DATA SCIENCE IN FINANCIAL SERVICES: RISK ANALYSIS, FRAUD DETECTION, AND MARKET INSIGHTS

One area of interdisciplinary academic study that is thought to offer deep business insights is data science. The study's objective is to evaluate the value of market, risk, and fraud analysis in data science for enhancing financial services. Literature review: It is well recognised that using data science to conduct a risk analysis influences commercial organisations' decision-making when it comes to starting new projects. To enhance the banking sector's services, data science's fraud detection division is required. A more complex sort of data science called market analysis can give businesses important insights. Methodology: A study's methodological choice is crucial since it determines the validity and dependability of the research endeavour. In this instance, it may be said that the study adhered to a theoretical analysis as including theories aids in the investigation of the research problem. Findings and analysis: In the context of data science, risk prediction models are helpful since they facilitate the start of statistical analysis. In this instance, it should be noted that quick fraud detection is made possible by the efficiency of data processing and encoding. Thus, putting in place such a network can enhance market data analysis by helping to unravel data layers and patterns. Conclusion: The purpose of the study is to critically examine the effects of fraud detection, risk analysis, and market analysis. This project has focused on leveraging data science to improve financial services. It has been demonstrated that the study's critical evaluation has a significant influence on how data science is applied in the financial industr

Anomaly detection of consumption in Hotel Units: A case study comparing isolation forest and variational autoencoder algorithms

Buildings are responsible for a high percentage of global energy consumption, and thus, the improvement of their efficiency can positively impact not only the costs to the companies they house, but also at a global level. One way to reduce that impact is to constantly monitor the consumption levels of these buildings and to quickly act when unjustified levels are detected. Currently, a variety of sensor networks can be deployed to constantly monitor many variables associated with these buildings, including distinct types of meters, air temperature, solar radiation, etc. However, as consumption is highly dependent on occupancy and environmental variables, the identification of anomalous consumption levels is a challenging task. This study focuses on the implementation of an intelligent system, capable of performing the early detection of anomalous sequences of values in consumption time series applied to distinct hotel unit meters. The development of the system was performed in several steps, which resulted in the implementation of several modules. An initial (i) Exploratory Data Analysis (EDA) phase was made to analyze the data, including the consumption datasets of electricity, water, and gas, obtained over several years. The results of the EDA were used to implement a (ii) data correction module, capable of dealing with the transmission losses and erroneous values identified during the EDA’s phase. Then, a (iii) comparative study was performed between a machine learning (ML) algorithm and a deep learning (DL) one, respectively, the isolation forest (IF) and a variational autoencoder (VAE). The study was made, taking into consideration a (iv) proposed performance metric for anomaly detection algorithms in unsupervised time series, also considering computational requirements and adaptability to different types of data. (v) The results show that the IF algorithm is a better solution for the presented problem, since it is easily adaptable to different sources of data, to different combinations of features, and has lower computational complexity. This allows its deployment without major computational requirements, high knowledge, and data history, whilst also being less prone to problems with missing data. As a global outcome, an architecture of a platform is proposed that encompasses the mentioned modules. The platform represents a running system, performing continuous detection and quickly alerting hotel managers about possible anomalous consumption levels, allowing them to take more timely measures to investigate and solve the associated causes.info:eu-repo/semantics/publishedVersio

多変量時系列データの変分オートエンコーダによるロバストな教示なし異常検知

九州工業大学博士学位論文 学位記番号：情工博甲第370号 学位授与年月日：令和4年9月26日1: Introduction|2: Background & Theory|3: Methodology|4: Experiments and Discussion|5: Conclusions九州工業大学令和4年

Комп’ютерне моделювання в наукоємних технологіях: збірник наукових праць міжнародної науково-технічної конференції (23-25 листопада 2022 р., м. Харків, Україна)

Для викладачів, наукових працівників, аспірантів, студентів вишів. Робочі мови конференції: українська, англійська.VIII Міжнародна науково-технічна конференція «Комп’ютерне моделювання в наукоємних технологіях» (КМНТ-2022) відбулася на базі Харківського національного університету імені В.Н.Каразіна 23-25 листопада 2022 року. Співорганізаторами цього наукового заходу виступили: ННЦ Харківський фізико-технічний інститут, MAX PLANCK INSTITUTE OF MICROSTRUCTURE PHYSICS, Київський національний університет імені Тараса ШЕВЧЕНКА, INSTITUTE OF NUCLEAR CHEMISTRY AND TECHNOLOGY (Warsaw, Poland), Рівненський державний гуманітарний університет, Національний аерокосмічний університет імені М. Є. Жуковського (ХАРКІВ), ЗАТ « Інститут інформаційних технологій » (Харків), Херсонський національний технічний університет, TEAM INTERNATIONAL SERVICES, INC. (Lake Mary, USA). Конференція проходила в онлайн режимі за наступними основними напрямками роботи: 1. Математичне моделювання технологічних процесів та приладів. 2. Моделювання інформаційних процесів у складних і розподілених системах. 3. Системи автоматизованого збору та когнітивного представлення наукових даних. 4. Моделювання фізичних процесів в радіаційних, плазмових та інших сучасних технологіях. 5. Безпека інформаційних систем і технологій. 6. Моделі процесів розробки та оцінки якості програмного забезпечення. Також було організовано загальну об’єднану секцію за всіма науковими напрямами конференції для студентів, аспірантів та молодих вчених, на якій молоді науковці мали змогу додатково доповісти результати своїх робіт. Метою цієї конференції було представлення та обговорення нових, оригінальних результатів досліджень українських та закордонних вчених у галузі математичного моделювання та обчислювальних методів, інформаційних технологій та захисту інформації. Крім того, конференція мала на меті організувати співпрацю науковців та студентів України та наших закордонних колег задля розвитку науки та освіти в Україні.The VIII International Scientific and Technical Conference "Computer modelling in high tech" (CMHT-2022) was held at V. N. Karazin Kharkiv National University on 23-25 November 2022. The co-organisers of this scientific event were: NSC Kharkiv Institute of Physics and Technology, MAX PLANCK INSTITUTE FOR MICROSTRUCTURE PHYSICS, Taras Shevchenko National University of Kyiv, INSTITUTE OF NUCLEAR CHEMISTRY AND TECHNOLOGY (Warsaw, Poland), Rivne State University of Humanities, National Aerospace University named after M.E. Zhukovsky (Kharkiv), CJSC "Institute of Information Technologies" (Kharkiv), Kherson National Technical University, TEAM INTERNATIONAL SERVICES, INC. (Lake Mary, USA). The conference was held online, the main themes being: 1. Mathematical modelling of technological processes and devices. 2. Modelling of information processes in complex and distributed systems. 3. Systems of the computer-aided acquisition and cognitive representation of the scientific data. 4. Modelling of physical processes in radiation, plasma and other modern technologies. 5. Security of information systems and technologies. 6. Models of software development and quality assessment. The joint section on all covered themes was organized for students, postgraduates and young scientists, where young scientists presented the results of their work. The purpose of this conference was to present and discuss results of new, original research of Ukrainian and foreign scientists in the field of mathematical modelling and computational methods, information technology and information security. In addition, the aim of conference was organizing the cooperation between Ukrainian scientists and students and our foreign colleagues to promote development of science and education in Ukraine