RISK MANAGEMENT USING DATA SCIENCE APPROACHES
Notice bibliographique
Résumé
Risk management is a vital component in the decision-making process, for instance, in the financial, healthcare and cybersecurity domains. Nowadays, in the time which is the era of explosive growth of data, where the data science approaches have shown to be powerful tools of risk management management. This study reviews using data science techniques to detect, describe, and minimize risk through various sectors. Data science uses advanced algorithms and statistics models to extract valuable insights from large datasets so that organizations can take informed decisions in a risk management process. The algorithms of machine learning have become important tools in the risk management operations because they use the data of the past in order to recognize the trend patterns and predict the outcomes. The technique also ensures the detection of anomalies such as abnormal behaviors or patterns which are considered risks or fraudulent activities. Data science data models are widely implemented in financial to determine credit risks, portfolio optimization or fraud detection. Historical market data and financial indicators can be analyzed by predictive models in order to determine the likelihood of default or to assess the risk/return tradeoff in investment portfolios. By the same token, in healthcare, the data-driven approaches which have the ability to identify patient risks, bring out the best of the available treatment plans, and predict disease outbreaks, are also being used. Moreover, data science plays essential role in cybersecurity, by identifying and preventing cyber threats during the process. Machine learning algorithms, built on the analysis of network traffic, user behaviors and system logs, can point out suspicious activities as well as potential vulnerabilities, consequently increasing the overall security level of the system. The use of data science within the risk management process has a number of advantages, such as better assessment of risk, faster decision-making, and the possibility to proactively deal with risks. However, data quality issues, the explainability of the models, as well as moral concerns are among the safety factors to enable reliable and efficient risk management of the data-based solutions. Eventually the data science techniques utilized in risk management yields the organizations a better grasp of classifying, evaluating, and overcoming risks in different sectors. Utilizing innovative analytical methodologies and smart data applications, organizations will be able to strengthen their ability to react to some uncertainties that they may face and make better decisions at a business atmosphere that is complex and dynamic.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,006 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».