Acknowledgement

I would like to thank my supervisor for guiding me in doing this assignment and has provided me with information to enhance my skills for research. I would also like to thank my friends and family to support me to understand the aspect of this research.

Abstract

This dissertation delves into the evolving landscape of financial technology (Fintech) in the context of big data analytics and its implications for mortgage lending. It seeks to investigate the potential synergies between big data analytics and the mortgage financial system, with a particular focus on predictive components. Through this exploration, we aim to contribute to a deeper understanding of the transformative role that Fintech plays in shaping the future of financial services.

Our research methodology adopts a secondary mixed research approach, employing both qualitative and quantitative analyses within a positivist framework. We utilize a deductive approach, a grounded-theory-based strategy, and a mixed-methodological approach. This methodological diversity allows us to gain comprehensive insights into the intricate relationship between big data analytics, mortgage lending, and the broader Fintech landscape.

The analysis and discussion chapter serve as a platform for presenting our findings. Thematic analysis reveals a discernible and positive link between big data analysis and the mortgage lending process, underscoring its potential to mitigate loan-related risks. Moreover, our trend analysis brings to the forefront Europe’s growing recognition of the advantages offered by financial technology. However, this recognition is accompanied by substantial challenges in implementation, which we address through strategic recommendations.

Our findings reflect a shifting paradigm in European territories, where Fintech is revolutionizing financial services by offering tailored solutions that empower individuals to manage their financial aspects effectively. Banks in countries such as France, Switzerland, the United Kingdom, Germany, and Sweden are embracing Fintech for its agility in data analysis and the rapid development of real-time solutions. Nevertheless, the integration of big data into financial activities necessitates the formulation of comprehensive digital transformation strategies to gradually replace or upgrade legacy systems. We propose an adaptive, modular approach to integration, enabling a seamless transition away from legacy systems.

This dissertation sheds light on the ever-evolving Fintech landscape, emphasizing its potential benefits and the need for strategic adaptability within the financial sector. Our research contributes to a broader discourse on the transformative role of Fintech and provides insights into its potential impact on the future of financial services, particularly within the context of mortgage lending and big data analytics.

Chapter 1: Introduction

1.1 Chapter Introduction

Financial technology or fintech is considered to be the organisations using new and advanced technologies to provide better quality financial services to their consumers. The objective is to compete with traditional methods and create a competitive edge in the business. This research paper is performed to determine significance of financial technology in shaping the future of mortgage lending and data analytics. Trends that are followed within the European marketplace in this context and challenges faced by companies here while using fintech are to be identified in this research.

1.2 Research Background

Fintech is defined as the practice of integrating advanced technologies into offerings by financial services organisations to facilitate their operations and services to their service users and purchasers. The purpose of fintech is to help in determining particular vulnerabilities and imperfections of the financial markets that are responsible for weakening the stability of financial prospects (Martino, 2021). System vulnerabilities that can create risks for consumers’ financial feasibility and undermine efficiency of the marketplace can be explored with the help of fintech (Murinde et al. 2022). The background of the research work is to explore these domains and detect ways in which these technologies can help in improving mortgage lending and utilisation of big data analytics in financial activities.

1.3 Research Rationale

Considering the data retrieved from Statista (2023), it is identified that the Fintech industry is evolving faster than ever now within the European marketplace due to technological advancements. The fintech market of Europe is valued at around USD 3,600 billion in 2023 as per transaction valuation. It is estimated that a 10% CAGR (compound annual growth rate) or more than that is to be registered during the period of forecast.

Figure 1.1: Growth of Europe-based Fintech Organisations

(Source: CB Insights, 2020)

Considering the above graphical representation, it can be stated that the European fintech market is growing substantially as per every passing year. As per the data retrieved from CB Insights (2020), the growth increased to $5.4 billion in 2020. Considering the potential of the marketplace to use financial technologies efficiently, it is necessary to indicate particular challenges that they face in the process or may face in future. The particular gap is related to having critical knowledge regarding functions and utilities of big data analytics to be used for mortgage lending. Without having essential knowledge and skills to manage technological integrations, successful outcomes cannot be assured. This research aims to provide essential data regarding these factors so that the knowledge gap can be overcome and banking activities can be improved. The concern is to make better arrangements so that the growth rate can keep on increasing and challenges can be overcome to ensure financial security, which is why this research is necessary. Data added here would help concerned authorities in using fintech potentially to ensure better business results.

1.4 Research Aim and Objectives

1.4.1 Aim

The aim of the research work is to explore ways in which big data analytics and its application can help in understanding predictive components within the mortgage financial system with focus on the European Market.

1.4.2 Objectives

• To identify the connection between big data analytics and mortgage lending that are beneficial for shaping financial technology
• To explore ways in which big data analytics can be utilised for comprehending various factors of mortgage financing which is identified to be the future of the Fintech industry
• To analyse the trends followed within the European marketplace in terms of using financial technology
• To evaluate challenges faced by the companies within the European marketplace on using Fintech to recommend strategies for further development

1.5 Research Question

1. How are big data analytics and mortgage lending systems connected together in terms of shaping the financial industry?
2. What are the ways of using big data analytics for comprehending mortgage financing factors within the Fintech industry?
3. How does the European marketplace utilise financial technologies to improve quality of services?
4. What are the challenges faced by Fintech organisations within the European marketplace in using advanced technologies for delivering financial services?
5. How can the identified challenges be overcome effectively to establish better circumstances?

1.6 Problem Statement

Financial technology is beneficial to be used for monitoring the fluctuating cost of searching for financial services. Big data has revolutionised the lending marketplace (Nguyen et al. 2022). However, there is a significant risk of credit management within the mortgage lending system. On the other hand, due to the significant knowledge gap regarding benefits of using financial technologies, many organisations fail to use it adequately. Issues associated with fintech and mortgage lending that this research intends to address primarily are related to having relevant knowledge and skills to use the advanced systematics, data security concerns, and financial risks. This research is conducted to overcome the knowledge gap and other risks associated with financial activities so that better circumstances can be ensured.

1.7 Research Significance

This research study is significant in terms of gaining knowledge regarding financial technologies and their contribution to the mortgage lending process. On the other hand, it is also significant for understanding fintech market trends in Europe.

1.8 Structure of the Dissertation

The complete dissertation is structured with five chapters, such as Introduction, Literature Review, Research Methodology, Findings and Discussion and Conclusion and Recommendations.

Introduction: The first chapter contains data regarding the research aim, objectives, background data, a problem statement and rationale to justify the reason behind conducting the research in the selected area.

Literature review: There are existing sources containing essential information regarding usage of financial technologies and those are to be analysed with the help of categorised factors. The purpose is to critically assess existing ideas and evidence on the current research phenomena.

Research methodology: Particular data collection and analysis methods are to be used for each research. Structured information regarding tools and techniques to be followed for present study will be evaluated in this chapter.

Findings and discussion: The results retrieved from collected data and emerging concepts, ideas, etc. are to be critically analysed in this chapter. Whereas, discussion will be added as a counter argumentative evaluation where data of LR and findings are compared and contrasted.

Conclusion and recommendation: The last chapter of the dissertation will include an overall summary of findings and results along with a segment linking with an objective. Limitations of current study along with recommendations for further development will be added as well along with its future scope.

1.9 Chapter Summary

Fintech has become a significant market in present day within the global context. It is resulted by technological advancements and interventions. Considering the data evaluated in this chapter, it is expected that this research will be able to provide effective solutions to current challenges faced by European firms in terms of maintaining the performance of fintech. During the initial research on current research context, it is also identified that data security management, financial risk management, managing operators’ knowledge and skills are important to ensure successful utilisation of fintech. These aspects emerged during the preparation of this chapter, which will be explored further in the following chapters.

Chapter 2: Literature Review

2.1 Introduction

The chapter on Literature Review is based on highlighting the concepts linked to the research aims and objectives in the research. In this chapter, financial technology with a focus on big data analytics will be discussed in order to understand the future of financial services. Arjunwadkar (2018) has illustrated in the research that financial technology could help in increasing flexibility and efficiency in the financial services of a company. From this statement, it is clear that financial technology has an opportunity for development in businesses to improve the situation of financial services. The purpose of this chapter is to accentuate the role of big data analytics in financial services to improve the financial conditions of organisations by using FinTech for organisational development. At first, the concepts of financial technology will be discussed following the challenges in using financial technologies and the strategies for solving the challenges. The theoretical framework along with the literature gap in the research will be highlighted.

2.2 Concept of financial technology in financial services

Financial services in a company are extremely important to be considered in an organisation as it helps an organisation in apprehending the profits and losses of the business. It needs technological undertaking to ensure that the services in the finance background are delivered as per the requirements of the organisation. Bazarbash (2019) has identified that financial technology known as fintech helps in automating services for financial management in an organisation. From the statement, it is comprehended that financial technology helps organisations in managing financial records in business and keep track of ongoing financial activities in business. This technology comprises smartphones, coded algorithms, and software that could help in assessing the financial condition of a business. On the other hand, Setiawan et al. (2021) have argued that financial technology can be faster but it requires financial investment of a higher aptitude in a business which can increase the expense of the business. As per the authors’ claim, financial technologies should not be used in organisations if they are concerned about reducing costs. On the other hand, refuting this claim, it can be reinstated from the previous claim that if financial technology is used, unnecessary costs in the business can be reduced by adequate calculation of business profits and losses. Thus, it is understood that  financial technology is used to manage the costs in business and undertake structural finance management in business.

2.3 Trends in Financial Technology

Figure 2.1: Trends of financial technologies in the banking sector

(Source: Self-developed)

Financial trends are an important aspect that helps in understanding the future of financial technologies. One of the biggest trends in financial technology is big data analytics which is the focus of the research. As per the views of Deepa et al. (2022), the increasing amount of data generated from financial transactions has led to an increase in the use of data analytics in recent years. Data analysis has been employed by fintech companies to find out more concerning consumer behaviour, market trends, and other essential information elements. Other than this, Cai et al. (2022) have highlighted IoT (Internet of Things) and AI (Artificial Intelligence) as the biggest trends for financial services in the sector. From this, it is understood that IoT and AI can also be used in embracing financial technology in business institutions. Using embedded finance, financial products or services can easily be integrated into non-financial platforms like e-commerce websites, smartphone apps, or other electronic spaces as per Abad-Segura and González-Zamar (2020). Using embedded financing is a trend for financial services. On the other hand, Maroufkhani et al. (2020); and Elsaid (2021) have argued that big data analytics are more effective in managing financial activities along with managing cost considerations in business.  Therefore, it is understood that if organisations use these technologies to develop their financial activities, it could help in improving the research by linking it aims and objectives of the research.

2.4 Implementation of big data analytics for financial services

Figure 2.2: Market Size of Big Data Analytics Worldwide

(Source: Taylor, 2022)

The importance of big data analytics can be understood from Figure 2.2. As per the report of Taylor (2022), the market size of big data analytics in 2022 is $271.83 billion which is expected to augment in the upcoming years. It was $240 billion in 2021 and in one year, more than 30 billion dollars of an increase can be perceived in the worldwide market of big data analytics. Thus, it is understood that big data analytics has become an important trend in FinTech to secure the future of many companies. The data analytics market in the world is expected to grow at a rate of 9.5% CAGR by 2023 (Global Newswire, 2023). As per the calculations made by global financials all over the world, it is comprehended that big data analytics has a significant future in developing financial services in the world. As per the affirmations in the research of Song et al. (2021); Melnychenko et al. (2020), financial institutions may employ big data analytics to learn more concerning consumer preferences and wants, helping them to supply customised interactions that are suited to the requirements of each unique user. Banks can continue to surprise customers with better-suited offers by applying this kind of comprehension. Financial services are able to improve customer service, consumer targeting, and channel effectiveness with the ability to interpret tremendous quantities of data. Real-time data acquisition aids in improving security, avoiding potential theft of money, and discovering fraud in banking institutions (Suryono et al. 2020). Big Data Analytics is an addition to financial services that administer making a safe and secure network of activities in organisations. Big data may follow the stock market to find the earliest indications of changes in stock price. This could substantially enhance the trading performance of financial institutions. Big data can be leveraged to provide enhanced financial solutions and assistance. Therefore, it is a meaningful grant to the organisations for handling finance.

2.5 Service of mortgage lending in financial services

Figure 2.3: Mortgage services in banks

(Source: Urbinati et al. 2019)

Mortgages are a form of loan that might be used for purchasing or holding up a house, land, or another item of real estate. The borrower agrees to make monthly payments to the lender, generally as a result of a series of regular instalments that are separated into principal and interest. The property then serves as a safety against the loan if the loan-takers fail to pay their loans. Urbinati et al. (2019) have accentuated in the investigative study that if a person fails to pay their interest amount, banks are structured to take the mortgaged objects of the person or an organisation. From the statement, it is understood that although mortgage lending helps in securing money for business it has a risk of losing the properties of a business if a company fails to pay their loan. On the other hand, Wang et al. (2019) have argued that technological assistance can help in reducing the negative impact of mortgage lending by managing financial services. From this statement, it is clear that, if organisations can have impactful financial technologies, it could help in reducing tensions related to the financial management of the business institutions.

2.6 Association between big data analytics and mortgage lending

The mortgaging operations in a business is a noteworthy set of operations that needs to be enforced to manage the activities in the business entities. In order to manage mortgage activities, technological assistance is mandated in the form of Big Data Analytics which helps mortgage lenders to determine and segment customers. As per the affirmations in the research of Wang et al. (2021), big data analytics is used to calculate the probability of delinquency in banking services. From this statement, it is comprehended that big data analytics are designed to compute the number of delinquent customers can the bank can harbour. Winling and Michney (2021) have argued that the calculation of delinquency is also not sufficient to manage the financial circumstances in the research. However, this claim can be refuted as if banks can have the assessment of tardiness, it could help in selecting clients for mortgage lending. Therefore, it is understood that big data analytics is significant for mortgage lending services.

Corporations might benefit from enhanced operational efficiencies, effectiveness, transparency improvements, risk reduction, and simpler procedures by establishing a routine for developing their data analysis. Big data analytics helps in assessing data related to the past and present financial grade of the company as per the declarations of Wang et al. (2016). From this statement, it is analysed that if past and present data are assimilated, it helps in understanding the differences It helps in assessing the data credit fraud that helps organisations to save their hackers. Organisations have complex workflows that are extremely important to manage which take help from big data analytics to guarantee to automate workflows as it can help in enhancing the financial services in an organisation. Mikalef et al. (2020) have criticised the usage of big data analytics as the services can be hacked easily which can be a negative impact on customer-sensitive details. From this statement, it is understood that it can be a disadvantage if all financial information is leaked to the competitors. However, this problem can be solved but using big data can be helpful in the successful management of financial services. Big data has blossomed into an essential resource for the mortgage sector, allowing lenders the ability to acquire relevant data on potential borrowers and assisting them in making more informed choices (Wiener et al. 2020). Lenders can easily spot customs in consumer behaviour that may indicate a risk or an opportunity with the proper data analysis. This enables them to manage their loan portfolios with greater success by modifying their approaches as essential. Therefore, it is understood that big data analytics and mortgage lending have an association that helps in improving financial services.

2.7 Implementation of big data analytics in mortgage lending

Owing to the variability in each customer’s characteristics, loans may not always be provided in the same manner. Norris and Lawson (2022) have highlighted in the research that mortgage lenders who fully customise their quotations achieve appropriate loan allocation and pricing. They offer customised supplying terms, rates of return, and loan amounts. For instance, a borrower with inadequate credit could end up offered a high-interest loan as compensation for suitable collateral. A borrower with a superior credit history and consistent employment is subsequently offered a low-interest loan as per Gatt (2023). Data analytics supports lenders in real-time loan creation. As an outcome, conversion rates also considerably rise. This can help in improving the situation of mortgage lending in financial services through big data analytics. On the other hand, Awan et al. (2021) have argued that big data analytics requires special attention from the technological experts in an organisation as employees do not have much knowledge of these technologies. From this statement, it is clear that it can be an issue for mortgage lending as a lack of knowledge could be a problem for the organisations. Even after that, this is not an issue for which the use of big data analytics in organisations should be stopped. Big data analytics helps an organisation to assume future trends in the business and manage financial activities as per the upcoming future trends. Mortgage lenders may anticipate consumer demands by utilising predictive analytics for qualitative and quantitative estimates as per the views of Dosalwar et al. (2021). By matching merchandise with potential growth markets and the most attractive partners, such as intermediaries or realtors, mortgage lenders can optimise supply decision-making through market statistics and capacity models. Thus, it is understood that big data analytics is extremely important for developing business developments through financial services.

2.8 Challenges in using financial technology for financial services

Financial technology can be efficient for banks but it can also be a huge problem for the banks as using technologies such as cloud computing and AI have the chance for hackers to hack data through breaching security codes. As per the opinions in the investigation of Taufick (2020), it has been seen that 95% of banks globally have suffered data breaches and hacking of customer-sensitive information. Therefore, it is a huge challenge for companies as companies would not be able to protect the data of their customers and lose the trust of the customers. On the other hand, Moro Visconti and Morea (2019) have argued that big data increases the profitability rate of banks by 20% if they use big data analytics. Therefore, even after having the challenge of security, it is understood that financial support for this technology could help in increasing the roots of businesses for banks. However, if data is lost to hackers, it could reduce customer retention in the banks so the development of financial technology in the banks for increasing efficiency is a risk factor in banks. Therefore, the lack of mobile banking solutions for customers to secure their data is a negative factor that supports data breaches in the financial institutions making a loss for the banks and their customers.

2.9 Challenges for big data analytics in mortgage lending

Figure 2.4: Challenges of big data analytics in the banking industry

(Source: Möller, 2023)

Big data is an impactful technology in the mortgage lending industry but it can have some challenges which need to be understood. As per the comprehension in the investigative study of Möller (2023), a massive challenge for big data analytics is nonadaptive legacy systems due to their incapacities [look into figure 2.9]. From the statement, it is understood that legacy systems are extremely important for banks but as with the development of technology, the workload increases, the legacy systems crash. However, Nti et al. (2022) have debated in the data retrieved from the database that the foremost challenge for big data analytics is cybersecurity and the amount of data retrieved in the banks per month. From this insistence, it is evident that the aspect of big data increasing its range and width is a challenge for banking companies along with their issue of getting hacked. Therefore, it is understood that the load of huge data controls and the cybersecurity challenges are impactful for big data to work in the banking sector for improvement in services.

2.10 Approaches to improve enactment of financial technology in financial services

Financial technology in banks can be led to many challenges but these challenges can be solved by using some techniques. Those techniques are:

• The legacy systems can be developed by using cloud-based systems: The legacy systems in the banks are not updated as per the modern data generation due to which they fail to operate with large base data. Due to this, legacy systems should invest in cloud-based technologies that could adapt to the changing business environment (LinkedIn, 2022). Investment in these technologies would help in making a more scalable system in banking.
• Real-time monitoring and multifactor encryption save risks of cybersecurity: Cybersecurity risks in the banks are increasing every day so banks should ensure their data are encrypted and cannot be hacked by others. Kafi and Akter (2023) have commented that multifactor encryption and real-time monitoring can help banks reduce the risks of cybersecurity in financial institutions. Multifactor encryption would ensure that no hacker can break the security-coded wall of technological devices in banks and other financial institutions. Real-time monitoring allies’ employees of banks to set an alarm system to know the moment data is being loaded to hackers. This way should be used by many banks to have effective technological implementation in business.

2.11 Theoretical Framework

Figure 2.5 Theoretical Frameworks

(Source: Self-developed)

2.11.1 FinTech Fundamental theory

As per the fundamental theory of FinTech, it is not possible for banks to have technical developments in their business without using technologies. In order to meet the current market demands and competitive trends in business, it is important for banks to develop technological assistance in the business (The Pulse of Fintech 2017, 2018). The theory can guide this research as it helps in understanding the significance of technologies in managing finance in the banks. This theory is relevant to the research as it helps in understanding the challenges businesses face without using technologies in business. Therefore, according to the theory, European banks should develop big data analytics to help their banking trends to get enhanced.

2.11.2 Financial Theory

The financial theory helps in understanding the importance of financial systems in the economy through which the economy runs itself. As per Druhov et al. (2019), European banks should maintain their finance system by developing technologies in the banks that can make the banking system faster. The theory is relevant to this research as it helps in accentuating the importance of financial management in business and the interdependence of economic finance systems and business finance management. Using technologies is the way of improving finance management so this theory would guide the research in technological implementation. This is the reason why banking companies should use technology as per the common trends in 2023.

2.12 Literature Gap

The articles and websites that have been reviewed in the section have highlighted the importance of financial technology in the banking sector. The literature gap is that facts has been mentioned in the articles but proper comparison with historical data has not been done. The LR section is focused on current situation of finance technologies, but with less focus on past data. In this research, trend analysis has been done to fill this gap in the literature.

2.13 Conceptual Framework

Figure 2.6: Conceptual Framework

(Source: Self-developed)

As per the above figure, it can be understood that financial technology employed in financial services is the dependent factor as it is dependent on challenges and strategic development of the banking sector. The independent variables in the research are strategies implemented by the banking sector. The dependent variable (technologies implemented in financial services) helps in identifying the dependency of technological implementation on certain factors. It is connected to the study as the research is based on analysing the role of financial technologies. The independent variables (Challenges and strategies) are the factors that impacts functioning of fiancé technologies, hence, they are related to this research for strategic development of financial technologies.

2.14 Summary

The chapter on Literature Review has highlighted the concepts of financial technology and its importance in the banking sector. The banking sector faces complications in dealing with a huge range of activities due to which it is extremely significant to develop financial technologies in business. Big data analytics is a technology through which mortgage lending becomes easier as it helps assess customers and their financial position in society to calculate the time, they would take to repay their loans. However, it faces challenges such as cyber security which should be solved by implementing multiple encryptions so that hackers cannot hack important information of customers. Financial theory and the Fundamental Theory of FinTech have been used in this research to highlight financial importance in an economy.

Chapter 3: Research Methodology

3.1 Chapter Introduction

There are particular methods, tools and techniques that are followed during performing new research on a particular topic. Within this process, data collection and analysis methods and tools are chosen as well. It is called research methodology and it is important to be determined so that research can be driven toward a focused direction. This chapter of the research work would contain essential information regarding the research approach, strategy, philosophy, data collection and analysis patterns, etc. that ought to be followed for the research based on the context of the importance of financial technologies and trends of using them within the European marketplace.

3.2 Research Onion

Research onion is a systematic structure containing a six-layered structure. Each layer opts for a specific tool that must be used in a research work. From the outer to the inner side, the following are the stages, such as research philosophy, research approach, strategy of research, methodological choice, time horizon and data analysis and collection methods (Saunders et al. 2015).

Figure 3.1: Research Onion

(Source: Saunders et al. 2015)

Following this systematic structure is beneficial to gather data accurately within a stipulated timeline and analyse that in a structured manner so that the research aim and objectives can be met and questions can be answered. It helps in stating the findings and results of the research adequately while addressing particular limitations so that future research works can be performed accordingly.

3.3 Research Philosophy

A research philosophy is considered to be a belief regarding the way that must be followed for a particular research phenomena to gather, evaluate and use particular information. There are two of these that are mainly formed for academic research works, such as interpretivism and positivism. Interpretivism philosophy depicts that the world is understood with the views of individuals. It has a subjective pattern to look into worldly matters, hence it states that people their experiences, and their perception of reality must be considered for developing findings (Alharahsheh and Pius, 2020). Whereas, positivism philosophy places emphasis on factual information and has an objective way of looking into matters. As per this technique, it is important to put facts in alignment and develop objective interpretation so that valid results can be formed. Current research is based on the positivism research philosophy.

Justification

Considering the factor that whether using financial technologies is beneficial for firms to improve their services offered to consumers or not cannot be identified using consumer perceptions. It is important to check technical facts and financial outcomes to identify the success rate. As factors related to the current research area demand more factual data and objective interpretation, positivism research philosophy is considered for analysis of collected data.

3.4 Research Approach

Research approach is considered to be one of those tools used for particular data collection and detailed analysis. There are three categories of this, such as abductive, inductive and deductive. The most used ones for academic purposes are inductive and deductive. While using deductive approach, the concern is to work with existing ideas, concepts, theories and factors, while inductive requires for evaluation of existing factors to create new theoretical structures. In using an inductive approach, there is a need to prove connections between various variables so that by establishing strong assumptions, hypotheses can be developed (Pearse, 2019). On the opposing side, deductive has no such restriction as it testifies to existing hypotheses for data analysis and results development. Current research on the context of fintech and its usage trends in Europe is supported by a deductive approach. Deductive reasoning enables to test existing hypotheses and theories, which are relevant when examining the impact of financial technologies on traditional financial services. This is why this research approach is chosen.

Justification

Financial technologies are used to overcome limitations of traditional methods of delivering quality financial services. Ample data regarding usage of big data technology, mortgage lending, etc. are available on different scholarly sites, research papers and books. Those data are to be evaluated based on current research context and information is to be deduced in link to current research objectives so that analysis can be done in a comprehensive manner. Practices followed within the European marketplace to work with fintech are to be evaluated to determine potential advantages and disadvantages of the existing system. As existing practices, ideas and theoretical implications are to be evaluated, deductive reasoning is suitable for this research to assess validity of already followed practices and business approaches toward using fintech.

3.5 Research Strategy

The research strategy that is to be followed in this research work is a grounded theory-based strategy. The reason behind using this kind of strategy is that existing theoretical concepts related to fintech and its growth within the European marketplace are to be utilised to determine its importance for improving financial services. Both qualitative and quantitative data are to be used in this research work. Grounded theory is mainly utilised for interpretation of qualitative data, however as limited quantitative data is to be analysed, it would not create a big issue. However. it is crucial to maintain the direction of analysis as both qualitative and quantitative types of data are to be gathered from secondary sources. Grounded theory puts emphasis on understanding phenomena related to particular contexts, which is why using it is advantageous to understand each variable adequately (Chun Tie et al. 2019). There are three variables to be tested within this research, such as usage of big data technology, mortgage lending and fintech.

3.6 Methodological Choice

There are three types of methodological choices that can be employed in academic research studies, such as mono-method, multi-method, and mixed method. Both mono and multi-method have two categories, such as mono-method qualitative, mono-method quantitative and multi-method qualitative and multi-method quantitative. The mixed method is used when the concern is to use both qualitative and quantitative data in research (Mik-Meyer, 2018). Considering the fact that utilisation of fintech for mortgage lending, usage of big data technology for improving the quality of financial services, etc. require the support of both types of data, a mixed methodological choice is opted. In a way to interpret financial growth-related matters, quantitative data is to be used. On the other hand, qualitative data is to be analysed to interpret the importance of using financial technology to ensure the growth of financial firms providing valuable economic support services to people. It is expected that with the help of this methodological choice, a robust analysis can be done to analyse European trends of using fintech and the challenges faced by them in the process so that strategic solutions can be developed effectively.

3.7 Time Horizon

Selection of a particular time horizon helps in conducting research within a stipulated timeline and finishing it within deadline. Two types of time horizons are followed mainly for academic purposes, such as cross-sectional time horizon and longitudinal time horizon. The current research is based on identifying the importance of financial technologies in ensuring growth of the financial sector, where particular focus is given to the European marketplace and its trends of using fintech. In case, longitudinal time horizon is followed, then data would require to be collected multiple times from a similar set of sources or a particular population size. The research timeline gets stretched in this case as data is required to be collected several times over a long time period (Lee, 2017). On the other hand, following the cross-sectional time horizon is advantageous to collect information within a stipulated timeline from a particular source and it does not require repetitive collection as it is a one-time process. It saves research time and helps in completing it within the deadline. Considering this argument, it is decided to conduct current research with the help of a cross-sectional time horizon.

Justification

There are three factors that are focused on in this research, such as financial technologies or fintech, the importance of big data technology in mortgage lending activities and trends in using financial technologies in Europe. Using the longitudinal horizon would require tracking on trend changes as data is to be collected over a long period and that would be disadvantageous to meet current research’s aims and objectives. This is why a cross-sectional horizon is selected so that without data diversion and the necessity to track overtime trend changes, this research can be delivered on time.

3.8 Data Collection Method

Data collection method is opted for choosing particular sources to be considered for collecting information. In case, primary data collection method is opted for, then data is to be collected newly by conducting surveys and interviews within a sampled population size. On the other hand, in case a secondary data collection method is followed, then data is to be collected from existing journal articles, newspaper reports, relevant websites, etc. Databases are collected from Google Scholar, ProQuest, Science Direct, Research Gate, Sage Journal, and so on while following secondary research (Jilcha Sileyew, 2020). Current research work is based on analysing current trends in European financial markets using fintech and the objective is also to determine significance of fintech in mortgage lending activities. As existing practices and trends are to be evaluated, a secondary data collection method is opted for.

Justification

The European marketplace utilises fintech to provide better financial support services to their consumers. The objective here is to overcome limitations posed by traditional methods in different financial activities. There is valid information available on secondary sources regarding usability of fintech and its utilisation within the European marketplace, which is why, it is decided to use a secondary data collection method. Not only is it beneficial for understanding current practices, but also beneficial to get insight into previous practices and challenges of all kinds of approaches.

3.9 Data Analysis Method

After the collection of data in context of current research phenomena, it is important to opt for an analysis technique so that analysis can be done comprehensively to answer particular research questions. There are two categories of data analysis methods, such as qualitative and quantitative and then there is another one that requires blending and using both the analysis methods namely mixed analysis method (Dawadi et al. 2021). Current research work requires analysing current trends of using fintech and its challenges, which can be done appropriately following the qualitative analysis technique as it allows to evaluation of information descriptively. However, as the research depends on analysing financial outcomes, using the quantitative analysis technique is also important. Both kinds of data are collected from secondary sources.

There are three variables on which this research beholds, such as importance of fintech technology, mortgage lending financial activities and importance of big data technology. Market trends in Europe regarding fintech are to be analysed based on these three prospects, which is why comparative trend analysis approach is selected for critical evaluation of collected information (Giraldo et al. 2019). It will help in attaining comparative analysis of different kinds of current trends related to financial technology and identifying areas of concern that are creating or can create bigger challenges within the financial activities. It will help in proposing strategies accordingly to ensure further development and better financial growth outcomes.

3.10 Ethical Consideration

An academic research must be conducted on certain ethical grounds so that it can be valuable and valid. This research work is supported by data gathered from existing databases available from secondary sources. In a way to acknowledge those sources, throughout in-text citation is done in the research document and a reference list is added at the end of the document as well. On the other hand, to ensure that data gathered from these sources is not misinterpreted, data fabrication, fragmentation, and data manipulation are not entertained (Elisa Raffaghelli, 2020). Evidence is collected only from valid sources so that accuracy of information getting added to the research can be ensured. Only peer-reviewed journal articles are considered to ensure reliability and validity while University guidelines are followed to prevent any kind of academic misconduct.

3.11 Chapter Summary

The present research work based on exploring the usage of financial technologies within the European marketplace is supported by secondary data collection and mixed analysis methods. Secondary mixed method is supported by positivism philosophy, deductive approach, grounded-theory strategy, mixed method choice and cross-sectional time horizon. It is expected that this particular proposed methodology is going to help in attaining adequate comparative trend analysis so that European fintech trends and their challenges can be well-recognised by the end of the research. This will help in meeting research objectives and answering particular research questions prominently to ensure success of current research study.

Chapter 4: Findings and Discussion

4.1 Chapter Overview

The research has four unique objectives that can help to increase knowledge about the implementation of financial technologies such as big data analytics in the financial sector of Europe. To fulfill the objective both qualitative data analysis and quantitative trend have been conducted. The advantage of using mixed methods is that it helps to analyze both types of data hence increasing the quality of the research.

4.2 Thematic analysis

This research uses thematic analysis to complete three objectives among four. At first, the analysis highlights the relationship between big data analysis and mortgage lending that are beneficial in shaping financial technology. Secondly, the thematic analysis highlights the process through which big data analytics can be used for understanding different elements of mortgage financing. The thematic analysis also tries to identify challenges that are being faced by the organizations within Europe while using or integrating Fintech so that recommendations can be created.  Lastly, the thematic analysis identifies recommendations so that barriers or challenges can be overcome. The themes have been developed are as follows:

4.2.1 Theme 1: Big data analytics identify risks and help in decision-making in mortgage lending

The success of the mortgage lending process depends on the decision-making process. Mortgage lending and other financial activities face a certain amount of risks. For gaining profit it is essential to identify and analyze the risks related to the activities. The study of Huttunen et al. (2019) highlights that big data analysis and other similar technologies such as machine learning, and cloud computing are capable of analyzing large amounts of financial information and identifying particular trends that can help in making decisions. Technologies identify the risk factors of a customer who is requesting mortgage lending. Based on the risk identified, managers make a decision whether to accept the loan request or not.

The research of Anand et al. (2022) further highlights that machine learning, which is an integral part of big data analysis, is capable of analyzing the behaviour of customers who have taken loans. Risk regarding mortgage lending starts after providing the loans. If customers fail to provide a loan within time or become bankrupt then it can cause financial loss for the financial institution. Due to this reason, it is essential to monitor the financial activities of the customers or loan takers during the loan period. However, it is not possible to observe the financial health condition of activities of multiple customers through the manual process since it will require a large amount of human resources hence increasing the operating cost. Machine learning and big data analysis process solve this issue since it is capable of analyzing information in a continuous manner. The system identifies the risk factors as they emerge for a particular customer and notifies the managers. This helps managers to have discussions so that the risk can be solved.

The review by Coşer et al. (2019) highlights that big data analytics can be integrated with different types of statistical models such as regression models. The integration process allows this technology to make high-quality forecasting. Results are often analyzed considering the market situation. Considering multiple variables altogether to understand the risk factors helps to improve the acting quality. The study of Zhu et al. (2019) also highlights that big data analysis is capable of identifying the probability of default through a random forecast algorithm. The random forecast algorithm has the capability to outperform regression statistical algorithms in certain situations. From this, it can be identified that there are different types of algorithms and analysis methods that can be followed in big data analysis to predict loan-related risk.

Considering the above-identified factres, it can be identified that Big Data analysis has a positive impact on the mortgage lending process. The main purpose of big data analysis is to identify the existing risks and forecast the risks that may arise in future.  Hence it can be said that big data analysis is an assisting tool in the mortgage lending process.

4.2.2 Theme 2: Big data analytics can be utilized for different purposes such as increasing security, sustainability, innovativeness and more in  mortgage financing

The study of Gillis et al. (2019) highlights that there are different rules and legal frameworks that need to be followed while providing mortgage lending. Using big data analysis and other similar technologies helps to ensure that all the regal requirements have been followed in a blending process. Legal frameworks tend to be complex in nature. Due to this reason, while copying the rules manually,  there is a risk of missing particular legal factor images. However, big data analysis ensures that all the legal factors are being followed while providing mortgage lending. Implementing big data analysis and other similar technologies helps to increase the clarity of the lending proces.

Large banking institutes often engage in cross-border lending. In these situations, a large amount of money needs to be transferred from one country to another country through secure networks. The study of Deepa et al. (2019) highlights that big data analysis instigated with blockchain technologies can help in cross-border financial transactions. Cross-border financial transactions face a higher amount of risk compared to local transactions. On the other hand, large money transactions attract a larger amount of hackers hence increasing the security risk. For securing a large amount of data transfer and detecting cyber security attack-related threats big data analaysis and blockchain technologies play an essential role in the modern world.

The study by Singh & El-Kassar, (2019) highlights that in the modern world, banking sectors are considering sustainability factors before providing loans. Due to the awareness of global warming and pollution, banks are changing their strategy from traditional style to green financing. In green financing, banks need to consider the effect of a loan on the environment. Big data analysis is capable of forecasting the effect of a loan on the environment. For example, it can detect the expected carbon output of a loan. Identifying these factors is helping banks to increase their sustainability factors.

The study of Ghasemaghaei & Calic, (2020) highlights that big data analytics are more efficient in handling large volumes of information compared to humans. By using the advantages of big data analytics technology banking businesses are building new products and services. Enhancing the premises of the mortgage lending service is helping banks to increase their product diversity. The enhanced services are also helping to increase the customer satisfaction of the banks. For example, big data analysis is allowing banks to provide 24*7 hours of services at a lower cost. These types of facilities are helping the banks to increase their customer loyalty.

Considering the factors identified it can be understood that data analysis plays an important role in the banking system. It helps to increase the security level while transferring information or money, invasion capacity and more. Hence, it can be said that big data analysis can be utilized in various manners in the mortgage lending process.

4.2.3 Theme 3: Maintaining storage facilities, organising training and investing large capital are the main challenges while using or implementing financial technologies

The study of Bhattarai et al. (2019) highlights that the basis of big data analysis is a large amount of data. Increasing the data available for analysis increases the quality of the data analysis. Due to this reason, banks need to collect and store large amounts of information relevant to the market and customers. The key challenge of big data analysis is in its storage process. For string larger volume of information high-quality storage facilities are required. Businesses with such facilities are costly. Storage facilities also face security-related threats. If the integrity of these storage facilities is harmed, stored data may become corrupted. Stolen data can also harm the privacy of the customers. Due to this reason, managing the storage systems is one of the major challenges while implementing big data analytics systems.

The study of Bhattarai et al. (2019) further highlights that technologies related to big data analysis are changing at a rapid speed. Employees working in the banking sector are not experts in the subject of Information technology or digital technologies. Due to the rapid development of the technology employees face difficulty in understanding the features properly. To solve these issues, businesses are required to organise training progress in a routine manner. However during the training programs, employees are unable to work hence, companies’ output starts to decline. Training programs are also costly hence increasing the human resource management costs that have a negative effect on the overall profitability.

The study of Tabesh et al. (2019) highlights that the implication of big data analytics requires a considerable amount of capital investment. Larger business organizations have a sufficient amount of capital hence they are able to bear the costs with less effort. However, for smaller businesses, they have to manage their whole operation with a limited amount of capital. Investing large amounts of capital in big data analytics makes smaller banks vulnerable to liquidity risks. Due to this reason, it becomes challenging for the smaller business to invest in such technologies.

From the above discussion, it cna be identified that there are multiple barriers when it comes to the application of big data analysis. For example, continuous evolution of the technology, cost of maintaining the storage facilities, and capital costs. These types of barriers can function as barriers when it comes to the implementation of these technologies into the business.

Considering the information highlighted above it can be understood that the trend of Financial technology is a positive trend all over Europe. Countries are gaining benefits from the technology and due to this reason, they are showing interest in investing more in the technologies.

4.2.4 Theme 4: Recommendations that can be provided to solve the challenges faced while implementing financial technologies are: multistage implementation, multistage training and more.

The study of Tabesh et al. (2019) highlights that big data analytics is a complex process and due to this reason, it cannot be understood properly within a short amount of time. Due to this reason, it has been recommended that businesses need to provide training on such technology in a multistage process. Initial training should have a low level of difficulty. As the experience of the employees increases, complex training should be provided to them. This will help employees to build knowledge of the technology over time. Employees with higher knowledge can also be utilized to train employees who have a lower level of knowledge. This can help businesses avoid hiring external \trainers for every training session.

The findings of Bag et al. (2020) highlight that integrating big data analytics or other similar technologies throughout the organization at once will require a considerable amount of capital. Changing the operation process within a small amount of time can also lower the work efficiency of the employees as they face difficulty in handling the technology. Due to this reason, it has been recommended that advanced technologies should be integrated through multiple stages. The business needs to implement the technologies into different specific operations. This can help to understand the compatibility of the technology with the business. The slow integration also helps manage capital while purchasing the technology. The study of Dai et al. (2020) highlights that operation processes followed by business organizations are unique. It has been suggested that the interface of the software needs to be redesigned before implementing it into a particular business operation. Customisation can help employees to become familiar with the systems within a short amount of time. Another advantage of customisation is that it can act as a unique style or feature of the business hence increasing organizational brand value.

Considering the above discussion it can be identified that there are different recommendations that can be followed or implemented to solve the challenges. Business needs to be creative while solving the issues. The challenges faced by individual businesses are expected to be unique hence it is essential that a suitable strategy is created and implemented.

4.3 Trend analysis

The third objective of the research was to understand the trend of using financial technologies in the European marketplace. To complete this objective,. Varies of secondary quantitative data have been gathered from different reports. The findings are as follows:

Figure 4.1: Fintech ranking per performance

(Source: mckinsey.com, 2023)

A report published by McKinsey company highlights the usage of financial technologies in different parts of Europe. It can be seen that the United Kingdom has the highest financial technology performance compared to other countries. However, on average, the majority of European countries are using Financial technology. This indicates that financial technology has a positive trend in Europe.

Figure 4.2: Customers’ behaviour trend towards financial technologies

(source: mckinsey.com, 2023)

The above figure highlights the reasons for customers to prefer financial technology. It can be seen that Financial technologies help reduce banking prices or costs from customers’ perspective (32%). It also provides easy access to banking services (32%). These facilities are helping to increase the trend of financial technology usage among customers. The report also highlights that the speed factor (30%) also helps the technology to become trendy among the customers. Overall it can be understood that customers are playing an important role in making financial technologies trendy in the European market.

Figure 4.3: Funding for Fintech in different countries of Europe

(Source: mckinsey.com, 2023)

The above figure highlights the funding provided in different countries for the early stage and stage. Here, it can be seen that the UK provides the highest funding for financial technology in the early stage (5.3 million) followed by Finland (5.00 million), Sweden (3.8 million) and more. In the case of late-stage funding, it can be seen that the UK provides the highest amount of funding (33 million) followed by Denmark (17 million) and more. From this, it can be seen that the UK provides the highest funding in both stages. However, funding provided by other countries in between the early and late stages differs from each other. Overall it can be identified that the majority of the countries are provided higher amounts of funding in the later stages compared to the early stages. After completing the early stages countries are receiving benefits from the technology. As a result, they are providing more funding to the later stages to ensure that financial technologies are integrated properly.

Figure 4.4: Fintech investment deals

(Source: statista.com, 2023)

The above figure highlights the financial technology-related investment deals. It can be seen that the largest number of Investment deals took place in the UK (469) followed by France and Germany. From this, it can be understood that countries are having multiple investment deals related to financial technologies.

4.4 Discussion

Thematic analysis and trend analysis have been conducted to understand the factors that can fulfil the research objective. This section tries to evaluate whether the findings made are sufficient to fulfil the objectives or not. The section also tries to understand the relevance of the findings compared to the literature review identified in the second chapter.

The first objective of the research was to identify the connection between big data analysis and the mortgage lending process. The analysis identified that there are considerable links between mortgage lending and technology. Hence it is essential for the businesses to important technologies properly so that the risk of the mortgage can be reduced as much as possible. This can help the business stabilise their financial risk. Big data analysis is used as an assisting tool in the mortgage lending process. The system helps identify risks and supports manageres in mortgage lending-related decision-making.

The second objective of the research was to understand the ways in which big data analysis can be utilised. The thematic analaysis highlights that big data analyses can be used in different aspects such as increasing data security, sustainability, innovativeness and more. Implementing technologies in such manner can enhances the operational integrity of the banking sector as a whole. It can also help to gain the trust of the consumers.

The third objective of the research was to understand the trend in the Eupre regarding the usage of financial technologies. The quantitative trend analaysis identified that European countries recognise the positive aspects of the technology. Due to this reason, they are investing a considerable amount of capital to implement the technology into their economy and banking sector. This can be seen as a positive growth trend for the financial financial technology integration. The fourth and last objective was to identify challenges and recommendations regarding the implementation of financial technologies. The thematic analysis highlights that maintaining storage facilities, organising training and investing large capital are the main challenges when implementing big data analytics and other similar technologies. In the recommendation, the thematic analysis highlights that businesses should use multistage implementation, multistage training and other similar strategies to integrate the technologies efficiently.

The findings made in the thematic and trend analysis can be supported by the literature reviews. For example, the study by Melnychenko et al. (2020) highlights that big data analytics can be used to identify the risk factors gendered due to customers’ behaviour. This factor helps to understand that the findings made under the first and second objectives are true as they discuss risk mitigation. The study of Awan et al. (2021)  on the other hand identified that technologies need special attention from the developers. This can be seen as a challenge for the businesses that are implementing technologies. This challenge has been indicated in the third theme. Considering this factor it can be said that the findings made in this research align with the literature review. Hence the findings are acceptable. The findings can be utilised by the decisions makers as they try to implement technologies within their organisations. It can also help to create guideline for using such technologies. Foer example, guideline for data security, guideline for selecting technologies and so on. In average the findings are expected to enhance the quality of the banking operation.

4.5 Summary

This chapter highlights the findings made through thematic analysis and trend analysis. Both analyses helped to identify factors that are relevant to the research objective. It should be mentioned that both thematic and trend analyses have been conducted on the basis of secondary information. The analysis does not use any primary data. This can be considered as the limitation of this research. However secondary data analysis helps to avoid the risk of primary data collection-related ethical issues. The thematic analysis has been conducted while maintaining neutrality to ensure its quality and to avoid ethical issues. The findings can be used by the banking sector to improve their lending process. Business are expected to choose technologies in an efficient manner ensure the the technology applied is suitable for the operation.

Chapter 5: Conclusion and Recommendations

5.1 Overview of the Research

The purpose of the research work was to identify the importance of financial technology, which is also addressed as fintech in improving the quality of financial services. The purpose was also to identify ways in which mortgage lending and big data analytics are interconnected. In a way to meet these requirements, secondary mixed methodology has been followed in this research. Both quantitative and qualitative data are collected from secondary sources and comparative trend analysis is done. The reason behind trend analysis is to develop a robust evaluation regarding European fintech trends. Considering the qualitative information regarding usability of big data technology for comprehension of aspects related to mortgage lending, it is identified that this technology helps in getting clearer ideas regarding consumer preferences (Kafi and Akter, 2023). It helps in understanding specific consumer requirements with the help of its automation feature and it helps banking organisations to customise financial services accordingly for each customer. It enables financial service providers to meet the immediate requirements of their clients and cope with financial risks effectively in real time.

However, there are certain challenges in using financial technologies while providing financial services. As per the findings, using big data technology creates issues within the legacy system. On the other hand, big data includes a critical system that can start functioning disruptively anytime in case it is not well-managed. The bigger the information database becomes, the harder it becomes to manage data (Möller, 2023). Within the European territories, fintech is used for providing personalised financial services to customers so that they can have their financial aspects well-managed. Banking organisations in different places, like France, Switzerland, the UK, Germany and Sweden are using fintech as it helps them in faster data analysis and developing effective real-time solutions for their clients (Statista, 2023). However, they are required to integrate upgraded devices, and efficient workforces and actively work on system updates to ensure operations are run seamlessly.

5.2 Linking with Objectives

Objective 1: Connection between Mortgage Lending and Big Data Technology for shaping Fintech

Considering the analysis of secondary qualitative data, it can be stated that big data technology and mortgage lending have become increasingly intertwined within the fintech industry. Big data enables fintech companies to analyse vast amounts of data beyond traditional credit scores. It includes transaction history, social media activity, online behaviour, etc. It helps in making more accurate and dynamic credit risk models. Big data automates and simplifies the mortgage application procedure. Borrowers can submit their financial information electronically, reducing paperwork and processing times (Winling and Michney, 2021). Data-driven algorithms can assess the completeness and accuracy of application documents, helping lenders identify erroneous data early in the process.

Objective 2: Utilisation of Big Data Analytics for Mortgage Financing

Fintech lenders may now provide personalised mortgage options based on each borrower’s financial profile and demands due to big data analysis. This personalisation improves the borrower experience and increases loan approval rates. The technology aids in the detection of fraudulent mortgage application activity. Fintech lenders can detect suspicious behaviour and take precautionary actions by analysing trends and anomalies (Wang et al. 2021). Big data techniques can help lenders stay compliant with changing mortgage requirements. They aid with the tracking and reporting of numerous compliance standards, lowering the risk of fines and legal complications.

Objective 3: European Trends in Using Financial Technology

Digital payments and mobile wallet usage have increased significantly in European countries, notably in Northern and Western Europe. Fintech businesses such as Adyen, Klarna, and Revolut have grown in popularity by providing simple cross-border payment options. In Europe, the Revised Payment Services Directive (PSD2) fostered the establishment of open financial systems (McKinsey, 2023). This enabled Fintech entrepreneurs to gain access to information from banks with user permission, resulting in the development of novel financial offerings and services.

Objective 4: Challenges faced by banking Organisations in using Financial Technologies

Peer-to-peer lending and cryptocurrencies, for example, are examples of fintech developments that potentially offer new and unknown hazards. To successfully handle these risks, banks must adopt risk management methods. Many established banks rely on obsolete and inflexible legacy IT systems. Integrating new Fintech solutions with these systems can be difficult and costly (Nti et al. 2022). Legacy systems may not be agile or scalable enough for contemporary banking.

5.3 Research Limitations

Considering the monetary and time constraints, only secondary data could be collected for the research work. The growth of the fintech industry within the European territories could be determined in a better manner by using primary data. This limits evaluation of current circumstances substantially, which is why future research works must focus on this area to overcome the limitation. Generalizability of findings is another limitation to be addressed as evaluation is limited to a few main factors, such as big data analytics, mortgage lending and fintech. Due to focusing on a limited area and only relying on secondary data sources, accuracy and reliability decrease of the research. Evaluation of thematic and trend analysis only of inputs gathered from secondary data sources limit the potential to address current circumstances, which could have been done following primary research methodology. Evaluation and results have potential to be biased considering only existing scholarly views are considered.

5.4 Recommendations

Following a number of technologies would be beneficial for banking and fintech companies to manage big data technologies and mortgage lending activities adequately. Recommendations to be followed for the purpose are given in the section below:

• Establishing a dedicated compliance team for monitoring and ensuring adherence to all relevant regulations is important for regulatory compliance. For this concern, organisational authorities must recruit and select strategically while also focusing on their training management and skill development. It is also recommended to consider using regulatory technology or Regtech solutions to automate compliance processes and reporting (Turki et al. 2020).
• It is recommended to implement advanced identity verification and transaction monitoring tools to improve anti-money laundering and know your customer (KMC) compliance (Han et al. 2020).
• Developing a comprehensive digital transformation strategy to gradually replace or upgrade legacy systems is suggested. It is important for concerned organisations to consider adopting a modular approach to integration to enable the gradual replacement of legacy systems (Wimelius et al. 2020). Implementing cloud-based solutions microservices architecture for scalability and flexibility would be convenient.

It is therefore recommended to the fintech organisations of Europe countries to consider these recommendations seriously so that they can ensure further growth of their business.

5.5 Future Scope

Data added to this document is beneficial for understanding possibilities and limitations of financial technologies. Ways in which big data and mortgage lending factors are interconnected and trends of European countries in this context are evaluated as well. These data would be beneficial for any fintech or banking organisation to strategically use financial technologies to ensure successful outcomes. It is important for researchers to conduct primary research in future so that they can testify current circumstances of the fintech marketplace. This will enable them in determining potential and drawbacks of current practices in ensuring financial security. The question that arises here which must be focused in future work is that whether fintech is the only solution to modern issues within the banking sector or not. Researchers interested in this area must work on this question.

5.6 Concluding statement

In the last it should be mentioned that big data analysis and other similar technologies will play important role in the banking sector. Due to this reasons, banks should integrate this technologies within their operation. Banks needs to integrate technologies through a proper planning to ensure the success.

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The aims and objectives are not presented in a manner you did. They are usually integrated into the text.

The following is the revised abstract that I prepared for you:

This dissertation delves into the evolving landscape of financial technology (Fintech) in the context of big data analytics and its implications for mortgage lending. It seeks to investigate the potential synergies between big data analytics and the mortgage financial system, with a particular focus on predictive components. Through this exploration, we aim to contribute to a deeper understanding of the transformative role that Fintech plays in shaping the future of financial services.

Our research methodology adopts a secondary mixed research approach, employing both qualitative and quantitative analyses within a positivist framework. We utilize a deductive approach, a grounded-theory-based strategy, and a mixed-methodological approach. This methodological diversity allows us to gain comprehensive insights into the intricate relationship between big data analytics, mortgage lending, and the broader Fintech landscape.

The analysis and discussion chapter serve as a platform for presenting our findings. Thematic analysis reveals a discernible and positive link between big data analysis and the mortgage lending process, underscoring its potential to mitigate loan-related risks. Moreover, our trend analysis brings to the forefront Europe’s growing recognition of the advantages offered by financial technology. However, this recognition is accompanied by substantial challenges in implementation, which we address through strategic recommendations.

Our findings reflect a shifting paradigm in European territories, where Fintech is revolutionizing financial services by offering tailored solutions that empower individuals to manage their financial aspects effectively. Banks in countries such as France, Switzerland, the United Kingdom, Germany, and Sweden are embracing Fintech for its agility in data analysis and the rapid development of real-time solutions. Nevertheless, the integration of big data into financial activities necessitates the formulation of comprehensive digital transformation strategies to gradually replace or upgrade legacy systems. We propose an adaptive, modular approach to integration, enabling a seamless transition away from legacy systems.

This dissertation sheds light on the ever-evolving Fintech landscape, emphasizing its potential benefits and the need for strategic adaptability within the financial sector. Our research contributes to a broader discourse on the transformative role of Fintech and provides insights into its potential impact on the future of financial services, particularly within the context of mortgage lending and big data analytics.

The rationale section should explain why this research is necessary or relevant. How does the growth of the European fintech market relate to the research? Clarify the specific gap in knowledge that this research aims to address.

Can you be more specific and clearly define the issues or challenges associated with financial technology and mortgage lending that your research intends to address?

While you’ve outlined the structure of the dissertation, briefly mention the content of each chapter to give the reader an idea of what to expect.

Instead of repeating the expected outcomes of the research, you can conclude the introduction with a more succinct summary of the key points discussed in this chapter.

Sentence not complete

Provide a more detailed explanation of the chosen theoretical frameworks (FinTech Fundamental theory and Financial Theory). Explain how these frameworks guide your research and why they are relevant to your study.

While you mentioned a literature gap related to subjective realities and quantitative analysis, consider expanding on this gap. Explain why it is significant and how your research will address it. What specific knowledge or insights are lacking in the current literature?

The conceptual framework is briefly mentioned but lacks a detailed explanation of how it relates to your research. Expand on how the independent and dependent variables connect to your study and hypotheses.

Clarity of Justification:

While you mention that the current research is supported by a deductive approach, it’s important to provide a more explicit and comprehensive justification. Explain why a deductive approach is the most appropriate choice for your research. For instance, you could emphasize that deductive reasoning allows you to test existing hypotheses and theories, which is relevant when examining the impact of financial technologies on traditional financial services.

Alignment with Research Objectives:

Clearly articulate how the deductive approach aligns with your specific research objectives. Describe how this approach will help you achieve your research goals and answer your research questions. If, for example, your research aims to assess the validity of existing financial theories in the context of fintech adoption in Europe, explain how deductive reasoning will facilitate this assessment.

While you’ve mentioned conducting both qualitative and quantitative analyses, the chapter seems to focus primarily on qualitative findings. It would be valuable to integrate more quantitative data and analysis results into this chapter, especially for the trend analysis section.

In the discussion section, go beyond summarizing your findings and provide deeper insights. Discuss the practical implications of your findings for the financial sector in Europe. How can these findings inform decision-makers or practitioners in the field?

You mention the limitations related to the use of secondary data, but it’s important to acknowledge any other potential limitations of your research. For example, discuss any limitations in the generalizability of your findings or issues related to data accuracy or reliability.

You can also further address other limitations in your secondary data analysis such as the scope of your data sources, potential biases, or limitations of the thematic and trend analysis methods.

While you provide recommendations, they could be more detailed and actionable. Instead of simply listing recommendations, consider elaborating on each point. For instance, explain how “establishing a dedicated compliance team” can be accomplished effectively.

In the “Future Scope” section, provide specific directions for future research. What are some research questions that arise from your findings? How can future studies build upon your work? This section should offer a roadmap for researchers interested in this area.

End the chapter with a strong concluding statement that summarizes the key takeaways of your research. Reiterate the importance of your findings and their potential impact.