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Please correct the file by adding more intent citations Application of Business Intelligence in Financial Services (FinTech) Jeyashree Jeyabalan University of the Cumberlands August 2022 Approval for Recommendation This dissertation is approved for recommendation to the faculty and administration of the University of the Cumberlands. Dissertation Chair: Dissertation Evaluators: Acknowledgments It is my genuine pleasure to express my sincere gratitude to my mentor, guide Dr. Archie Addo who have helped me with proper guidance and giving valuable advice that helped me during my research. I would not have written this dissertation paper successfully without his continuous supervision and guidance and, I would also like to thank the University of the Cumberlands who have helped in approving this topic. I also want to thank my family and friends who have continuously given me time and comfort while writing this paper. Finally, I would like to thank the Almighty God for writing this paper successfully under his blessings without him nothing would’ve been possible. Abstract A data-driven approach is emerging across the financial service enterprises and fintech companies, affecting the financial services institution’s operations, strategies, technology, and risks. Over the last thirty years, providing access to and enabling active usage of affordable financial products or services has become a global and pressing issue, gaining a lot of attention. According to the statistics conducted by the World Bank, there are about 200 million micro, small and medium-sized enterprises and about 2 billion people with no access to financial products and services. Hence, advances in digital technologies such as artificial intelligence and machine learning, and big data analytics are crucial enablers for financial inclusion. The study's main objective was to investigate how the Fintech industry can be improved using current trends and methods such as machine learning, artificial intelligence, and DBMS to leverage first, second, and third party data. The following objectives guided the study: Establish how business intelligence can be improved in financial technology, establish the extent to which business intelligence can be applied in Chime financial services and be improved, and lastly, identify some of the challenges to Productionisation data within financial services and fintech products. The study adopts a quantitative descriptive research design. The study population comprises nine financial services institutions; Chime is the leading case study evaluated against its competitors like Payments and Starling Bank. Chime is a private financial technology company founded in 2013, which focuses on developing a mobile platform that offers banking services. The study's key findings reveal a strong demand for the application of business intelligence and the deployment of embedded analytics from financial service institutions and fintech companies. The study recommends that a fintech company should integrate business intelligence in various ways, such as customer retention. Table of Contents Chapter One : Introduction 1 Overview 1 Background and Problem Statement 1 Purpose of the study 3 Significance of the study 4 Research Problem 4 Research Objectives 6 Theoretical Review 6 Resource-Based View Theory 6 Limitations of the study 7 Assumptions 8 Summary 10 Chapter Two : Literature Review 17 Introduction 18 Business Intelligence 18 Data Management Systems (DBMS) Challenges for Financial Services 18 Big Data 20 Artificial Intelligence (AI) and Machine Learning 21 Chime Financial Services Company 25 Machine Learning, Artificial Intelligence, and Big Data 26 Sources of Data 30 Traditional Sources of Data 30 Alternative Sources of Data 32 Sources of Operational Data 33 Customer Data Analytics of Financial Services 34 Research Gap 36 Conceptual Framework 37 Research Methodology 37 Introduction 38 Research Design and Approach 38 Population and Sampling 38 Study Population 38 Data Collection Methods 39 Data Analysis 39 Research Quality 40 Validity 40 Reliability 40 Ethical Considerations 41 Data Analysis, Findings, And Interpretations 41 Introduction 41 Response Rate 41 The influence of Data Analytics in the Fintech Industry 42 The Need for Comprehensive Data Analytics in Financial Services Enterprises 42 The State of Deployment and Technology 46 Deployment costs and Regulatory Change 49 Customer Experience and operations of a financial service enterprise 53 Customer Satisfaction Analytics on Delivery of Customer Experience 55 External Data and Amount of analyzed data 56 Application of big data technologies 57 Challenges to Productionisation data within financial services and fintech products. 58 Opportunities for application of business intelligence in financial services 59 Chapter Summary 60 Conclusion 61 References 62 APPENDICES 70 APPENDIX I: LIST OF FINTECH COMPANIES 70 List of Figures Figure 1 : Conceptual Framework………………………………………………………….37 Figure 2 : Adoption of Data Analytics 43 Figure 3 : The need for embedded data analytics by clients 45 Figure 4 : The benefits of embedded data analytics solutions 46 Figure 5 : Deployment of data an a lytics in a financial service organization. 47 Figure 6 : The technology used by data analytics 48 Figure 7 : The rank of the financial service enterprise platform on time-taken. 49 Figure 8 : The rank of the financial service enterprise platform on cost-taken. 50 Figure 9 : The tendency of a client Change request. 51 Figure 10 : Customer Experience and operations of a financial service enterprise 53 Figure 1 1 : The components of Customer Experience. 54 Figure 1 2 : The opportunities for application of business intelligence in financial services. 59 List of Tables Table 1 : Customer Satisfaction Analytics on Delivery of Customer Experience 55 Table 2 : Percentage of data sourced externally 56 Table 3 : The extent to which big data technology is applied in the financial service institution. 57 Table 4 : Challenges to Productionisation data. 58 I APPLICATION OF BI IN FINANCIAL SERVICES Chapter One Introduction Overview The study's main objective was to investigate how the Fintech industry can be improved using current trends and methods such as machine learning, artificial intelligence, and DBMS to leverage first, second, and third party data. The following objectives guided the study: Establish how business intelligence can be improved in financial technology, establish the extent to which business intelligence can be applied in Chime financial services and be improved, and lastly, identify some of the challenges to Productionisation data within financial services and fintech products. The study adopts a quantitative descriptive research design. This chapter presents a summary of the survey, a conclusion, and the recommendation of the study based on the study's findings. Background and Problem Statement The global financial technology market is growing. However, with new startups blooming with venture capital and the big bank's emergence, competition in the sector is increasing rapidly. Nonetheless, the nature of financial technology (fintech) makes it at the forefront of innovation. The innovation in the industry is faced with an ever-changing landscape with a set of unique challenges and obstacles. One of the challenges is to battle against the spending power of organizations such as gigantic insurance and financial banks and their willingness to market far and wide to achieve attention from their customers. Hence, the stakes are enormous, and the margin for error is insignificant (Dawuda, 2021). The purpose of business intelligence is to provide solutions that mine and analyze a company's data. Consequently, business intelligence provides actionable insights to the user. The insights from business intelligence help improve the operations of fintech companies (Ouko, 2019). Hence, it contributes to capitalization on opportunities and prevention of severe financial and regulatory risks through the support of swift data-driven decisions. Among the reasons for a fintech company to embrace business intelligence is to understand their customer needs better. Fintech is changing how people protect and grow their money through Robo-investing platforms and digital banking solutions. However, fintech margins for each transaction are thinner than conventional banking facilities. Consequently, fintech companies need to look for effective performing services to encourage more transactions from current customers and access a more significant market share. Additionally, fintech companies produce more data, such as customer behavior, as the customer base increases. Hence business intelligence seeks to track suspicious activity in the platform of a fintech company. It allows a fintech company to detect potentially fraudulent activity, which has regulatory issues such as money laundering. Hence, business intelligence allows a fintech company to understand key risk indicators and user behavior metrics, such as frequent transfer from various accounts into a single account. Chime is a financial technology company that helps people lead better financial lives and automate their savings. Its main objective is to provide essential banking services in an easy, free, and efficient manner. The customers benefit from a saving account, a visa Debit Card, and an FDIC-insured deposit account (How Fintech Startups Drive Financial Inclusion - An Empirical Study, n.d.). Its model does not rely on monthly services or any other consumer fees to deliver a personalized mobile-first banking experience. Instead, Chime exploits data analytics to personalize all the aspects of the mobile application. For instance, customers can find over twenty-four thousand fee-free ATM locations nearby through Chime's personalized mobile feature (Bragen, 2018). Chime uses Snowflake's data warehousing architecture. Consequently, it can analyze patterns in the usage data for the mobile fee-free ATM locator. In addition, the Chime can improve the functionality of the mapping feature to have the map shows associates to the individual's member's location. The outcome is a user-friendly experience that can locate nearby fee-free ATMs. Chime incorporates countless services to its leading-edge technology infrastructure. Chime identifies ways to improve the member experience by analyzing back-end, web server platforms, and mobile while delivering value. In the past, it was complex and cumbersome for a business to examine member engagement and other critical business metrics. The main reason was that the data required to be analyzed from a large set of services such as events from third-party analytics tools and ad services from Google and Facebook. This problem made Chime look for a solution that could gather data into a single location for analysis; this would improve the services that Chime could provide to its customers. To do so, Chime would need to migrate to business intelligence, which would offer improved performance and query data using the standard SQL. The study looks at how financial service companies utilize big data, integrate artificial intelligence, and apply business intelligence methods and tools to improve the fintech industry. Purpose of the study The purpose of the study will be so useful to the financial service companies who wanted to survive in the marketing competing the other Financial banks like JP Morgan Chase, Bank of America, Wells Fargo. This research finding the also provides insights and help the company to take lead and decide if actively on the next step on how to improve the company by leveraging the Business intelligence capabilities. The study also provides more information on how to beat the other companies and increase their revenue. The study also tells us on how to improve and get more attractive to the customers. Significance of the study The research finding will be precious to financial service enterprises that are considering adopting business intelligence. It will provide resourceful knowledge on how financial services enterprises can better understand the needs of their customers. The study also highlights how financial services enterprises using traditional techniques deal with data. Similarly, the study establishes how business intelligence can transform data into insights and allows decision-makers to take fast action with confidence. Additionally, the study also enriches other academic knowledge repositories on the existing trends and practices on adopting financial services globally. Also, the study will provide information on how current trends such as artificial intelligence, machine learning, and DBMS leverage first, second and third-party data. Research Problem The advancement of financial technology has created a lot of buzz from financial service enterprises to their consumers (Osman & H., 2013). However, Productionizing data within financial services and fintech products presents difficulty in achieving a systematic approach due to its complexity. As a result, it is not easy to achieve seamless integration and system-based thinking around integrating technology, processes, data, models, and people. The main objective for financial services enterprises to produce data is to shrink the time between business and data discovery continuously (Contributor, 2018). However, many financial service corporations are faced with challenges that slow down their ability to generate data-led insights; This is contributed by the limited flexibility and agility of legacy architectures, which helps prepare data for increasingly machine learning and effectively for analytics. Consequently, non-traditional data are getting adopted into more financial services enterprise's decision-making. Accordingly, financial service corporations must aim to democratize access to artificial intelligence and machine learning (10 Key Benefits of Business Intelligence (Every Organization Needs It), 2020). Big data is the use case for business intelligence. For instance, the AI services are integrated with applications to address everyday use cases such as document processing, personalized recommendations, and identity verification. The fintech should be made to understand the meaning of the data that is to be productized. Hence, every single version of the data is treated as a single source of data. Additionally, fintech lack ways of handling the data without implying the structure and link the data to unstructured resources such as images and multimedia. Additionally, collecting data from various sources remains a considerable challenge for most global financial services enterprises. Although there are instances where the most effective Productionisation of data is worked on across the data value chain, specifically focusing on scalability, trust, and privacy, it is a significant challenge to collate all the available data sources (Zhao et al., 2014). Many financial service enterprises desire to solve this issue due to increased pressure from fintech and consumers to innovate data while pursuing profitability and an increased number of legacy systems not supported by data interoperability. However, it is still challenging for many financial service companies to adopt new data sources such as biometrics and media, deal with toxic data, collate the legacy data and plug in the gaps with systems. Many financial service enterprises are adopting machine learning to obtain the social media data of consumers. However, preparing data with consent presents a significant challenge to adopt these technologies to provide an accurate illustration of the consumer's behavior (About Us, 2018). Therefore, the research question of this study is: How can the first, second and third-party data be leveraged using the current trends and methods such as big data, AI, Machine learning, DBMS, Visualization to improve the Fintech industry? Research Objectives The study's overall objective is to establish how the Fintech industry can be improved using current trends and methods such as machine learning, artificial intelligence, and DBMS to leverage first, second, and third party data. The specific objectives of the study include: 1. Establish how business intelligence can be improved in financial technology. 2. Establish the extent to which business intelligence can be applied in Chime financial services and be improved. 3. Identify some of the challenges to Productionisation data within financial services and fintech products. Theoretical Review Resource-Based View Theory The main idea behind the resource-based view (RBV) is that the resources of a financial service enterprise are necessary for the success of the company's competitive advantage, which can also be interpreted as a grander long-term performance. Therefore, an organization is likely to achieve a competitive advantage due to participating in activities that go a long way to improve the efficiency that other competing enterprises in the same industry do not offer (Woerner & Wixom, 2015). Similarly, an enterprise can achieve sustained competitive advantage when competitors cannot imitate or copy its strategy. Hence, the theory suggests that an enterprise differ based on the resources they possess. Accordingly, the difference does not change over time. A resource refers to an intangible or tangible thing that an organization can exploit, while capabilities are concerned with absorbing and using the resources. The theory states that not all resources are strategically relevant; for a resource to provide a sustainable competitive advantage, it must be valuable, rare, and non-substitutable. An organization, therefore, can absorb and apply them, which is considered a capability. Therefore, this study adopts the RBV theory primarily on the way an organization can manage its resources. The study focuses on the behavior of the organization as a result of an impact by external resources. Business intelligence is a crucial resource to financial services enterprises since it informs top management decision-making. Consequently, data, primarily focusing on customer data, acts as an essential resource to financial service enterprises. Data analysis segments in many organizations are under the Research and Development (RnD) Department (Arruda & Madhavji, 2017). Financial service enterprises can leverage data since most activities they carry out depend on customer data to achieve enhanced performance and competitive advantage. Hence, data analytics on customers provides a financial service enterprise an option to use their resources to fulfill the customer's needs. Consequently, it enhances customer satisfaction. Limitations of the study It was challenging to convince the respondents to share their details in the questionnaire as it was highly confidential. Additionally, some respondents may have been dishonest or biased; this can be attributed to the non-disclosure policies in some financial services enterprises. Similarly, the scope of the study was limited as it only looked at the application of few technologies concerning leveraging data in the fintech industry: machine learning and artificial intelligence, and big data analytics. Hence most of the interpretations were based on these trends and methods. However, it does not generalize the adoption of other technologies such as blockchain, augmented reality, and hybrid cloud. Consequently, further research should focus on expanding the list of these technologies to achieve broader results. Assumptions Currently, fintech companies can maximize the latest technology to boost their financial systems. It presents an opportunity to startups globally to provide financial technology such as alternative lending and wealth management, unlike previously, whereby small lenders could not afford to secure loans. Hence, the sector is experiencing an exponential growth rate, and the competition is getting stiffer since various technologies can delve into innovations in departments such as investment and financial literacy. Hence, it is essential to adopt business intelligence, massive data analytics, experience various benefits associated with it, such as carrying out audits to meet the compliance standards of financial regulators. Hence, it is recommended for a fintech company to integrate business intelligence in the following ways: 1. Customer retention- application of business intelligence is effective in garnering data and retaining customers. A financial service company can explore data from social media, which is a reliable source of valuable insights. Additionally, business intelligence allows for the company to achieve both long-term and short-term goals. For instance, through creating personalized services and products, customer engagement increases, increasing the return-on-investment (ROI). 2. Risk Management- both small and big-sized financial services companies need to keep their data secured. Hence, the organization can take a proactive approach to protect themselves and their customer from fraud. They can adopt big data analytics, which allows predictive analysis. It is a viable tool for preventing fraud risk. Hence, business intelligence supplies algorithm with unprocessed data and trains them to detect irregular patterns. Predictive analysis is famous for risk management solutions since it makes use of biometrics. 3. Customer acquisition- a particular type of extra service or product, which a specific client may be interested in can be determined through big data analytics. Additionally, robust customer profiles can be accessed from the public and internal data. Hence, financial services can attract more customers and forge customized offers through sectioning the target audience according to identified parameters. Accordingly, employing digital channels reduces the cost of acquisition. 4. Credit Scoring- a financial service institute business operations are evaluated to assign an appropriate credit score. Initially, the process relied on basic financial transactions were considered. The traditional method failed to consider particular factors such as client behavior and ability, which are evaluated through business intelligence. 5. Customer service- financial services enterprises and fintech companies aim to deliver quality services to their customers. Currently, the consumers are willing to share their information as long as they are protected from any third party. The data from the customers is used to enhance the range of services provided. Hence, it is efficient to adopt artificial intelligence and big data analytics since they are trained to generate many ideas. The results help improve the overall customer experience. Therefore, a financial service sector will not require an agent since financial advice online by artificial intelligence-driven robotic advisors is on the rise. Hence, financial service enterprises and fintech companies should adopt business intelligence to release products and services tailored to consumers' needs, preferences, or complaints. Hence, many fintech trends that are currently being adopted will remain relevant. Studies predict that more financial services enterprises will begin to rely on advanced blockchain software to handle digital payment. Similarly, another trend to anticipate is the collaboration of fintech companies and conventional financial service institutions. A financial institution can forge creative solutions by integrating artificial intelligence and big data, particularly in minimizing the fintech industry's risk and simplifying the whole process. Definitions Productionsation is the process of turning a prototype offer design into a version that can be more easily mass-produced. Fintech computer programs and other technology used to support or enable banking and financial services. Chime is an American financial technology company which provides fee-free mobile banking services provided and owned by The Bancorp Bank or Central National Bank. Leverage is an investment strategy of using borrowed money—specifically, the use of various financial instruments or borrowed capital—to increase the potential return of an investment. Customer is a person or organization that buys goods or services from a store or business. Summary Humankind is said to have dealt with data ever since the first enterprising accountant in ancient Mesopotamia. Similarly, the data revolution in the financial technology industry is promising with new inventions and technologies. However, despite financial services enterprises being well acquainted with data, it a significant challenge to exploit the data to give actionable insight, drive innovation and growth and learn about trends. Since the late 1970s and early 1980s, the financial institution has relied on highly skilled technical specialists to make sense of data modeling. Hence, only the big players in the sector could undertake analysis. However, they still faced the same challenges today, such as a need for a rapid market, commoditization of traditional services, and a competitive landscape. Currently, many financial service institutions can connect and analyze data to drive better business decisions. Therefore, the study looks at the application of business intelligence in the financial service sector. The main reason for choosing the topic is that business intelligence is associated with the advantage of presentations through visualizations and dashboards; they are said to enable entry-level understanding to deeper analytical trends. Application of business is a value-addition to a financial service sector since it analyzes the results in moments previously used to take weeks. Similarly, the study looks at the application of data analytics in leveraging data. Data analytics is considered to leverage the data by creating stories, which build statistics-driven and transparent cultures. Additionally, data analytics makes conversation on what is helpful for the financial service enterprise and frees the stakeholders to focus on other things. Consequently, the adoption rate of analytics and business intelligence is on the rise. Many financial service companies adopt advanced analytics to interact with big data to operationalize findings from massive data sets. Many larger organizations use big data analytics but sparingly. However, the study shows that adopting both analytics and business intelligence at the center of the financial service institution is a great asset in the near future. Currently, many customers are demanding greater integration with fintech and digital solutions. The number is expected to grow. Statistically, approximately 66% of users under the age of 25 regularly use fintech applications. Therefore, this forces many fintech companies such as Chime to address where they lie when it comes to deploying actionable data analytics platforms. Additionally, financial technology companies need to address what their clients are demanding from them to insights. Therefore, this results in the study investigating the impact of leveraging data on customer experience. Another question many financial service enterprises need to address is whether it is better to buy, acquire, or build these technologies. Additionally, the cost of the deployment, time, and the cost of protecting the financial institution from plunging is directly associated with implementing these technologies. Similarly, it is also essential to consider whether shifting data analytics to machine learning and artificial intelligence represents a solution to traditional problems. Further, it is necessary to evaluate how the financial service sector has been by the digital acceleration. The study focuses on the data and its relationship with the fintech industry. Chime's marketing efforts have recently been proven to be very successful. Therefore, this has increased the interest of the many as to what the fintech is doing right. Although Chime has invested heavily in basic strategies such as website design through its featurization strategy of showing the site visitors what products they offer, they have explored the topic of our interests: machine learning and artificial intelligence. According to the CEO of Intellimize, Inc., Guy Yalif, Chime uses artificial intelligence and machine learning to forge a sophisticated testing approach. The test is used to determine the marketing tactic the company deploys. However, much of Chime's success can be attributed to its snowflake warehousing architecture, which can analyze patterns in its consumers. The native integration of Snowflake is the Looker Business Intelligence tool. Compared to other big data platforms such as Hadoop, Snowflake puts JSON …