measurement model analysis was run at both stages to assess the reliability and validity of the constructs and items. Hypotheses testing was done through the structural model analysis using bootstrapping. Findings The findings of this study showed a positive relationship between Top Management Pressure, Customer Pressure, Competitor Pressure and Green Banking Adoption. Community Pressure did not significantly influence Green Banking Adoption in bank branches. Green Capability positively moderated the relationship between Customer and Competitor Pressure and Green Banking Adoption. Originality/value Green Banking has limited academic research, especially in the context of developing countries. This study aims to fill the research gap currently existing in Green Banking through the integration of Institutional Theory and the NRBV of the firm.

Corporate reputation has shifted from a vague intangible to a measurable strategic resource exposed to disinformation waves, generative-AI risks, cyberattacks, and tighter disclosure/privacy rules. In this context, reputation assessors require integrated competencies that combine data analytics, ethics and law, risk management, and operational execution. The article designs and empirically validates an evidence-based, five-factor competency framework - Data & Intelligence; Ethics/Law/Governance; Risk & Resilience; Strategy & Stakeholders; Assurance & Performance - links these competencies to measurable outcomes (accuracy, time-to-decision, incident severity, stakeholder trust), and delivers practical instruments (validated scale, training pathways, governance templates, explainability artifacts, and benchmark datasets). An explanatory, sequential mixed-methods program integrates scoping review and expert Delphi, psychometric development (EFA/CFA, reliability, convergent/discriminant validity, measurement invariance), field measures and crisis simulations, quasi-experimental evaluations of analytics and governance (event studies, difference-in-differences, synthetic controls), randomized usability tests of XAI artifacts, and A/B studies on data-governance ROI. The model exhibits strong fit, reliability, and cross-industry/language invariance; higher competency levels are associated with greater assessment accuracy and trust, faster decisions, and lower incident severity. Quasi-experimental estimates indicate that adopting NLP/graph analytics and implementing MRM controls causally reduces time-to-detect, peak severity, and market impact. SHAP summaries paired with model cards improve practitioner comprehension and decision readiness, while data lineage, DQ rules, and access controls enhance model performance, auditability, and evidentiary robustness with minimal privacy-driven utility loss. Targeted micro-credentials produce durable gains across domains. A competency-centric, analytics-enabled, governance-anchored approach transforms reputation assessment into a managed, auditable discipline that organizations can operationalize immediately through the provided scale, governance templates, explainability playbooks, and open benchmarks.

The field of rheumatoid arthritis (RA) is moving towards identification of and intervention in people at risk of RA, but a validated risk stratification method is lacking. This work was undertaken to develop a risk stratification method for persons presenting with arthralgia considered to be at risk of RA. A joint European Alliance of Associations for Rheumatology (EULAR)/American College of Rheumatology (ACR) expert committee was established. Risk factor and outcome data from 10 arthralgia cohorts (including clinically suspect arthralgia and autoantibody-positive arthralgia) were studied. The work focused on differentiating the risk of progression to clinically apparent inflammatory arthritis (IA) within 1 year, using clinical and serologic variables, without and with subclinical joint inflammation detected by ultrasound (US) or magnetic resonance imaging (MRI). Developing RA according to the 2010 EULAR/ACR criteria within 1 year was a secondary outcome. A set of validated risk stratification criteria was developed. Using data from 2293 symptomatic at-risk individuals, a stratification method was derived consisting of 6 clinical and serologic variables (morning stiffness, patient-reported joint swelling, difficulty making a fist, C-reactive protein, rheumatoid factor, and anti-citrullinated peptide antibody) yielding an area under the curve (AUC) of 0.80 (95% CI, 0.77-0.83) for IA development. The inclusion of US variables did not increase the discriminative ability. When MRI-detected subclinical inflammation variables were included, the AUC was 0.87 (95% CI, 0.82-0.90). In the presence of clinical, serologic, and MRI variables, a sensitivity and specificity of >75% was achieved. For RA development, the AUC of the criteria with MRI was 0.93 (95% CI, 0.90-0.97). EULAR/ACR risk stratification criteria have been developed for people with arthralgia in secondary care who are considered at risk for RA. They can be applied in the absence or presence of imaging data and have been developed to define homogeneous risk groups for future prevention trials.

Der Text untersucht den Stand der Wissenschaftsfreiheit in der Ukraine während des andauernden Krieges und stützt sich dabei auf den SCIENCE AT RISK Monitoring Report 2024. Die Analyse basiert auf den Indikatoren des Academic Freedom Index und hebt die Veränderungen hervor, die seit der Verhängung des Kriegsrechts stattgefunden haben. Während zu Beginn der vollumfänglichen Invasion Russlands im Jahr 2022 ein deutlicher Rückgang des Index zu verzeichnen war, erreichten bestimmte Aspekte - wie die Verbreitung von Forschungsergebnissen und die internationale Zusammenarbeit - bis 2024 wieder das Vorkriegsniveau. Weitere Erkenntnisse des Artikels basieren auf einer Umfrage unter ukrainischen Wissenschaftler:innen verschiedener Fachrichtungen, Karrierestufen und Regionen.

Comprehensive and robust hazard and risk assessments require information beyond the current hazard map information. Current hazard mapping is usually limited to selected return periods and these return periods are commonly based on the return period of release and not the return period of impact.We present a modelling approach, which allows to derive probability-based hazard information at any location in avalanche-prone areas. Such an event-based modelling approach provides continuous intensity-frequency-curves at any location and allows to calculate continuous loss-frequency-curves for individual objects as well as for groups of objects. It is therefore suited for comprehensive hazard and risk assessments.The modelling approach is based on probabilistic seismic hazard analyses, which are common practice for seismic hazard and risk assessments since several decades. Continuous intensity-frequency information is required for hazard assessment and continuous loss-frequency curves are required for risk assessment. Therefore, we simulate numerous possible avalanches, covering the entire range of release volumes, from small to very large, and various possible model parameters. The probabilities of the different avalanche events are based on the exceedance probabilities of different snow accumulation heights, represented by the three-day snow accumulation, which is also the key information used for the generation of traditional hazard maps. The combined evaluation of avalanche intensities and probabilities in the area of impact allows to derive continuous intensity-frequency-curves at any given location. This approach contrasts with the common practice of assigning the return period of a single release volume to all possible outcomes of this release volume. In combination with exposed values and their vulnerability functions, this modelling approach allows to derive continuous loss-frequency-curves for comprehensive risk assessment and risk communication.We present the simulation results from a case study in the Bernese Oberland in the Swiss Alps. The case study results highlight the advantages of this new modelling approach in hazard and risk management and the additional analyses being possible with this new modelling approach.

To investigate the screening efficacy of six thrombotic markers for hypercoagulable state (HCS) in pregnant women, including thrombin-antithrombin III complex (TAT), plasmin-alpha-2 plasmin inhibitor complex (PIC), thrombomodulin (TM), tissue-type plasminogen activator inhibitor complex(t-PAI-C), D-dimer(D-D), and fibrinogen degradation products (FDP). This was a retrospective study. Eighty-five high-risk pregnant women who underwent antenatal examination at Baoding maternal and Child Health Hospital from December 2022 to September 2023 were included as the observation group, while 85 healthy pregnant women without complications or comorbidities who underwent routine antenatal examinations at our hospital were randomly enrolled as the control group. Analyzed the screening value of these six thrombotic markers in pregnant women at risk of HCS. The levels of TAT, PIC, TM, FDP, and D-D in the observation group were significantly higher than in the control group (P < 0.05, respectively). Pearson correlation analysis showed a weak correlation between maternal age and TAT levels (r = -0.179, P< 0.05) as well as between BMI and FDP and D-D (r = 0.214, 0.232, P< 0.05, respectively). Binary logistic regression analysis revealed that abnormal D-D levels were a risk factor for HCS in pregnant women (OR = 0.117, 95% CI: 0.621-0.639, P< 0.05). The area under the joint curve was 0.757, indicating that the model is of certain predictive value for HCS in pregnant women. TAT, PIC, TM, t-PAI-C, D-D, and FDP can used for the screening of HCS in pregnant women and have a certain predictive value for high-risk pregnant women.