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In the current scenario, most of the financial, banking services as well as insurance organizations are taking resort to the adoption of an approach, which is fully data driven for growing their businesses as well as enhancing the services, customers are provided with. Analytics have a major potential for transforming the financial sector. Despite of the disruption of analytics landscapes by various BFSI organizations, Big Data is at varying level of maturity in these companies. With increasing customer volume, financial service organizations are assigned with the evaluation of vast amount of client data. However, with the incorporation of Big Data, this information can be utilized by tracking down data regarding the clients (Popovič et al., 2018).

Identification of more areas in which, resources of Big Data can be used effectively includes aligning business cases as well as technological capabilities revealing opportunities for improved procedures of businesses. This study is really interesting because of its importance in financial sector as well as for human society. Because big data in financial departments various new facilities added which helps human life easier and also it provides better securities which is needed in this sector. For technological interventions in financial department, online banking made human life accommodating. It also improved fraud detection in financial sector in a significant way which helps the sector very much. For the implementation of this technology in financial sector customer service become more approachable and detailed which helps an organization in making more loyal customers and also in sustaining market.

Implementation of fintech is new in the industries which gain a massive amount of popularity in really small periods. Fintech or financial technology had been implemented in 21 centuries in the back-end systems of financial institutions. After that, for its multitudes of usages, it had been shifted over consumer-oriented services. Nowadays in every aspect of financial sectors fintech can be found which is reforming this industry and making it is every working procedure easier and more reliable. Data experts will be able to set the suitable goals for new project as well as inject expertise of analytics into business components for the maximization of benefits

 Research Project Background and Objectives (Literature Review)

Background of the study

This particular study works to define a significant understanding of how big data has impacted the financial sector in UK.

Research Contribution

This study encompasses a world of technology which has been affecting the working of different financial institutes. This study will be working with significant data pertaining to this aspect and bring an in better understanding to the readers about how this aspect of big data can bring in favorable change into this new sector. As opined by Zhang et al. (2017), in financial sector different sources of media and global market provide ever growing unstructured and structured data as numeric insight. Systems, which are enabled with big data will be able to find out fraud signals and analyze them in machine learning along with accurately predicting illegitimate users or transactions. Thus, big data is a crucial inclusion in every sector and financial sectors know the best use of it. There are alarms which have been constructed which help in detecting poisonous material in the atmosphere and different medical types of equipment which have helped to mitigate different medical conditions.

Research Objectives

·       To analyze the concept of Big Data

·       To identify the contributions of Big Data in the Banking Sector

·       To analyze the issues concerning it

Literature Review

Critical Analysis of Articles

1.     The concept of Big Data


The industry of finance has seen a lot of innovation which has helped it come in bright light making things much more achievable in this particular sector. However, Gabor and Brooks (2017) argued that in relation to a report which has been generated by PWC, 77% of financial has will be incorporating technological innovation into their operations. The journey which is made by the customers is made much more soothing with the incorporation of technology into this particular sector. Yi (2019) suggested that banking sector can get facilitate by the use of big data for establishing reliable and dynamic credit system. This can help them to identify their several potential customers and clients with high risks.


Big Data analytics refers to the utilization of techniques concerning advanced analytics against diversified large data sets including structured, unstructured along with semi structured data from various sources in distinctive sizes encompassing terabytes till zettabytes (Raguseo, 2018).  It is quite easy to recognize triumph and different sets of failure tend to speak for themselves. In between these two aspects of triumph and failure, there is an area which is grey working with intellectual termites who cohesively eat into the structural reality. As opined by Tiwari et al., (2018), big data enables researchers as well as business users for making effective and faster decisions utilizing data, which was inaccessible previously.


A good team of customer service was a vital inclusion in every financial institute. It required trained staff to answer any question which is related to the financial institutes. In today's world, it can be witnessed that chat-box is introduced which has helped to reach out to different customers much more feasible. On the other hand, as stated by Zuo and Guo (2019), financial enterprises can identify their customers in more efficient way by selecting vital information with the help of big data. This introduces an AI which keeps on evolving and gets smarter with each day coming. 

In relation to behavior which is posted by different clients it is quite significant to run down and understand the behavior which is incorporated by a particular client of the financial institute. As suggested by Rolffs et al. (2015), this helps to underpin when there is a change which is introduced by a particular client there needs to be efforts which are to be put by the institution to make that change much more feasible for the client. The change can be best fostered with big data being incorporated into different processes which are being carried out by financial institutes. Moreover, Chiu (2016) commented that this theory helps to understand why this change is required and what different is going to be introduced after this change is implemented.

2.     Theoretical framework of Big Data interventions

Behavioral Intervention Technology Model:  


This particular model works to align the aspect of big data with that of behavior which is introduced by different customers towards the financial organization. Banking was done in a traditional mannerism in the past and with time the intervention of online banking has brought in changed behavior in relation to different customers and has shown a positive behavior towards this particular aspect. Thus, this model is quite pivotal as it highlights different behavioral aspects which are being posted by the customers of this particular industry.


Behavior Change Theory:  This theory helps to understand what tools can be implemented to make the change much more flexible for extracting big data pattern and meaningful representation (Hasan et al. 2017). The effect which will be introduced by this change into the organization and client is also significant to be underlined.


When, how and why this concept of technological intervention has become attractive


Big Data has become a ubiquitous aspect of different organizations and even a crucial part of an individual's personal life. This aspect of technology is quite transparent and it has been taken as the holy Holy Grail on a desktop of different financial advisors and it has been significantly increasing efficiency. This has been beneficial to maintain a high level of service which has helped different clients gain an easier solution. This concept has been introduced into use in the late 1980s and has been a great inclusion towards each sector in the world (Gabor and Brooks 2017). Schooling and banking have gained substantial effect with the introduction of technological interventions.


This concept has made different processes much smarter and different equipment have been made easier to operate. Moreover, this has seen that there are several antecedents of the big data quality which are related to people, technology, process and several external aspects (Haryadi et al. 2016). In relation to the banking sector, there is increased efficacy and satisfaction among customers which have been received after the concept of technological interventions has been incorporated.


Big data helps in capturing, management, distribution and analysis of information. It helps in building better quality service delivery as well as outcomes of works. As opined by Haryadi et al. (2016), big data helps in maintaining financial records of several risks and working process. With the help of big data availability of financial data is exponentially growing in advanced devices, maintain and in taking electronic records and many more. Bog data helps to provide risk coverage in well manner. This generates the cost saving in significant way with the help of several automated process.


3.     Methodologies, hardware, software, tools applicable to technological intervention:


One significant methodology which is incorporated into banking sector is termed to be the transfer of technologies which helps the flow of know-how, different sets of equipment and experience which helps in mitigating change. Another significant technological intervention tool incorporated by sector of finance is termed to be statistical modeling implying supervised along with unsupervised classification problems. With the preprocessing of the data evaluation regarding various models is done with logical loss metrics and are further evaluated followed by the reporting of the data (Srivastava and Gopalkrishnan, 2015).



Different hardware peripherals are used in this sector with the help of big data analytics which include account management. Hardware in banks has been incorporated in the late 1960s which consisted of a framework and punch card machine. Different client-server hardware is incorporated which has been crucial to run a whole bank. In today's world different passbook printing machines, latest computer technologies and security hardware make an important part of banking. In relation to big data analytics there are software inclusions which include blockchain technology which has been fundamental in transforming this sector significantly. ATMs have been upgraded with latest technology which has made it much easier to work with cash withdrawal and deposits. Digital banking is another concept which has been a boon of technological intervention which every financial institute has incorporated.


Tools like voice-first banking, open banking, digital-only banking and forms of cyber-security have been introduced into baking sector with the help of big data analytics which are beneficial towards growth of different financial institutions. As opined by Kish and Leroy (2015), there are several aspects which need to be underpinned in relation to how behavior can be taped down with the introduction of different techniques by the financial sector to underpin what a particular customer is looking forward to. However, Laeven et al. (2015) contradicted that working with different sets of technology is quite significant towards bringing out the best possible customer service towards different customer base.

The aspect of big data analytics has been quite crucial towards financial institutions as they have to work their way out to underpin what technologies they need to introduce towards attaining best customer service. This research thus will bring out the best possible understanding of how impactful is a big data in relation to the sector of finance and how it churns out the best results pertaining to this particular sector.

4.     Technology implementation in financial sector


The technology is implemented in financial sector through various intervention sectors such as in fraud detection, safety and online banking procedures firstly. Online banking was the first implementation of technology usages in the banks before that people needs to go from place to place to get their money or usually needed to wait a lot. However, the Big Data technology improves the risk model and their predictive powers. This has seen that this technology develops the time of the responses of the systems and increase effectiveness as well. After the intranet and internet facility came to the open world banks are started to get their intranet system and through that technology was implemented in the financial sectors (Gabor and Brooks, 2016). Later, in the ATM machines via internet, technology was implemented in the financial sectors.


From the above study, it can be concluded that technological interventions play an essential role in the financial sector with the help of those interventions growth of financial sector can be increased at a significant rate which will be really beneficial for human society. From the literature review, part's framework and theory analysis of how big data interventions can be implemented for the benefit of the society can be cleared easily. Various challenges such as lack of awareness of customers, data breaching risks are also presented in the implications of intervention which needs to be improved in a significant manner to provide better services and gain more customer loyalty to the organization. Additionally, the use of the BIG data may assist the companies involved to optimise their inventory and at the same time, allow them to interact and optimise the customer databases. This may provide the ability to the organisations to stay ahead in terms of use of the advanced technology in the market and have a competitive advantage. Financial organizations can introduce various adverts to increase awareness of technological interventions. They also can conduct meeting with experts and create their network system to increase security risk management to gain support from the consumers.

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