Sophisticated Risk Research Articles

Participatory risk diagnosis: A preliminary approach to confined space work in the oil, gas, and petrochemical industries.

Confined space work poses a significant threat to worker safety and health, especially in industrial environments like petrochemical plants and refineries. These environments present additional hazards beyond those inherent to confined spaces, such as high pressures, temperatures, and exposure to toxic, flammable, and combustible substances. This study aimed to apply the Deparis method (Participatory Risk Diagnosis) to confined space work in the oil and gas industry. The goal was to identify the key risk factors involved from the perspective of the workers themselves, propose risk reduction measures where feasible within the Deparis framework, and highlight factors that require more sophisticated methodologies for risk mitigation. The study employed the Deparis method to assess 20 different working conditions. The survey yielded a range of results, encompassing issues with readily achievable on-site solutions to more intricate challenges requiring specialized expertise and resources. The Deparis method successfully identified risk factors present in the tasks from the workers' perspective. The application of risk reduction measures proposed by the method allowed for the criticality of most factors to be reduced to acceptable levels. However, certain critical areas, such as physical space constraints, task organization, communication with confined space workers, and exposure to chemical and biological hazards, were found to require alternative approaches to achieve the desired safety levels. The study underscores the effectiveness of the Deparis method as a valuable tool for evaluating risks in confined space operations and advocates for its broader adoption due to its demonstrated efficacy. Additionally, the study highlights the need for further research and development of more sophisticated risk mitigation strategies for specific critical areas in confined space work within the oil and gas industry.

Predicting Bank Nifty Movements Based on ICICI, HDFC, and Axis Bank Returns

This study investigates the link between the returns of ICICI Bank, HDFC Bank, and Axis Bank and the overall performance of the Bank Nifty index, which is a crucial measure of the Indian banking sector's health on the National Stock Exchange. Using five years of historical data, regression analysis and descriptive statistics evaluate trends, correlations, and prediction accuracy in anticipating Bank Nifty returns based on the performance of these large banks. The study finds that while Bank Nifty displays stability as it aggregates multiple bank stocks, individual banks like Axis Bank and HDFC exhibit distinct volatility patterns, with Axis Bank showing significant fluctuations due to firm-specific and external factors.Regression results show a little association between individual bank returns and the Bank Nifty, indicating that the index has limited predictive value for single equities. Nonetheless, knowing these patterns may help investors manage risk, anticipate market changes, and develop portfolio diversification methods. The findings emphasize the relevance of sector-specific patterns, implying that although Bank Nifty captures broad market movements, other elements have a distinct influence on each bank's performance, providing insights for more sophisticated investment and risk management methods.

Perceived Risk Assessment Criteria for Public–Private Partnership Projects in the Water and Sewage Sector: Comparison of Perspectives from Iranian Public and Private Sectors

This research used the SWARA approach to analyze risk assessment criteria for public–private partnership (PPP) projects in Iran’s water and sewage sectors to identify and prioritize the most significant elements influencing project success from public and private viewpoints. Key results show that the public sector considers “risk probability” to be the most important aspect, highlighting the requirement for stability and predictability in project outcomes. In contrast, the private sector prioritizes the “ability to predict and discover risk”, emphasizing efficiently anticipating and managing uncertainty. Furthermore, this study revealed five common major risk characteristics, including “risk manageability” and “uncertainty of risk”; however, their rankings differ per industry, demonstrating various risk prioritizing methodologies. This study is unique in that it focuses only on Iran’s water and sewage infrastructure, an area historically neglected in PPP research, providing a rare investigation of sector-specific hazards as well as the interaction between public and private interests in a developing country environment. The paper makes specific suggestions, calling for more openness, improved communication, and the use of sophisticated risk management techniques to bridge the gap across sectors. These findings not only add to the scholarly knowledge of PPP dynamics in emerging countries but also provide practical recommendations for governments and private investors navigating Iran’s infrastructure issues.

Engaging in cyber hygiene: the role of thoughtful decision-making and informational interventions.

The effectiveness of human-centric cybersecurity largely depends on end-users' adherence to security and privacy behaviors. Understanding and predicting variations in the adoption of these safeguards is crucial for both theoretical advancement and practical application. While existing frameworks are often adapted from health science literature, there is potential to enhance these models by incorporating criminological constructs relevant to online victimization. This study introduce rational choice theory of thoughtfully reflective decision-making (TRDM) into the information security domain. TRDM suggests that variations in cognitive decision-making capabilities influence behavioral outcomes, particularly in the context of security and privacy practices. The study employed a field experiment to test the applicability of TRDM in predicting end-users' engagement in security and privacy behaviors. Participants were exposed to security-related warnings, with the hypothesis that thoughtfully reflective decision-makers would be more likely to adopt robust protective behaviors. Data was collected on participants' responses to these security warnings, as well as their overall adherence to privacy and security practices. The findings support the theoretical framework: individuals exhibiting thoughtfully reflective decision-making tendencies demonstrated a higher likelihood of engaging in privacy and security behaviors. Specifically, participants with higher TRDM scores were more likely to adopt protective behaviors when warned of the consequences of non-compliance. These results indicate that cognitive decision-making capabilities significantly influence the likelihood of engaging in cybersecurity practices. The study challenges the prevailing one-size-fits-all approach to cybersecurity by highlighting the importance of individual differences in cognitive decision-making. Thoughtfully reflective decision-makers are better equipped to adopt preventive security measures, suggesting the need for more tailored interventions in cybersecurity education and risk assessment. This research contributes to the development of sophisticated risk assessment tools aimed at mitigating vulnerabilities and reducing users' susceptibility to digital threats. Incorporating TRDM into information security models provides a more nuanced understanding of user behavior, offering insights into how cognitive processes influence cybersecurity adherence.

The impact of fintech on the efficiency of urban commercial banks in China-based on super slacks-based measures with undesirable output

This study endeavours to elucidate the influence of financial technology (fintech) on the operational efficiency of urban commercial banks in China. By scrutinizing the integration of Internet-based fintech solutions, it seeks to illuminate their potential for streamlining banking processes, elevating customer experiences and augmenting the precision of credit assessments and lending decisions through sophisticated data analytics and risk management tools. This research evaluates the efficiency of Chinese urban commercial banks employing the Super Slacks-Based Measure (Super-SBM) which incorporates undesirable output. Furthermore, it leverages the System Generalized Method of Moments (System GMM) to investigate fintech’s specific impact on urban commercial bank efficiency across various Chinese regions. The findings underscore fintech’s pivotal role in catalyzing digital transformation within banks, thereby bolstering operational efficiency and customer satisfaction. Moreover, this study highlights the critical importance of fintech in fortifying risk management capabilities within the banking sector. The insights gleaned from this research contribute to the banking industry’s sustainable growth and competitive resilience. Additionally, the findings furnish policymakers with empirical evidence to support the strategic integration of fintech and the optimization of banking service processes. Urban commercial banks can streamline operations, enhance customer experiences, refine credit assessments and ultimately attain higher efficiency and long-term sustainability levels in the rapidly evolving financial landscape by harnessing the transformative potential of fintech.

Development Cyber Risk Assessment for Intrusion Detection Using Enhanced Random Forest

In cybersecurity, the lack of statistical data on cyber-attacks presents a significant challenge from an insurance perspective, hindering the accurate calculation of insurance premiums, furthermore assessing cybersecurity risk exposure and identifying high-risk threat categories. Effective intrusion detection systems (IDS) are paramount in addressing these issues. This research introduces a sophisticated cyber risk assessment model utilizing the Random Forest classification algorithm, tailored explicitly for IDS, and leverages the comprehensive CIC-IDS 2017 dataset. The central objective was to engineer robust models capable of classifying a broad array of cyber threats, focusing on classification accuracy. The model achieved an accurate average classification rate of 96.94% through systematic experimentation and hyperparameter tuning.This study found that 'n_estimators' values of 10 to 300 did not affect cyberattack performance. It was also shown that Bagging and bootstrapping improve model stability by mitigating variance and improving accuracy without many trees. Model performance was high, with an average F1-Score of 97.86%. Cyber-attack statistics are scarce, and from an insurance perspective, the lack of statistical data on cyber-attacks hinders the calculation of insurance premiums. Risk assessment allows for informed self-insurance or risk transfer processes ensuring that policies align with risk management strategies and premium calculations.

Reducing the negative impact of wildfire disasters through multi-scenario risk analysis of land investment

Natural meteorological and climatological hazards and vulnerable vegetation are frequent triggers of natural disasters, such as wildfires. These cause loss of life and significant economic damage, likely to increase due to climate and socio-economic changes. The increase in wildfires in recent decades has affected the decision-making of technocrats and policy experts and created the need for more sophisticated risk management. This paper develops a theoretical framework for reducing the economic impact of wildfire risk on forests. Also provides insight into land use decisions regarding losses that this increased risk of forest land management can cause. The impact of wildfire risk on economic land value is evaluated by comparing three scenarios, i.e. the deterministic risk-adjusted land expectation value with its risk-free counterparts calculated assuming the stationary risk and predicted increasing risk as time goes by. Also, for this purpose, we evaluate the economically optimal length rotation period pine stands and the risk premium. Fire risk is described as the probability of pine stands destruction due to their age and meteorological conditions in the experimental area Záhorská lowland (western Slovakia). Our findings highlight the importance of alerting forest policymakers to carefully incorporate assessment of the increasing risk of random natural disasters due to climate change into sustainable forest management. The study's results will also help current or future forest owners estimate and evaluate the economic/investing risk when buying or selling forest land.

Integration of electromagnetic and mechanical models for effective lightning protection in buildings

The study focused on the design of an advanced algorithm for the optimal sizing of protection systems against atmospheric discharges in architectural structures, applying the rolling sphere method. This technique facilitated the incorporation of user-specified parameters through an advanced graphical interface. The methodology began with the exhaustive accumulation of data relevant to the project. Risk indices were estimated through sophisticated risk analysis software applications. If adjustments were required, the process continued; If not, the building was considered to be adequately secured. The ground resistivity was evaluated according to IEEE Std. 81, and the rolling sphere method was implemented according to IEC 662305-3. The grounding systems were configured in accordance with IEEE Std. 142 and IEEE Std. 80. To analyze the interaction of electrical discharges with the protected building, the electrical equivalents of elements such as meshes, fused copper rods were computed. , and conductors positioned horizontally and vertically. Using these data, a model was built in ATPDraw, interconnected with Python for the generation of graphical representations of the current waves in the different protection subsystems. To conclude and corroborate the effectiveness of the process, the risk indices were reevaluated. The validation of the algorithm was achieved by minimizing the margin of error to insignificant levels by incorporating standardized data proposed by organizations such as IEC and IEEE, thus confirming the precision of the designed algorithm

Bibliometric Analysis of Trends and Developments in Agile Project Management

This study employs a comprehensive bibliometric analysis to explore the evolution, trends, and research networks within the field of Agile Project Management (APM). Utilizing data from prominent academic databases and visualized through VOSviewer, this paper identifies thematic clusters that highlight foundational Agile principles, strategic implementations, team dynamics, and technical aspects of APM. The research trends over the past decade demonstrate a significant shift from basic Agile methodologies towards advanced topics like risk and cost management, reflecting the field's maturation and adaptation to diverse and complex environments. The analysis also uncovers substantial research opportunities in integrating Agile practices with cost management, applying Agile in non-traditional fields, and developing sophisticated risk management strategies. Moreover, an examination of author collaboration networks reveals distinct clusters of scholarly activity and highlights influential researchers and their interrelationships. This study provides valuable insights into the dynamic landscape of Agile Project Management research, underscoring both established areas and emerging frontiers for future exploration.

The Evaluating Impact of Artificial Intelligence on Risk Management and Fraud Detection in the Commercial Bank in Bangladesh

The integration of Artificial Intelligence (AI) in the banking sector represents a significant leap forward in the realms of risk management and fraud detection. This paper explores the transformative effects of AI in these areas, emphasizing both the improvements and the challenges brought about by its implementation. In risk management, AI's influence is diverse and profound. Advanced algorithms allow for the creation of more sophisticated credit risk assessment models by detecting subtle patterns in large datasets that might be overlooked by human analysts. This capability enhances the accuracy of credit risk evaluations. Additionally, real-time monitoring of transactions helps in the immediate mitigation of risks, which is particularly crucial when dealing with market and liquidity risks. AI also significantly aids in automating compliance with regulatory requirements, reducing the likelihood of human errors and enabling quicker adaptation to changes in regulations. Operational risks are also minimized through AI's ability to automate routine tasks and strengthen cybersecurity measures. AI systems are adept at identifying anomalies that may indicate fraud by scrutinizing transaction data and customer behavior. The predictive capabilities of AI enable banks to anticipate and prevent potential fraud schemes. Moreover, AI systems can adapt and evolve in response to changing tactics used by fraudsters, maintaining their effectiveness over time. AI enhances customer authentication processes through the use of advanced technologies such as biometric verification, providing an additional layer of security. However, the implementation of AI in banking raises significant concerns regarding data privacy and security due to the sensitive nature of banking information. Furthermore, AI models can inherently carry biases that lead to discriminatory outcomes, necessitating ongoing monitoring and adjustments to these models. The complexity and lack of transparency in AI systems also pose challenges, particularly when AI-driven decisions have significant impacts on customers. The evolving regulatory frameworks for AI in banking present another layer of complexity, as banks must continuously adapt to comply with new and changing regulations. This paper highlights the need for a balanced approach to leveraging AI's potential in banking, addressing both its transformative benefits and the ethical and regulatory challenges involved. By doing so, banks can harness AI to enhance their operations while ensuring fairness, transparency, and compliance with regulatory standards.

Integrating Advanced Technologies in Financial Risk Management: A Comprehensive Analysis

This paper delves into the pivotal role of advanced technologies in enhancing financial risk management across various domains, including credit risk, market risk, operational risk, and liquidity risk. It meticulously explores the application of machine learning (ML) algorithms and artificial intelligence (AI) in developing sophisticated risk assessment models, portfolio diversification strategies, and regulatory compliance mechanisms, which collectively surpass traditional methodologies in accuracy, efficiency, and predictive power. Through a detailed examination of enhanced Value at Risk (VaR) models, dynamic hedging strategies, and the impact of geopolitical events on market risk, alongside innovative approaches to operational risk mitigation and liquidity planning, this study underscores the transformative potential of technological advancements in financial risk management. It highlights how these technologies facilitate real-time analysis, predictive modeling, and strategic planning, significantly contributing to the resilience and stability of financial institutions in the face of evolving risks and regulatory requirements.

Integrating Advanced Technologies in Financial Risk Management: A Comprehensive Analysis

This paper delves into the pivotal role of advanced technologies in enhancing financial risk management across various domains, including credit risk, market risk, operational risk, and liquidity risk. It meticulously explores the application of machine learning (ML) algorithms and artificial intelligence (AI) in developing sophisticated risk assessment models, portfolio diversification strategies, and regulatory compliance mechanisms, which collectively surpass traditional methodologies in accuracy, efficiency, and predictive power. Through a detailed examination of enhanced Value at Risk (VaR) models, dynamic hedging strategies, and the impact of geopolitical events on market risk, alongside innovative approaches to operational risk mitigation and liquidity planning, this study underscores the transformative potential of technological advancements in financial risk management. It highlights how these technologies facilitate real-time analysis, predictive modeling, and strategic planning, significantly contributing to the resilience and stability of financial institutions in the face of evolving risks and regulatory requirements.

Advancements in Artificial Intelligence for Precision Diagnosis and Treatment of Myocardial Infarction: A Comprehensive Review of Clinical Trials and Randomized Controlled Trials.

Coronary artery disease (CAD) is still a serious global health issue that has a substantial impact on death and illness rates. The goal of primary prevention strategies is to lower the risk of developing CAD. Nevertheless, current methods usually rely on simple risk assessment instruments that might overlook significant individual risk factors. This limitation highlights the need for innovative methods that can accurately assess cardiovascular risk and offer personalized preventive care. Recent advances in machine learning and artificial intelligence (AI) have opened up interesting new avenues for optimizing primary preventive efforts for CAD and improving risk prediction models. By leveraging large-scale databases and advanced computational techniques, AI has the potential to fundamentally alter how cardiovascular risk is evaluated and managed. This review looks at current randomized controlled studies and clinical trials that explore the application of AI and machine learning to improve primary preventive measures for CAD. The emphasis is on their ability to recognize and include a range of risk elements in sophisticated risk assessment models.

Advancing portfolio optimization: The convergence of machine learning and traditional financial models

This paper delves into the transformative impact of machine learning (ML) on portfolio optimization, showcasing how ML algorithms can significantly enhance traditional financial models such as the Capital Asset Pricing Model (CAPM) and the Arbitrage Pricing Theory (APT). Through a comprehensive examination of regression analysis, classification algorithms, and reinforcement learning, we illustrate the methodologies by which ML refines the prediction of asset returns, assesses investment risks, and dynamically adjusts portfolio allocations. We discuss the integration of ML with CAPM and APT to improve the estimation of systematic risk and identify multi-factor influences on asset returns, offering a more nuanced approach to optimizing portfolios. Additionally, the paper highlights the role of big data in augmenting predictive accuracy and the application of optimization algorithms like Gradient Descent and Genetic Algorithms in achieving optimal asset allocations. By addressing challenges such as multicollinearity and overfitting, we demonstrate the potential of ML to revolutionize investment strategies, enabling more sophisticated risk management and return maximization. This study not only underscores the synergy between ML and traditional financial theories but also paves the way for future innovations in financial analytics.

ДОВГОСТРОКОВА ДИНАМІКА МІЖ BITCOIN ТА ФОНДОВИМИ РИНКАМИ

This paper investigates the long-run relationship between Bitcoin and the S&P 500 index using daily price data from January 2018 to July 2024. While previous studies have focused primarily on correlation analysis and volatility spillovers, we employ both Engle-Granger and Johansen cointegration methodologies to examine the possibility of a long-run equilibrium relationship between these markets. Our results provide strong evidence of cointegration, with the Engle-Granger test indicating significance at the 5% level (p-value = 0.0288) and the Johansen trace statistics confirming one cointegrating relationship. Interestingly, this long-run relationship coexists with a relatively low daily return correlation (0.2884) and substantially different volatility profiles (Bitcoin: 68.56%, S&P 500: 19.87% annualized). These findings suggest that while Bitcoin and the S&P 500 may deviate substantially in the short term, they share common long-run drivers. Our results have important implications for portfolio diversification, statistical arbitrage strategies, and regulatory frameworks, challenging the conventional view of Bitcoin as a purely alternative asset class. The evidence of market integration supports the need for coordinated regulatory approaches and sophisticated risk management strategies in cryptocurrency investments.

Two-view topogram-based anatomy-guided CT reconstruction for prospective risk minimization

To facilitate a prospective estimation of the effective dose of an CT scan prior to the actual scanning in order to use sophisticated patient risk minimizing methods, a prospective spatial dose estimation and the known anatomical structures are required. To this end, a CT reconstruction method is required to reconstruct CT volumes from as few projections as possible, i.e. by using the topograms, with anatomical structures as correct as possible. In this work, an optimized CT reconstruction model based on a generative adversarial network (GAN) is proposed. The GAN is trained to reconstruct 3D volumes from an anterior-posterior and a lateral CT projection. To enhance anatomical structures, a pre-trained organ segmentation network and the 3D perceptual loss are applied during the training phase, so that the model can then generate both organ-enhanced CT volume and organ segmentation masks. The proposed method can reconstruct CT volumes with PSNR of 26.49, RMSE of 196.17, and SSIM of 0.64, compared to 26.21, 201.55 and 0.63 using the baseline method. In terms of the anatomical structure, the proposed method effectively enhances the organ shapes and boundaries and allows for a straight-forward identification of the relevant anatomical structures. We note that conventional reconstruction metrics fail to indicate the enhancement of anatomical structures. In addition to such metrics, the evaluation is expanded with assessing the organ segmentation performance. The average organ dice of the proposed method is 0.71 compared with 0.63 for the baseline model, indicating the enhancement of anatomical structures.

Risk assessment for synthetic GICs: a quantitative framework for asset–liability management

AbstractThis study addresses a research gap in quantitative modeling framework and scenario analysis for the risk management of stable value fund wraps, a crucial segment of the U.S. financial market with over USD $400 billion in assets. In this paper, we present an asset–liability model that encompasses an innovative approach to modeling the assets of fixed-income funds coupled with a liability model backed by empirical analysis on a unique data set covering 80% of the stand-alone plan sponsor market, contrasting with models based solely on regular deterministic cash flows and interest rate differences. Our model identifies and analyzes two critical risk scenarios from the insurer’s perspective: inflationary and yield spike. Our approach demonstrates that the tail risk of wraps, used as an economic capital measure, is sensitive to characteristic parameters of the fund, such as the duration, portfolio composition and credit quality of assets. This finding significantly differs from U.S. regulatory approaches like the NAIC’s, which often result in a zero capital requirement. These findings reveal limitations in current actuarial risk and profitability metrics for U.S. insurers and argue that a more sophisticated risk model reproducing the two critical scenarios is necessary.

High-risk pooling for mitigating risk selection incentives in health insurance markets with sophisticated risk equalization: an application based on health survey information

BackgroundDespite sophisticated risk equalization, insurers in regulated health insurance markets still face incentives to attract healthy people and avoid the chronically ill because of predictable differences in profitability between these groups. The traditional approach to mitigate such incentives for risk selection is to improve the risk-equalization model by adding or refining risk adjusters. However, not all potential risk adjusters are appropriate. One example are risk adjusters based on health survey information. Despite its predictiveness of future healthcare spending, such information is generally considered inappropriate for risk equalization, due to feasibility challenges and a potential lack of representativeness.MethodsWe study the effects of high-risk pooling (HRP) as a strategy for mitigating risk selection incentives in the presence of sophisticated– though imperfect– risk equalization. We simulate a HRP modality in which insurers can ex-ante assign predictably unprofitable individuals to a ‘high risk pool’ using information from a health survey. We evaluate the effect of five alternative pool sizes based on predicted residual spending post risk equalization on insurers’ incentives for risk selection and cost control, and compare this to the situation without HRP.ResultsThe results show that HRP based on health survey information can substantially reduce risk selection incentives. For example, eliminating the undercompensation for the top-1% with the highest predicted residual spending reduces selection incentives against the total group with a chronic disease (60% of the population) by approximately 25%. Overall, the selection incentives gradually decrease with a larger pool size. The largest marginal reduction is found moving from no high-risk pool to HRP for the top 1% individuals with the highest predicted residual spending.ConclusionOur main conclusion is that HRP has the potential to considerably reduce remaining risk selection incentives at the expense of a relatively small reduction of incentives for cost control. The extent to which this can be achieved, however, depends on the design of the high-risk pool.

A REVIEW OF U.S. FINANCIAL REPORTING SCANDALS AND THEIR ECONOMIC REPERCUSSIONS: INVESTIGATING THEIR BROADER IMPACT AND PREVENTATIVE MEASURES

This study provides a comprehensive analysis of financial reporting scandals in the United States, focusing on their evolution, impacts, and the effectiveness of preventative strategies. The primary objective was to understand the historical development of financial reporting, identify the mechanisms of financial scandals, assess their economic and social repercussions, and evaluate the regulatory and technological responses implemented to prevent such occurrences. Employing a systematic literature review and content analysis, the study analyzed data from academic journals, government publications, and reports from financial regulatory bodies, with a focus on literature published between 2010 and 2023. The key findings reveal that financial reporting scandals have significantly influenced corporate governance and financial regulation, leading to substantial economic and social impacts, including loss of investor trust and market instability. Preventative strategies, notably legislative reforms such as the Sarbanes-Oxley and Dodd-Frank Acts, have been instrumental in enhancing transparency and accountability in financial reporting. Technological innovations have also emerged as vital tools in detecting and preventing financial irregularities. The study concludes that building a resilient financial reporting landscape requires continuous adaptation to technological advancements, development of sophisticated risk management tools, and fostering a culture of ethical reporting. Policymakers and industry leaders are urged to focus on creating balanced legal frameworks that encourage transparency and protect against financial malpractices, thereby ensuring a financial reporting environment that is transparent, accountable, and resilient against future challenges.
 Keywords: Financial Reporting Scandals, Corporate Governance, Regulatory Reforms, Preventative Strategies.

How to translate genetic findings into clinical applications in spondyloarthritis?

Spondyloarthritis (SpA) is characterized by a strong genetic predisposition evidenced by the identification of up to 50 susceptibility loci, in addition to HLA-B27, the major genetic factor associated with the disease. These loci have not only deepened our understanding of disease pathogenesis but also offer the potential to improve disease management. Diagnostic delay is a major issue in SpA. HLA-B27 testing is widely used as diagnostic biomarker in SpA but its predictive value is limited. Several attempts have been made to develop more sophisticated polygenic risk score (PRS). However, these scores currently offer very little improvement as compared to HLA-B27 and are still difficult to implement in clinical routine. Genetics might also help to predict disease outcome including treatment response. Several genetic variants have been reported to be associated with radiographic damage or with poor response to TNF blockers, unfortunately with lack of coherence across studies. Large-scale studies should be conducted to obtain more robust findings. Genetic and genomic evidence in complex diseases can be further used to support the identification of new drug targets and to repurpose existing drugs. Although not fully driven by genetics, development of IL-17 blockers has been facilitated by the discovery of the association between IL23R variants and SpA. Development of recent approaches combining GWAS findings with functional genomics will help to prioritize new drug targets in the future. Although very promising, translational genetics in SpA remains challenging and will require a multidisciplinary approach that integrates genetics, genomics, immunology, and clinical research.