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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’s 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 of 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.

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 (Nicoletti, 2017). 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.

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.

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.

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 (Morgan, 2017). 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 production 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 services 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?

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’s 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 Production data within financial services and fintech products.

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.

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.

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

Productionisation 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.

Humankind is said to have dealt with data ever since the first enterprising accountant in ancient Mesopotamia (Rana, 2019). 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 (Reed et al., 2016). 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 (Jun, 2020). It is 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 (Jun, 2020). 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 (Morgan, 2017). 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 (Contributors, 2018). 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 IntelliMax, 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 (contributors, 2018).

However, much of Chime's success can be attributed to its snowflake warehousing architecture, which can analyze patterns in its consumers (Contributors, 2018). The native integration of Snowflake is the Looker Business Intelligence tool. Compared to other big data platforms such as Hadoop, Snowflake puts JSON data to simplify its data pipeline and work faster. There are many advantages of the adoption of Chime's Snowflake's data warehouse architecture. For instance, it can deliver structured and semi-structured data from different sources. It achieves this functionality easily without significant transformation effort. Hence, it allows near real-time for queries made to the system.

Additionally, the architecture enables on-demand scalability. Accordingly, Chime can scale to its desire or stay to keep its costs under control. Lastly, Snowflake is flexible, which makes integration with other tools such as Tableau quick.

Additionally, the Chime team adopts a familiar SQL instead of other platforms with complicated languages to analyze data. The ETL process of Chime pulls data from various sources, about 14 different sources including other databases, without difficulty (Contributors, 2018). Similarly, any prospective solution to the company is integrated with its various web-based data sources and various APIs. Adopting snowflakes allows Chime to take data from many sources and snowflakes instead of creating the whole stack in its environment. The different advantage of Snowflake allows Chime to achieve a significant JSON performance (Contributors, 2018). Hence, by Snowflake providing data, Chime achieves faster evolution of personalized banking services to its clients.

The study suggests that customer satisfaction analytics influences the delivery of customer experience. Customer satisfaction analytics allows businesses to recognize the needs of its customer. Therefore, it will enable the financial service company to adjust its price points for its services. This actionable insight allows the financial institution to experience optimal returns by fulfilling particular needs for specific client groups instead of general approaches (Lafreniere, 2019). Hence, data analytics allows for financial services to improve its customer acquisition, improve its products and services and reduce costs to retain its clients. Accordingly, the study findings suggest that data analytics is necessary to many financial services enterprises looking to deliver a more remarkable customer experience and compete favorably (Lafreniere, 2019).

By optimizing the data on the customer, financial services institutions can boost customer value. Financial service enterprises can achieve it by improving their business process and aligning themselves towards achieving customer-centricity. The study findings suggest that data analytics is a significant asset for financial service institutions looking to compete favorably. It enables the company to identify the needs of its customer and allows them to adjust their prices to attract more customers.

Customer loyalty is vital for a financial service institution to survive and succeed (Indriasari, 2019). For instance, in retail financial service, customer satisfaction and the perception of service quality influence customer loyalty. Hence, financial institutions must explore and understand customer experience and journey over time. This is an efficient approach to enhancing customer satisfaction and improving service quality (Ouko, 2013). Additionally, it increases the focus on customer experience since they can interact with the company through various channels and media. Artificial intelligence and big data analytics technologies are crucial in leveraging customer experience (Maskey, 2020). This is achieved in multiple ways, such as:

1. The technologies enable financial services enterprises to analyze on entire customer experience and journey on each database channel.

2. The technologies help establish patterns of usage, which generate sales and analyze the poor performance of database channels. Such knowledge is helpful for financial institutions to create a strategy to optimize each database channel.

3. The technologies deliver memorable moments, which leads to improving financial performance, loyalty, and satisfaction.

Big data expose financial service enterprises to more business opportunities. Hence, they can potentially gain a more holistic view of customers and the market (Najafabadi et al., 2015). Big data analytics result in an advantage to the financial institution in various aspects such as fraud detection and accurate customer analytics. Similarly, the combination of Artificial intelligence, big data analytics, and machine learning benefits a financial service industry in aspects such as availing more valuable information for the company to achieve more intelligent systems. Ultimately, the financial service institution gains a competitive advantage over its competitors.

The study objective was to establish the challenges to Productionisation data associated with machine learning and the application of business intelligence in financial service. The study findings suggest that the assessed challenges affect the financial service company at different levels. One of the biggest challenges found was insufficient quality data. It would need over a million relevant files to train a machine learning model on top of the data. Additionally, it cannot be any data. Predictability risks and data feasibility must be considered. However, it is even more challenging to set up relevant data sets and assesses whether they are fast enough.

Another problem is getting the contextual data. Similarly, having the data is also not enough (5 Challenges to Be Prepared for before Scaling Machine Learning Models, n.d.). The machine learning team will need to start with a non-data lake approach and train the machine learning model on top of their traditional data warehouse. Data scientists can spend about 80% of their time cleaning and managing the data rather than training the model when handling conventional data systems (Najafahadi et al., 2015). Also, the data cataloging and solid control system is necessary to have the data cataloged well and shared transparently for it leveraged again. Hence, that is why the study findings indicate poor quality of data to be a significant challenge. Data complexity makes the cost of maintaining and running a machine learning model relative to the return diminish over time (Najafabadi 2015).

Hence, it becomes less profitable to the financial institution in the future. Machine learning models heavily rely on maintaining transparent communication across DevOps, data science, and other relevant teams (Data Management Challenges for Financial Services, 2019). It is complicated to assign roles. Similarly, it also difficult to give detailed access and monitoring each group (How Fintech Companies Can Leverage Big Data Analytics, 2018). Identifying risks across various areas require an overdose of communication and strong collaboration. Hence, financial services institutions will need to keep data scientists deeply involved since they decide the future of the machine learning model.

Additionally, it is challenging to engineer a machine learning system since the technical stacks and infrastructure need to be completed in the future resilience and use case even after the data is made available. The machine learning space involves a wide variety of technology. Reducing these challenges is crucial to establishing a successful machine learning model (Najafabadi, 2015). This is achieved by standardizing various technology stacks in different areas while choosing each one. Other datasets and modeling need to be well integrated to allow a scalable production environment. It is challenging to incorporate operational systems and different teams (Schoenherr & Speier-Pero, 2015). A production to be possible, complicated codebases will require to be made into well-structured systems. The team can get stuck at any stage if they do not include a standardized process to take the model to production.

Integrating different information and data into the workflow system and the test requires workflow automation. The entire ecosystem can fail and need to be fixed if the model is not tested at the right stage. Hence, integration can be an absolute nightmare when the technology stacks are not standardized. Notably, without integration, the machine learning model ceases to provide value to the financial service enterprise during crisis or business environment changes (Orenga and Chalmeta, 2018). Similarly, it is difficult to test the model; however, it is valuable like other production processes. The Productionisation cycle is closed by monitoring the model performance, retraining the model, and running health checks. Additionally, a proper management tool for the machine learning lifecycle is necessary to check for ignored or invisible issues during tests.

Chapter Two

This chapter covers a review of the application of machine learning, Artificial Intelligence (AI), and big data in financial services enterprises. Big data is used with machine learning applications in many different areas such as financial stability, monetary policy, and research. The chapter also explores the application of big data analytics and AI to leverage customer experience in a financial service enterprise. Additionally, the chapter explores how to leverage data to improve customer experience and customer analytics. Additionally, the section focuses on the role of customer data analytics in strategic decision-making.

Business intelligence plays a critical role in generating helpful information among most companies and making better decisions. Notably, business intelligence represents systems and tools, which help provide companies with informed choices (Jun, 2020). An organization can gather, store, analyze and gain access to data through these systems, which will be used in decision making. In essence, business intelligence makes it easier for data interpretation, while business intelligence applications transcend from strategic to operational (Papachristodoulou et al 2017). Operational business intelligence is helpful in the everyday running of the organization, while strategic business intelligence is on an occasional basis. In addition, business intelligence technologies provide functions such as statistical analysis, predictive analytics, and benchmarking.

Many non-traditional players give unrelenting pressure for financial services to transform digitally. Consequently, data management trends such as user demands, ubiquity, and data volume are necessary for insurance companies and banks to become data-driven enterprises. The availability of data in all media outlets has driven financial services companies to capture (Jyoti, 2020). Financial services companies focus on data associated with product and services purchase histories, customer information, and financial transactions. The user demand for data is increasing (Vargas et al., 2017). Hence, many financial services enterprises need the data for an analytic process such as decision support for the employees to base decisions on empirical evidence instead of intuition or gut feelings. Additionally, it should allow for the employees to trust in the security and accuracy of the data.

Consequently, an increase in the volumes and data source result in new challenges. The issue arises with a complex enterprise data management landscape that consists of thousands of silos (Ates, 2017). Often, many companies resort to deploying multiple data warehouses, online apps, analytics solutions, and operational applications. Most of the data can be found in the cloud, hybrid environments, and on-premises. Many companies choose to combine their existing and new data into a single data to reduce complexity. Additionally, financial companies enhance efficiency, growth, and automation through single data, which helps deliver insights.

However, single data is still an aspiration for many financial services enterprises because most of the data resides in multiple siloed environments. Hence, the data cannot meaningfully be connected across these silos. Consequently, they become inaccessible, leading to a compromise of insight into products, sales, customers, and financial performance (Weichert, 2017). As a result, many financial services corporations choose to build large enterprises to overcome multiple data silos. In addition, a result of the increasingly changing and growing data sources has made traditional enterprise data warehouses obsolete. It hence cannot keep pace with the analytics needs of the company. Some of the reasons include:

1. The increasing number of internal customers demanding new insights and analytics leads to slow and costly responses to business needs.

2. Solutions cannot keep up with the growing new data types.

3. Production of analytics and data capture processes in batch creates for solutions that cannot deliver real-time insights.

4. Users with no insight into data origins, consolidation across multiple data marts, and data replication make data linage challenging.

These challenges are worsened by the increasing number of analytics solutions that need real-time data access for decision making, business processes, and data consumption endpoints. Consequently, financial services enterprises need to address two types of data considering the data management system challenges (Awotunde et al., 2021). First off, enterprise data is characterized as security concepts, high quality, structured data management, and lifecycle management policies (Ren et al., 2019). In addition, enterprise data include financial transactions and contractual agreements. Enterprise data is primarily captured in relational database management systems. Secondly, big data typically consists of large volumes of unstructured or semis structured data—for example, text files like social media streams and emails, video and audio files. Often, big data is captured in data lakes such as cloud object storage systems (Vargas et al., 2017).

The data lakes are intrinsically cheaper than conventional database management systems (DBMS). However, these platforms lack similar enterprise governance, lifecycle management, and security (Maskey, 2018). Notably, many existing data management landscapes do not establish a connection between big data and enterprise data. As a result, it complicates the deriving valuable advantage of data-driven analytics and operationalizes data science. Consequently, this contributes to difficulty in looking for massive haystacks of data to achieve actionable insights. This problem leads to many financial service enterprises not delivering data-driven innovations, which is the core element of digital transformation.

Like open banking, the Internet of Things (IoT), and the cloud, big data has become a catchphrase in the financial services industry. Big data focuses on the collection, processing, and analysis of an enormous set of collected data (Rana, 2019). Business intelligence is associated with big data through advanced analytics concerned with potential events prediction and behavioral research (Artificial Intelligence & Machine Learning in Finance: The Whys, the Hows and the Use Cases, 2020). Additionally, financial services enterprises can anticipate the consequence of the changes that negatively impact the insight in business strategies (Najafabadi et al., 2015). Therefore, the business intelligence agent uses predictive analytics, performs big data analytics to a specified level in marketing, mine the data and finance.

The combination of business intelligence and big data leads to more innovative thinking and solution. Consequently, companies can collect a vast amount of data every second. The data collected include the services and products that the organization delivers, sales and customers (Nicoletti, 2017). Frequently, a considerable amount of data moves rapidly and are too big for traditional technologies to handle. The duet of big data and business intelligence improves the processing of organizations. Additionally, it also enhances the demand for better profits. The combination of big data has contributed to the revolution of numerous industries.

The exquisite performance of particular tasks by artificial intelligence compared to a human being has resulted in integrating financial services, precisely when raw unstructured data is involved (Alt et al., 2018). Notably, Machine learning is a subset of artificial intelligence. Machine learning is attributed to financial service needs as it processes data and automates the learning used in particular financial tasks (Schlobach & Knoblock, 2012). Artificial intelligence and machine learning potentially contribute to Robo-advisor for personalized wealth management, fraud detection, consumer behavior forecasts, and corporate finance by enhanced customer experience and reduced costs; this contributes to delivering tailored services helpful in making informed marketing decisions.

Artificial intelligence (AI) creates a significant difference in the analytics field as it helps democratize data and enhances adoption. AI allows financial service enterprises to distribute and organize massive data efficiently. Additionally, it facilitates the way to understand big data accurately (Top Chime Competitors and Alternatives, n.d.). Artificial intelligence applications and methods such as machine learning and predictive analytics have contributed significantly to the new product within business intelligence. Consequently, insights are more understandable and accessible to novice users. However, with an upgrade of artificial intelligence, it assists financial service corporate leaders to rethink their outlook and strategies.

Financial technology (fintech) can produce about 2.5 quintillion bytes of data daily as new opportunities for data emerge (Putra, 2021). 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. Many financial service corporations attempt to harness the power of data analytics to gain value and exceed their competitors. Adopting valuable business intelligence allows an organization to stay ahead and be part of innovation as the competition becomes aggressive in the current markets. Therefore, it is essential to understand how business intelligence has evolved into a common practice in the contemporary business world (Varga, 2017). For example, financial services have become automated. Current trends such as advanced analytics, blockchain, and Artificial Intelligence (AI) pressure traditional skills while forging transformative opportunities for individuals and financial service enterprises.

There are many AI-driven Business applications such as visualization tools, data mining, dashboard, and predictive analytics. Such applications are helpful in automatizing financial service activities to achieve efficiency. For example, the reports can be shared with suppliers, and sales and inventory can be tracked in real-time. Many financial services companies have understood the essence of attempting to enhance business practices and leverage data-driven insights (Thimou, 2020). The current data applications in business intelligence have already begun to automate many repetitive tasks. Consequently, the present data applications business intelligence is shifting to more complex functions with AI assistance. This has contributed to further impact on the strategic decision-making within financial service corporations.

According to recent AI statistics by Deloitte, approximately 70% of the respondents offering financial services are using machine learning for fraud detection and cash flow predictions (Maskey, 2018). Financial Technology (fintech) exudes an excellent example of a practical implementation of machine learning and artificial intelligence to improve decision making, process automation, and minimize operational costs. In finance, machine learning aims to improve the way financial service companies deliver service (Huang et al., 2020). Additionally, machine learning changes how financial service corporations' clients receive them. Machine learning and artificial intelligence influence the individual customer's experiences globally.

Since the financial technology industry is built on big data, the benefits of machine learning are ideal for finance (Breidbach et al., 2020). Hence, with a valid data set to a machine learning algorithm, financial service enterprises can tap into a deep pool of machine learning opportunities and artificial intelligence for fintech. Some of the challenges that financial service enterprises face while implementing AI solutions include cost. It is costly to implement artificial intelligence in finance. Additionally, an inadequate resource is a barrier to AI implementation because low tech and human resources can be detrimental (Jyoti, 2020). Access to top talent and tools is as much important as having money. As a result, artificial intelligence implementation is associated with financial risks such as low Return on Investment (ROI) even when the business has the necessary funds to invest.

There are so many reasons for financial service companies to adopt machine learning. There are various ways enterprises can leverage artificial intelligence and enhance processes in different economic fields (Alzubaidi et al., 2021). For instance, fraud detection, which is most widespread with machine learning applications and artificial intelligence. Fraudulent activities can be detected and neutralized through analyzing the online transactions histories and behavior of the clients. Additionally, artificial intelligence allows for accurate trading decisions based on big data. For example, a machine can place bids in a fraction of a second, which is valuable because of swift market changes.

Artificial intelligence can complement fintech with optimized processes, work efficiency, and better customer experience. Finance provides a great learning environment for artificial intelligence. Hence, it provides datasets for the machine learning can process. New models of implementing financial machine learning models are predicted to arise in the future. For instance, fintech will expand its dependence on machine learning algorithms. Hence, future finance will be optimized mainly for operational efficiency by using the possibilities of artificial intelligence (Morgan, 2017). The complex diagnostic engines are expected to work for the benefit of a customer as the machine learning algorithms become more advanced. Financial service enterprises will require increasing user acceptance as the demand for personalized and humanized approaches rises to keep up with current trends. Consequently, regulatory frameworks most probably transform, leading to expanding machine and artificial intelligence in financial service enterprises.

Artificial intelligence (AI) in financial service technology is growing from machine learning-driven risk management and automated financial advisory to investment and intelligent Robo-advisors (Maskey, 2018). Chime financial services company attempts to leverage predictive personalization to produce more accounts. Artificial intelligence is used to enhance every step of a customer's journey ranging from prospecting and marketing to personalizing and customizing the experience (Silva, 2019). The financial service industry is mainly faced with the challenge of distrust from potentially profitable customers. Hence, many financial service leaders recognize the problem's urgency and adopt personalization to rebuild the relationship.

The emergence of artificial-driven personalization has been based on no coincidence since financial service business leaders acknowledge that a significant customer relationship helps forge a competitive advantage (Shukla, 2018). Apple and Amazon companies are examples of big-tech players who have taken advantage of an excellent customer relationship; hence it has led to most of these customers expecting a high degree of personalization.

Chime financial services company performs predictive personalization exceptionally well in driving a significant increase in new account signups (Contributor, 2018). The main objective of predictive personalization is to enhance the better experience for the prospects and customers at prominent financial services companies (Shevlin, 2020). Predictive personalization technology in an online environment can observe user behavior, illustrate the best performing version of a website to a specific individual and automatically identify segments. In addition, the tools of this technology give results faster with less work than other website optimization approaches; This means that the predictive personalization technology replaces the previous technologies on websites such as rule-based personalization systems and A/B testing.

The main objective of Chime digital bank is to assist individuals in forging healthy financial habits. The marketing team managed the new account goals at Chime. Consequently, Chime decided to deploy a predictive personalization system to optimize its website to achieve its strategic goals (Contributor, 2018). Hence, Chime began with a simple initial set of tests involving new body content such as an overview video introducing their product and demonstrating its mission, comparing fees with other banks, and automatic savings to assist individuals in achieving healthy financial lives. In a nutshell, Chime's marketing team performed about twenty-one different ideas. Chime Company would have taken approximately nine years to accomplish in three months had they used the A/B model to test out their twenty-one ideas (Contributor, 2018).

Chime was able to act with a more profound level of efficiency than many marketers normally would through the adoption of artificial intelligence (Pal et al., 2020). For instance, the use of AI made it possible for Chime to discover that the various version performed better in Illinois while another version created more new account signups. Resources such as human capital and budgets and time, partnering with the right people, and deploying the right technologies are usually involved in achieving the most out of predictive personalization. Personalized experience technology has significant benefits to financial services companies. More financial services enterprises are adopting AI better optimize their website; this drives more customer revenue and signups since they distinguish their experience from their competitors.

The study conducts a systematic review on the application of big data in a financial service enterprise. Big data refers to a data set that has a size, which is beyond the traditional means. Big data provides business and research value. However, it presents notable challenges in terms of ethics, management, systems administration, and stockpiling. Currently, many businesses areas are linked to big data. Big data has a critical effect on various dimensions of financial services companies. For instance, the enterprises' operational performance, advertising, business to business processes, marketing, and mechanical assembling measures. A study conducted by Falih recognized big data as an essential factor of HR cycles and business measure management, which is helpful in the decision-making process.

Additionally, a study done by Duan and Xiong (2017) suggested that big data is vital in the business examination. Big data is helpful in data management through a framework foundation (Lafrenière, 2019; Oussous et al., 2018). Data management incorporates the strategies to store, move, and catch and process the information. Moreover, business systems and business analytics should be linked firmly to improve investigation-driven experiences. According to Grover, eBay, Amazon, and Apple promote the connection between business analytics and business systems, enhance business tasks, and digitize transactions to appraise their market. Moreover, studies suggest that big data is also helpful in promoting business to business deals with consumers' information examination (Zuech et al., 2015). Consequently, the sales, relationship with consumers, and growth are improved by utilizing the client's enormous datasets. The study focuses on how big data is applied in financial services; however, limited studies are conducted on the subject. Hence, the research objective is to enlarge big data's contribution to finance.

Over the last decade, studies show explosive growth in the amount of readily available data. The advent of big data coincides with the development of software and technology such as artificial intelligence (AI) and machine learning used to analyze it. Hence, a researcher can find meaningful patterns in large quantities. The application of AI and online delivery eliminates human interaction and offers a unique customer experience that keeps costs low. Artificial intelligence and machine learning are expected to be the following significant drivers of fintech. Machine learning provides computer systems with the ability to find patterns and derive insights from data. Hence, Machine learning techniques such as deep learning and support vector machines are becoming popular with the advancement of technology, which can be used for performing predictive analytics and classifying financial data.

For example, the machine learning techniques are easily adapted to the financial setting that predicts whether a financial customer is likely to cancel financial service according to the customer's past use of services and demographics (Indriasari & Gaol, 2019). Hence, financial service enterprises can utilize such analysis to optimize their resources to prevent customer churn. Fraud detection is another example in which an analytical solution can automatically isolate financial fraud and be included in the operational process through historical data and machine learning. The training process and the algorithm automatically differentiate fraudulent activities from legitimate transactions by applying historical transaction data.

The dependence on machine learning techniques is contributing to getting more from pattern recognition in financial technology (Zhong et al., 2016). Financial services enterprises can effectively utilize these techniques by ensuring that the data is categorized appropriately, good quality, and cleansed for data analysis. Consequently, machine learning and artificial intelligence play a significant role in big data and analytics, especially examining data and feed into the machine learning algorithms. However, caution should still be maintained since there is still a lot of hype about AI and ML prescription and predictions. Any bias imposed by artificial intelligence and machine learning must be offset. Hence, the existing studies suggest that Artificial intelligence and Machine learning provide tremendous opportunities to advance financial technology further. These current trends and methods are evolving rapidly, and most of the solutions supplied by ML and AI are not easily explainable or understandable. Consequently, studies are still ongoing to make these "black boxes" at least translucent or transparent.

According to a study conducted by LaValle, he recognized the intensive application of big data analytics in the decision. The study stated that big data analytics and its related technology help a financial service institution forge a better understanding of their market and businesses. According to Kiron et al. (2014), big data analytics provides businesses with insights through the talent of the personnel, data management, and infrastructure. The study concluded that business intelligence by adopting big data analytics transformed many financial sectors into a competitive force (McAllister, 2020). A framework proposed by Elgendy (2013) further suggested that big data analytics tools and methods are combined in the decision-making process. The decision-making process was divided into four phases: intelligent phase, design phase, choice phase, and implementation phase. The intelligent phase is the first phase of the decision-making process. It includes:

1. Problems and opportunities are identified from data collected from internal and external sources.

2. The sources of big data need to be clearly defined.

3. Processing of data through the management and big data storage tools should be after the data sources are defined, and the types of data necessary for the analysis are identified.

4. When further data is necessary, it should be collected and stored from different sources; This is stored and sent to the user.

5. Arranging, preparing, and processing big data can only be completed through Extract Transform and Load (ETL) or big data processing tools.

The second phase is design, which is concerned with developing and analyzing a possible course of action (Watson, 2014). It is usually done by creating a problem representative model or conceptualization. The framework is divided into three in this phase: analysis, model planning, and data analytics. The third phase is the choice phase, which focuses on evaluating the proposed solution. Lastly, the implementation phase is concerned with implementing the proposed solution.

There are various reasons to use machine learning and artificial intelligence in the fintech industry (12 Use Cases of AI and Machine learning [ML] in Finance, 2020). They include:

1. It creates better revenues since it leads to better productivity and improved customer experience.

2. Contribute to low operational costs since it supports process automation.

3. Machine learning and artificial intelligence provide reinforced security and better compliance.

This section evaluates vital sources of information, which financial services enterprises can consider for potential market or operational insights. Often, data sources are treated in isolation (Rehman et al., 2016). The main reason is that combining multiple data sources can lead to an increasingly nuanced understanding of the data encodes realities. This chapter provides an overview of the most common alternative and traditional sources.

Some of the traditionally sourced data include transactional data, customer records, and primary market research. Additionally, most data such as credit are stored as a document, either hardcopy or softcopy. The centralized databases keep only the banking activity data and essential customer registration. Financial services corporations face the challenge of ensuring that the traditional data is stored in a digital format to aid data analysis. Consequently, how the information is collected needs to change or introduce technology to convert data to a digital format. However, it is excellent to digitize legacy data, even though new technology to digitize the traditional data is available.

I. Primary Market Research

This data is helpful in better understanding the market segments, seek customer feedback, understand customers, and track market trends. The market research can either be quantitative or qualitative, which can assist in knowledge of both how and why customers use certain products and services.

II. Client Agent Data

Financial services enterprises collect a vast amount of data on their customers during registration and loan applications. They range from business reasons to comply with the regulation. Additionally, they collect information about their agents in the process of application and during monitoring visits. The data collection on both the agent and the consumers includes variables such as location, gender, and income. Some of the data are discussed and captured during an interview, while official documents verify others.

III. Third-Party

An objective and verifiable data can excellently be sourced from the registries and credit bureaus. They allow credibility checks on the data reported by loan applicants. Primarily, they can reveal information that the applicant may not willingly disclose—online queries for relevant data such as public registries and credit bureau reports. However, the emerging market is a challenge since it lacks a fully functioning credit reporting infrastructure.

Alternative sources of data are achieved through computers, tablets, and phone which acts as sources of digitized data to provide insight into customer behavior and financial capacity. Such sources reveal how the person spent their money and time, with whom and where they spent it.

I. Social Media Profiles

Financial services enterprises are increasingly developing online and maintaining a presence on social media sites such as LinkedIn and Twitter. Online behavior data provide information on how financial service enterprises can play a role in customer lives, customer lifestyle, attitude, feedback, and goals (Reed, 2014). The data on social media networks include online web behavior such as location, sequence of a website, site, and data on traffic initiated and social connectedness. Similarly, social media indicates the socio-economic status of an individual. The public profiles from social media can be helpful in the verification of contact details and basic personal customer information.

II. MNO Call Detail Records (CDRs)

Mobile network operators (MNO) have access to call detail records and coordinates of cell towers (Provost & Fawcett, 2013). They are helpful to financial services enterprises in analyzing CDRs to accomplish targeted marketing promotions and campaigns to adjust pricing. The information can be matched with the cell tower signals to generate locations of customer activity. Customer behavior and usage can be predicted through combining MNOs offering mobile money services with access to call detail records data and the digital financial services transactional database for analysis.

III. Agent Assisted Transactional Data

An insight can improve agent network performance into understanding the locations and agents are most active. Hence, in many digital financial services, the agents are the core face to tracking agent and customer usage patterns. Such activity predicts the agent's performance and customer preference. The information may be directly recorded from transaction point computers or mobile phones.

A digital financial services enterprise operations require many business processes to run. Each department relies on multiple sources of data to complete tasks and meet performance targets. Some essential data sources include business intelligence (BI) systems reports, core system data, and technical log files.

1. Business Intelligence (BI) System Reports

It is typical for a business to create customized reports from raw data using simple tools such as Microsoft excel when the digital financial service products are new and a relatively low volume of data (“Advanced Analytics-Technology and Tools,” 2015). However, the business and data growth results in more complex analysis, hence making the process unmanageable. Many extensive financial service systems have a data warehouse that applies BI systems to draw multiple data sources, followed by basic reports and features that allow customization.

2. Core System Data

The core system provides bulk data. The transactional engine performs the management of transactions and interactions workflows and avails as much metadata and granular data as feasible to the relevant databases. Similarly, the transactional engine is responsible for the movement of funds, commissions and fees, commission splits, and tax rules. Additionally, the transactional engine also provides fully auditable workflows trails of non-financial activities such as balance inquiries, data downloads, and Personal Identification Number (PIN).

3. Technical Log Files

The technical log files are considered a rich source of data. More advanced digital financial services proactively use dashboards to ensure early fault detection and system health. It is typical for monitoring systems to have performance monitors and alerts to achieve valuable information (Hasan et al., 2020). Financial services enterprises only access the data if a specific forensic analysis lacks available and valuable data.

Since the customer expectations grow daily, they are dynamic and complex regarding how to satisfy them. Financial services are continually changing their strategies to keep up with the customer's expectations and compete favorably. Consequently, financial services enterprises amass a significant amount of data from their customers through various methods (Ouko, 2019). Exponential growth in data and continued advancement in communication and technology have resulted in a considerable shift in customer dynamics (Finextra, 2019). Such data enable financial services enterprises to improve considerably on their performance.

However, there are various challenges associated with making sense out of the data. These issues range from combining data from analyzing and extracting the patterns in the massive dataset to disparate sources. Data analytics tools enable financial services to change the data into meaningful information; this helps the enterprise improve customer experience delivery and compete favorably (Rana, 2019). The analytics tools allow the financial services enterprise to know and understand their customers well, hence placing them to be able to quickly design and implement services and products that address their desires. Financial services evaluate the big data through:

1. Identify how critical the collection and analysis of data to the financial services process and strategy.

2. Understanding how equipped the financial service enterprise to meet the growing data needs.

3. Identify how structure and integrated the financial services enterprise data is.

4. Lastly, it establishes how close the IT and financial services enterprise team work.

The top management in the financial services industry agrees on the essence of customer-centricity. However, the most important aspect is their understanding of what customer centricity means. There are various questions that financial services enterprises need to address to improve customer experience and achieve customer-centricity. They include:

a) What does the financial service enterprise really know about their customers? Do they have a product channel that meets its customer's needs?

b) Do they provide a multichannel experience, and does the customer trust them enough? Among many other questions,

Data analytics is essential to financial service enterprises in answering these questions effectively.

Financial service enterprises experience a wide variety and amount of data from their customers through various transactions. However, many financial services use a small portion of this data to make a decision, gain insights and enhance the customer experience delivery. However, customer data analytics surpass the conventional statistical methods of analyzing data (Grant, 1991). Previously, the data would be collected, and it would take long before the data is analyzed and appropriate measures are put in place in response to the day. However, today data can be leveraged through data analytics to provide real-time analysis; This enables fast decision-making in the financial service industry. Leveraging data through data analytics can improve the financial service process efficiency, fix enterprises' bottlenecks, and open new business opportunities.

Existing studies show that leveraging data through data analytics, trends, and methods such as Artificial intelligence and machine learning, affects customer experience delivery. There is a positive relationship between customer data analytics and the delivery of customer experience. Chime financial services company, for instance, makes good use of dashboards to aid decision-making in the delivery of customer experience. According to recent research, financial services enterprises make use of customer sentiment analytics for customer engagement programs to offer relevant real-time interactive content; this goes a long way to enhance financial services enterprises and their customers.

However, this study focuses on leveraging data in customer engagement practices and not particularly on customer experience. The study considers how data analytics can leverage data to improve customer experience based on Chime financial services (Sedkaoui, 2018). Consequently, financial services enterprises can take consumer satisfaction to another level since, through leveraging data, they will be able to respond to consumer feedback and opinions better than before. Similarly, the financial services enterprises will have a better understanding of the customer experience.

The existing research shows that in 2011, Bloomberg reported that approximately 97% of corporates that adopted sales data analytics had a revenue of over $100 million compared to the previous years' figures which were 90% (Arruda and Madhavji, 2017). This study, however, is limited since it does not capture how customer data analytics affect customer experience through Artificial intelligence and machine learning. Therefore, this report aims to solve financial inclusion, which lies in the advances in digital technologies such as artificial intelligence and machine learning, big data, cloud computing, and blockchain. Likewise, it can be achieved through mobile technology, which is widely adopted in Chime Financial Services with mobile-only banking.

The study examines how financial services corporates of all sizes in the market can connect with their customers to improve their experience and analyze data to drive better business decisions. Therefore, the study mainly focuses on how data can be leveraged in artificial intelligence technology and machine learning, DBMS, and Big to deliver customer experience.

Independent Variables Dependent Variable

Delivery of Customer Experience

Adoption of Big Data Analytics Technologies