Role of big data & data in Finance

Pressacademia, 2021

Purpose-Digital infrastructure and technology advancements are steering the innovations in financial sector globally. The technology and data driven aspect has fueled the Fintech sector, evolving at the tangent of mighty finance sector and revolutionary technology domain, especially the digital technologies. The purpose of this paper is to show that most FinTech innovations, are significantly driven by big data analytics and its efficient implementation. Methodology-The use of latest ICT technologies lightens up the finance operations and services to exponential levels. Big data analytics is new and requires comprehensive studies as a research field specially in the finance domain. The intent here is to study an adoption model specially IT diffusion mode to Big data analytics that could detect key success predictors. The study tests the model for adoption of big data as novel technology and the related issues. The paper also presents a review of academic journals, literature, to study the diffusion and adoption of big data in to the finance domain. Findings-The research reflects a significant interest and utility about Big data analytics value that epitomizes the rise of Fintech phenomenon. Big data analytics may provide some competencies to the organizations that may consider its several dimensions along with its framework in the pre-adoption phase or adoption phase or implementation or diffusion phase. The research also attempts to describe the several dimensions of Big data analytics as a new technology. This shall be of good interest to the researchers, professionals, academicians and policy-makers. Conclusion-The paper first defines big data to consolidate the different discourse and literature on big data. We also reflect the point that predictive-analytics (with structured data) overshadows other forms: descriptive and prescriptive analytics (with unstructured data) which constitutes more than 90% of big data. We also reflected on analytics techniques for unstructured data: audio, video, and social media data, as well as predictive analytics. In the analysis and testing part we also performed the testing of the IT diffusion model which concludes that there are significant relationships among IT-planning, IT-implementation and IT-diffusion.

Journal of emerging technologies and innovative research, 2020

Big data is a buzzword that indicates data that do not fit traditional database structure. Their potential is enormous for many fields, and risk management is within the ones that could benefit the most from new sources of unstructured data. This paper introduces the big data framework, terminology, and technology, in order to understand the upsides and challenges that they pose to financial markets. A review of standard methods and tools in risk management is then provided, in order to be able to understand the revolution brought into the environment by big data. Simulation and forecasting are the two areas that are affected the most, and therefore the ones of interest for this study. Nowadays banks operate in changing environment influenced by regulatory requirements, emerging risk types and competition on the market. At the same time banks have available large datasets arising from internal and external sources. The potential for this data usage in risk management has only recent...

Big data, 2017

Significant research challenges must be addressed in the cleaning, transformation, integration, modeling, and analytics of Big Data sources for finance. This article surveys the progress made so far in this direction and obstacles yet to be overcome. These are issues that are of interest to data-driven financial institutions in both corporate finance and consumer finance. These challenges are also of interest to the legal profession as well as to regulators. The discussion is relevant to technology firms that support the growing field of FinTech.

Open Journal of Science and Technology

Big data is a form of data with increased volume, difficult to analyze, process, and store using traditional database technologies. It has long been adopted in business and finance where a large number of bank transaction are executed daily. The emergence of big data in banking industry results to large proportion of technical improvements in the industry. However, its processing causes disruption in the banking industry. Big data analytics is the process that involves using algorithms and software tools to extract useful business information from the dataset. This study adopts big data analytics process to investigates the disruption due to big data processing in the banking industry. The study identifies, acquired, and extracted dataset of the banking industry which was analyzed using MapReduce based fraud committed due to processing of large amount of data. findings show that government employee commit more crime in comparison with the private sector employees. Finally, based on ...

EPH-International Journal of Science And Engineering, 2024

By allowing actual time analysis of vast amounts of information, AI is transforming risk management in FinTech, particularly in the fraud detection. Often depending on rule-based algorithms, conventional fraud detection methods find it difficult to fit the sophisticated methodologies utilized by the modern fraudsters. Big data analytics is used in AI-driven systems to find the anomalies, identify suspicious patterns & the react to emerging risks with more efficiency. By means of supervised learning for transaction categorization & the unsupervised learning for anomaly detection, ML approaches improves financial institutions' capacity to detect the fraudulent behavior with higher accuracy & the efficiency. Deep learning techniques study complex behavioral patterns across many data sources, hence improving fraud detection. Apart from technical proficiency, AI-driven fraud detection has to solve legal challenges like data security laws, compliance requirements, and ethical problems with bias in artificial intelligence models. Big data and artificial intelligence together are transforming fraud prevention methods, lowering false positives, and allowing proactive threat minimizing. Financial institutions are progressively protecting the consumer transactions, fostering trust & the lowering financial losses by means of AI based risk management solutions. Given the increasing cyberthreats, artificial intelligence's capacity for actual time learning & the flexibility make it a required component of contemporary FinTech security.

International Journal of Case Studies in Business, IT, and Education (IJCSBE), 2020

Business analytics refers to the skills, technology, methods of continuous iterative discovery,and study of past business results. In the banking industry, business analytics can be utilized tothe extent that basic banking reporting can be improved with the help of descriptive analytics,predictive analytics, and prescriptive analytics utilizing significant technical developments andthe use of big data currently available. The application of business analytics to banking andfinance, for both organizations and professionals, is crucial, profitable, and extremelyrewarding. Using advanced machine learning technology, combined with analytics, supportsbanks to research a great deal on customer behavior and preferences, allowing banks tocontinuously learn and fine tune analytical models to optimize products and services and minimize the cost of offering products across different channels. Cloud-based analytics platforms provide flexibility and elasticity for banks to work at high speed with large dataworkloads and to gain business value more quickly. In this paper, the major business analyticscomponents - descriptive analytics, predictive analytics, and prescriptive analytics areaddressed and their applications in various functions of banks for optimum decision-making aswell as for activities such as fraud detection, application screening, custom acquisition andretention, awareness of customer purchasing habits, effective cross selling of different bankingproducts and services, payment collection mechanism, better cash/liquidity planning,marketing optimization, consumer lifetime value, management of customer reviews, etc areanalyzed. The effects of these analytics on the banking and financial industry sector'scompetitive and innovative capabilities are also discussed.

Theme In the past, the main challenge that businesses faced had to do with operating under conditions of data scarcity. Decision-making was subject to incomplete, insufficient, and time-lapsed data. However, such challenge no longer exists, given the current exponential growth of data available to businesses. Instead, the challenge that businesses now face has to do with the sheer abundance of data. Up until 2003, only 5 exabytes (1018 bytes) of data were available, whereas nowadays the same amount of data can be created within two days (Sagiroglu and Sinanc, 2013). Businesses now have more data than they can effectively use; as a result, a new critical issue is emerging: big data analytics. The term big data refers to " data sets with sizes beyond the ability of common software tools to capture, curate, manage, and process the data within a specific elapsed time " (Bharadwaj, El Sawy, Pavlou and Venkatraman, 2013, p. 447). The ability to manage big data, information, and ...

Deleted Journal, 2024

Big Data analysis has become an integral part of financial decision making in the modern corporate context. In an increasingly complex and competitive environment, a deep understanding of the factors that influence the success of Big Data analysis is critical. This research aims to investigate the role of variables such as data quality, data security, technology infrastructure, data analysis skills and expertise, leadership and organizational culture, regulations and compliance, and clear business objectives in corporate financial decision making. Using a literature analysis approach, we found that high data quality, strong data security, adequate technology infrastructure, high data analysis skills and expertise, supportive leadership, regulatory compliance, and clear business objectives each contribute positively to the effectiveness of Big Data analysis in supporting financial decision making. These findings highlight the importance of holistic integration of these factors in efforts to improve corporate financial decision-making processes.

IOSR Journal of Computer Engineering, 2016

Financial services organizations around the world are experiencing drastic change. Financial services firms are turning to big data technologies and Hadoop to reduce risk, analyze fraud patterns. Ability of Big Data platforms to process and analyze unstructured data efficiently unlike current platforms that are limited to handling structured data. This flexibility makes it possible to gain insights from different variety of data. Big data can be analyzed for insights that lead to better decisions and strategic business moves. For most organizations, big data is the reality of doing business. It's the proliferation of structured and unstructured data that floods your organization on a daily basisand if managed well, it can deliver powerful insights. Big data analytics is the process of examining large data sets containing a variety of data types-i.e., big data-to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful business information. The analytical findings can lead to more effective marketing, new revenue opportunities, better customer service, improved operational efficiency, competitive advantages over rival organizations and other business benefits.