Monetizing Financial Data Analytics: Best Practice

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.

International journal of science and research, 2018

The financial services industry is undergoing a transformative shift propelled by advancements in data engineering. This paper delves into how data engineering significantly enhances operational efficiency and customer experience across various domains such as fraud detection, personalized banking, risk management, and algorithmic trading. Through empirical evidence and survey data, we demonstrate substantial improvements in efficiency and customer satisfaction brought about by data engineering implementations. Additionally, we outline a robust data engineering framework tailored for financial institutions, which integrates advanced tools and practices to address the unique challenges of the industry. This framework serves as a blueprint for achieving streamlined data integration, management, and analysis, further strengthening the capacity for innovation and compliance in the financial sector. We also explore the challenges and opportunities associated with adopting such data engineering practices, highlighting the necessity for strong data governance and ethical considerations within the financial landscape.

2021

A large number of EU organizations already leverage Big Data pools to drive value and investments. This trend also applies to the banking sector. As a specific example, CaixaBank currently manages more than 300 different data sources (more than 4 PetaBytes of data and increasing) and more than 700 internal and external active users and services are processing them every day. In order to harness value from such high-volume and high-variety of data, banks need to resolve several challenges, such as finding efficient ways to perform Big Data analytics and to provide solutions that help to increase the involvement of bank employees, the true decision makers. In this book chapter, we describe how these challenges are resolved by the self-service solution developed within the I-BiDaaS project. In more detail, we present three CaixaBank use cases, namely i) Analysis of relationships through IP addresses; ii) Advanced Analysis of bank transfer payment in financial terminal; and iii) Enhance...

This study explores the integration of big data and statistical analytics in financial reporting systems to uncover hidden patterns and enhance financial decision-making. The primary objective is to examine how statistical techniques such as regression analysis, chi-square tests, and time series analysis improve transparency, accuracy, and fraud detection. Using a mixed-methods approach, quantitative data from financial reports (2020-2024) and qualitative insights from industry experts were analyzed. The results indicate a significant correlation (r = 0.87, p < 0.001) between investment in big data analytics and reporting accuracy, with regression analysis (R² = 0.82) showing that a $5M increase in data investments enhances financial accuracy by 3%. The chisquare test (χ² = 45.63, p < 0.001) confirms that improved data integration raises fraud detection rates from 78% to 96%, while time series analysis highlights a 520% surge in financial transactions over the study period. These findings demonstrate the critical role of predictive analytics in streamlining financial reporting. The study concludes that leveraging big data analytics enhances financial integrity, minimizes errors, and strengthens fraud detection. To maximize these benefits, financial institutions should invest in standardized data governance, explainable AI models, and cyber security measures while fostering data literacy through continuous training.

Journal of Big Data

Value Chain has been considered as a key model for managing efficiently value creation processes within organizations. However, with the digitization of the end-to-end processes which began to adopt data as a main source of value, traditional value chain models have become outdated. For this, researchers have developed new value chain models, called Data Value Chains, to carry out data driven organizations. Thereafter, new data value chains called Big Data Value chain have emerged with the emergence of Big Data in order to face new data-related challenges such as high volume, velocity, and variety. These Big Data Value Chains describe the data flow within organizations which rely on Big Data to extract valuable insights. It is a set of ordered steps using Big Data Analytics tools and mainly built for going from data generation to knowledge creation. The advances in Big Data and Big Data Value Chain, using clear processes for aggregation and exploitation of data, have given rise to w...

2020

Digital transformation is a key imperative across multiple industries globally. One of the main tenets of digital transformation is improving the intelligence quotient in an organization in order to enable accurate decision-making. Intelligence in the digital era is fueled by data and .by the ability to analyze data in a way that generates meaningful insights, thereby making an organization competitive. Literature shows that the benefits of being analytically competitive are widespread and impactful across many organization functions like strategy, finance, marketing and operations; but this can be accomplished only if an organization has the right level of capability for adopting and executing analytics initiatives. Data, leadership, people skills, culture and governance are capabilities that are essential for successful analytics endeavors in an organization. This study is focused on building an end-to-end perspective comprising input factors and outcomes of being analytically competitive.

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.

IAEME PUBLICATION, 2024

Data warehousing plays a crucial role in the financial services industry, enabling institutions to manage vast amounts of data, derive valuable insights, and enhance operational efficiency. However, the industry faces unique challenges in terms of data integration, data quality, and scalability. This article explores these challenges and the opportunities presented by data warehousing, such as enhanced risk management, improved customer service, and regulatory compliance. The regulatory landscape, including data privacy, security, and reporting requirements, is also examined. By leveraging realistic data and case studies, this article provides a comprehensive overview of the current state of data warehousing in the financial services industry

Oriental journal of computer science and technology

The following work presents a methodology of determining the economic value of the data owned by a company in a given time period. The ability to determine the value of data at any point of its lifecycle, would make it possible to study the added value that data gives to a company in the long term. Not only external data should be considered but also the impact that the internal data can have on company revenues. The project focuses on data-driven companies, which are different to the data-oriented ones, as explained below. Since some studies affirm that data-driven companies are more profitable, the indirect costs of using those data must be allocated somewhere to understand their financial value14 and to present a possible alternative for measuring the financial impact of data on the revenue of companies.

Big Data and Cognitive Computing

Today, almost all active organizations manage a large amount of data from their business operations with partners, customers, and even competitors. They rely on Data Value Chain (DVC) models to handle data processes and extract hidden values to obtain reliable insights. With the advent of Big Data, operations have become increasingly more data-driven, facing new challenges related to volume, variety, and velocity, and giving birth to another type of value chain called Big Data Value Chain (BDVC). Organizations have become increasingly interested in this kind of value chain to extract confined knowledge and monetize their data assets efficiently. However, few contributions to this field have addressed the BDVC in a synoptic way by considering Big Data monetization. This paper aims to provide an exhaustive and expanded BDVC framework. This end-to-end framework allows us to handle Big Data monetization to make organizations’ processes entirely data-driven, support decision-making, and ...