Rationalising Factors Influencing the Effective Utilisation of Big Data in Malaysian Fintech Companies

23-004


Introduction
Financial services are being transformed by digital innovation, slowly but clearly. The world has seen the emergence of new financial technology innovations like mobile money, crowdfunding, robo-advice, insurance technology (insurtech), and cryptocurrency. Fintech has already improved retail users' access to and convenience with financial services during the past decade (Feyen et al., 2021). Fintech, or financial technology, is typically seen as a driving force behind disruptive innovation in the area of finance, seeking to reshape the financial services while also capturing customers' demands (Lee et al., 2018).
According to Chen and Zhang (2014), Big Data is a term used for datasets that come in a huge volume and complexity that conventional data processing application software are incapable to deal with them. Large volumes of data can now be processed and analysed by organisations at unprecedented speeds. Businesses are now able to use data-driven insights to optimise their operations and spur growth, which has revolutionised the way they make choices. Davenport et al. (2012) pointed out that businesses that invest in Big Data can be distinguished from standard data analysis environments in three ways: 1. They focus on data flows rather than looking at data to assess past events. 2. Instead of data analysts, they are relying more on data scientists and product and process developers. 3. They are integrating Big Data Analytics into core business, operations and production functions, rather than treating it as a data silo.
Big data has been at the core of businesses like Google, eBay, LinkedIn, and Facebook since the beginning (Davenport and Dyche, 2013). The digital transformation has been expedited by the COVID-19 pandemic, and this is evident in the closure of bank branches across the world due to the shift to digital banking (Macknight, 2021). In particular, it will become ever more crucial that digital connectivity eventually replace physical contacts between consumers and suppliers (Feyen et al., 2021). Press (2016) highlighted ten Big Data technologies: NoSQL databases, Predictive analytics, search and knowledge discovery, in-memory data fabric, distributed file stores, integration of data, data virtualization, stream analytics, data preparation, and data quality.

Fintech and Big Data in Malaysia
Fintech is an emerging industry in Malaysia; at the time of writing, the Malaysian Fintech sector grew by 27 per cent in 2021/22 to 294 fintech companies (Fintech News Malaysia, 2022), According to Fintech News Malaysia (2022), the payments sector continues to dominate the industry with 60 companies, followed by lending (55), e-wallets (43), and insurtech (31).
Over the years, the Malaysian government has introduced and implemented a number of initiatives to help businesses digitise, promote digital literacy and improve performance. For example, the establishment of the Malaysian Digital Economy

Research Focus
As Fintech continues to grow, there is a need to understand how different elements, such as Big Data, can be used in order to have effective operations; however, there is not enough research conducted on Big Data in Fintech companies as most reports are made by popular press which lacks of theoretical insight (Mikalef et al., 2020). As a result, there is a limited understanding of how organisations can leverage Big Data technologies and align them with their business strategy (Kwon et al., 2014;Mikalef et al., 2020;Sivarajah et al., 2017). In order to derive meaningful theoretical insights and practical implications, it is important to understand the barriers to and readiness for technology adoption, and whether the benefits outweigh the costs and risks of adopting new technologies. The purpose of the study is thus to determine the factors having considerable impact on Big Data utilisation in Fintech companies. Consequently, this paper seeks to answer two relevant questions: 1. How can Fintech companies benefit from using Big Data? 2. What factors influence and hinder the use of Big Data?

Methodology
The approach employed in this study involves an exhaustive library search and literature review on Malaysia Fintech companies. The library search consists of online content, ranging from government reports, magazines, newspaper to journal articles.
The following keywords were used to identify relevant articles from the Google Scholar and Scopus database: Big Data, Challenges, Factors, Malaysia. The authors limit the library search to the literature that was published between 2012 and 2023. The authors also pay attention to the: • Types of publication (e.g. newspaper articles, industry reports, academic literature). • Industry type (e.g. manufacturing, services) Fintech is increasingly becoming a vital part of Malaysia's financial sector. Malaysia's traditional banks are still dominant in deposit, lending and raising capital, but these banks are also embracing new technology, either in competition or in partnership with new technology start-ups (International Monetary Fund, 2020). The rise of cashless payments has led to a demand for skilled digital workers, especially in banks, which have a strong interest in creating in-house tech teams that include Big Data, user interface and analytics experts to digitise both product offerings and internal processes (Marketing Interactive, 2019). For instance, Malaysia's AmBank Group has set its sights on open source DevOps and data science in order to meet the growing needs of its customers and improve the overall banking experience (Yapp, 2020). More recently, Mastercard has established a Data and Services (D&S) hub in Kuala Lumpur to support its clients across the Asia Pacific region (Fintech News Malaysia, 2023).
A study conducted by Wong et al., (2015) on a sample size of 132 Malaysian largescale enterprises revealed that the companies are relatively ready for Big Data implementation. About 82% of the companies surveyed had a decent level of capabilities and competencies to gain the benefit of Big Data analysis; however, this only indicates the attitude towards using Big Data and further investments in technology and skills are required to tap into Big Data economy.
To sum up, Big Data usage in Malaysia is increasing but it is not widely used yet. Malaysian Fintech companies mostly rely on consultancy or outsource the Big Data functions, although the situation is slowly changing especially in the banking sector.

Rationalising Factors Influencing the Effective Utilisation of Big Data in Malaysian Fintech Companies
International Journal of Management and Applied Research, 2023, Vol. 10, No. 1 -49 -Next section explores the perceived benefits and risks of using Big Data technology and the associated challenges.

Perceived Benefits of Using Big Data
The key assumption behind the use of Big Data is that companies can gain competitive advantage by improving organisational performance and strengthening organisational capability. Yadegaridehkordi et al. (2018) found that perceived benefits is one of the most important drivers of Big Data adoption and organisational performance. More specifically, Yadegaridehkordi et al. (2018) collected data from 234 industrial managers who work in Malaysian manufacturing companies and the findings showed that, technological drivers -perceived benefits, complexity, technology resources, Big Data quality and consolidation -are the most influential for Big Data adoption and business performance. Similarly, Ali et al. (2021) found that that Big Data has a significant impact on social performance and environmental performance of Malaysia's Islamic banks.
One of the benefits of Big Data and business analytics is that it can help improve decision making. Abdullah et al. (2022) examined the role and impact of Big Data on strategic management accounting practices in a Malaysian manufacturing company. Referred as Choco1, the case study shows that Big Data enabled Choco1 to identify, assess and reduce product costs, work on product design according to customer feedback, reduce waste, and determine profit margins and pricing. Big Data provided Choco1 with costing and pricing information on which to forge its strategies. This information included the pricing of similar products on the market, the price that customers are willing to pay, customer reviews and expectations of the product, and information on raw materials from potential suppliers.
Big Data Analytics also helps logistics companies improve productivity and overall business competitiveness by enabling warehouse operators to make effective decisions, track and correct errors, forecast future demand, monitor and optimise inventory levels (Wahab et al., 2021). In a study of examining the impacts of Big Data on supply chain performance in Malaysia, Xiang et al. (2021) concluded that several factors have a significant relationship with supply chain performance in the logistics industry, these are: improved forecasting, supply chain system integration, human capital, and risk and security governance. Big Data Analytics is useful to efficient delivery services in a country like Malaysia, with a population of around 35 million people spread over a vast geographical area. Adopting Big Data Analytics enables company like Cainiao Smart Logistics Network to simplify and streamline warehouse processes, including procurement, storage, customs clearance and goods delivery (Wahab et al., 2021).
Another benefit of using Big Data is that it can help companies in managing risk, such as fraud detection and credit risk assessment. Banks can more accurately determine the creditworthiness of new loan applicants or assess the credit risk of existing customers by evaluating data such as credit scores, payment histories, education levels, employment information and utility payment histories (Siew and Farouk,

Rationalising Factors Influencing the Effective Utilisation of Big Data in Malaysian Fintech Companies
International Journal of Management and Applied Research, 2023, Vol. 10, No. 1 -50 -2023). Big Data Analytics can be instrumental in the identification of fraud and the prompt notification of insurers of suspicious claims. Takaful (Islamic insurance) operators could more effectively deter, detect, record and report fraud using Big Data Analytics tools (Hemed et al., 2021).
Big Data is being used in the retail industry to gain a better understanding of buying behaviour, to capture fluctuations in buying behaviour in real time and to make strategic decisions for the business on this basis. A personalised online shopping interface typically led to positive customer satisfaction and repurchase intention (Chan et al., 2021). Many e-commerce platforms use Big Data to personalise the shopping experience for customers, provide recommendations based on search history, and dynamic pricing (Abdullah et al., 2021; Chan et al., 2021; Sek, 2019). Additionally, social media data also collected as a reference to see the pattern of posting about buying and selling among users on social media platforms (Shayaa et al., 2018). As the largest ecommerce platforms in Southeast Asia Shopee is a Big Data driven business which uses data to improve the user experience, to help shoppers find the right products, and to automate the business processes of internal teams to improve the efficiency of their work and reduce costs (Sek, 2019). Repurchase intention and overall customer satisfactions are driven by the information search, recommendation system, dynamic pricing and personalisation (Chan et al., 2021).

Perceived Risk of Using Big Data
There are numerous security and privacy issues related to infrastructure, processes and protection mechanisms for organisations already involved in Big Data initiatives or planning to jump on the Big Data bandwagon (Salleh and Janczewski, 2019). Mitigating the risk of cyber-attacks has become a challenging task to ensure the integrity of Big Data environments, as the number of cyber-attacks around the world is on the rise. In order to avoid data breaches, it is also essential to secure large volumes of heterogeneous data by applying appropriate and cost-effective data anonymisation and encryption techniques to ensure that sensitive information is not personally identifiable (Anawar et al., 2022).
Data security in financial services has always been a priority, especially now that the industry is undergoing a massive digital transformation as a result of changes in consumer behaviour, intensified competition and technological advances (Mohsin et al., 2022; Salleh and Janczewski, 2019; Siew and Farouk, 2023). The adoption of new technologies to support better capabilities and scalability is driving the trend towards a new, all-digital customer experience. However, the risks and costs of digital transformation are increasing due to the combined challenges of cybersecurity threats and a more stringent regulatory environment (Salleh and Janczewski, 2019). Financial services organisations need a technological solution that can optimise and automate data-centric protection across their hybrid IT environments.

Rationalising Factors Influencing the Effective Utilisation of Big Data in Malaysian Fintech Companies
International Journal of Management and Applied Research, 2023, Vol. 10, No. 1 -51 -

Challenges In Adopting Big Data
The adoption of Big Data in decision making is a lengthy and complex process that may result in project failure or in worst case scenario, business failure. In a case study of a Malaysian commercial bank BankX which is the outcome of multiple mergers for the years, Siew and Farouk (2023) reported that the first problem BankX faced when adopting Big Data is system migration and data integration because each bank had its own system and if data is transferred from one legacy system to another without proper conversion, it may not be recognised or interpreted correctly, resulting in data errors. Other reasons cited by BankX for delays in implementing Big Data included working with multiple divisions or departments, sitting on committees, and the lengthy process of obtaining approvals from the Central Bank of Malaysia (Siew and Farouk, 2023). This naturally leads to another major barrier to Big Data adoption: organisational factor.  2017) reported that Malaysian IT officers lack the necessary skills as data scientist, and the authors outlined seven skillsets that are deemed mandatory for IT officers: analysis, data visualisation, data modelling, decision making, ethics, communication, and basic database knowledge. Data scientists are expected to collect data in an ethical manner, perform appropriate analysis, and deliver successful results (Anawar et al., 2022). Organisations, in turn, need to ensure that all the necessary roles are in place for Big Data projects, while specifying the skills required for the increasingly diverse roles (e.g. data engineer, AI specialist and product manager). To a great extent, the Big Data Analytics adoption behaviour significantly depends on the type of industry. For instance, the complex work processes in banking, with lengthy governance and approval procedures, limits its agility compared to start-ups

the "✓" refers to the presence of the factors)
Although these studies have different emphasis and management support is not explicitly listed in their TOE framework, all mentioned that management support is crucial for Big Data adoption.
Apart from the study by Yadegaridehkordi et al. (2020), none of these studies mentioned organisational size as an influential factor. The size of the firm has usually been seen as influencing the acceptance and use of new technologies because of limited resources, but smaller firms may have the advantage of simplified structures and faster decisions (Maroufkhani et al., 2023). Therefore, firm's size is considered as

Data Privacy and Security
Increasingly, cyber-attacks appear in the form of theft or compromised data, and have the potential for physical damage to critical infrastructure of Big Data (Tao et al., 2019). For example, hackers use malware to encrypt, delete or manipulate data, intellectual property or personal information in a ransomware attack. Potential financial gain, whether through blackmail, ransoming or intellectual property theft, is often a motivating factor for hackers. Data privacy is one of the biggest concerns for telecommunication operators (Anawar et al., 2022) and banks (Salleh and Janczewski,

Rationalising Factors Influencing the Effective Utilisation of Big Data in Malaysian Fintech Companies
International Journal of Management and Applied Research, 2023, Vol. 10, No. 1 -55 -2019) for adopting Big Data. The challenge requires telecommunication operators and banks to protect data privacy while meeting business needs and profits. Therefore, based on the above arguments, the following proposition is formulated: P1: Data Privacy & Security positively influence the effectiveness of BDT utilisation.

Complexity
The complexity of the technology is one of the biggest barriers to adoption. Technology is more likely to be adopted when it can be easily integrated into business operations. In a study on the use of Big Data in Malaysian SMEs, Maroufkhani et al. (2023) found that complexity has a negative impact on top management support. This is because the complexity of Big Data Analytics can compel the managers to doubt the investment in Big Data due to a lack of resources and skills. The following proposition is thus formulated: P2: Complexity of Big Data Technology negatively influences the effectiveness of BDT utilisation.

Perceived Benefits
Executives expect a return on investment from any new technology implementation (Maroufkhani et al., 2023). This can be in the form of tangible or intangible results, such as productivity or improved financial performance. The key concern is whether the perceived benefits will outweigh the costs and the perceived risks. Based on this, the following proposition is formulated.
P3: Perceived Benefits of Big Data Technology positively influence the effectiveness of BDT utilisation.

Technical Human Skills
Technical expertise is critical to the successful utilisation of Big Data Technology. However, studies have shown that there is a shortage of necessary skills required to work with Big Data among IT officers (Abidin et al., 2017) and telecommunication providers (Anawar et al., 2022). Therefore, based on the earlier studies, the following is proposed: P4: Technical human skills positively influence the effectiveness of BDT utilisation.

Technological Resources
Organisations with technology resource capability are likely to embrace Big Data Technology more quickly than others. This is in line with Resource-Based View that BDT is an innovative IT capability and a strategic resource for competitive advantage (Kwon et al., 2014). The following proposition is thus reached. P5: Technological resource competency positively influences the effectiveness of BDT utilisation.

Rationalising Factors Influencing the Effective Utilisation of Big Data in Malaysian Fintech Companies
International Journal of Management and Applied Research, 2023, Vol. 10, No. 1 -56 -

Organization Size
Organisation size is defined by number of employees. The availability of resources is typically in proportion to the size of the organisation (Baig et al., 2019). Larger firms tend to have more resources than smaller ones, and are therefore better placed to adopt new technologies. However, small and medium-sized enterprises (SMEs) would be able to react quickly to the introduction of Big Data Analytics, as their high level of flexibility and less hierarchical system would enable them to make decisions and implement strategies faster than larger companies (Maroufkhani et al., 2023). Therefore, on the basis of the arguments above, the proposition is as follows: P6: Organizational size positively influences the effectiveness of BDT utilisation.

Financial Capacity
Access to finance is one of the most cited factors in previous studies (Maroufkhani et al., 2023;Wahab et al., 2021;Yadegaridehkordi et al., 2020). The cost of developing or utilising Big Data technology and the need to hire Big Data professionals are obstacles for SMEs due to limited resources (Maroufkhani et al., 2023). The following proposition is made in light of these considerations.
P7: Financial Capacity positively influences the effectiveness of BDT utilisation.

Perceived Risk
Perceived risk is one of the critical factors that most executives take into account when making a financial decision. The utilisation of Big Data is often associated with high costs, uncertainties, complexity and risks (Baig et al., 2019). The cost of new technology adoption increases as the level of complexity and risk increases. Therefore, the following proposition is derived to.
P8: Perceived risk negatively influence the effectiveness of BDT utilisation.

Management Support
Management support is frequently cited as a crucial success factor to utilisation of BDT in earlier empirical research (Maroufkhani et al., 2023;Wahab et al., 2021;Yadegaridehkordi et al., 2020). Management support is needed to encompass organisational policies, rules, dealing with data storage issues, technological and financial capabilities (Baig et al., 2019). If management is reluctant to change or set a strict limit on budget, then the whole organisation may have difficulty to optimise Big Data due to resource constraints, which will delay the adoption process. Management support is equally important for smaller firms. As smaller companies tend to have a more straightforward management structure, support from top management is particularly important in SMEs (Maroufkhani et al., 2023). Thus, the following proposition is formulated. P9: Management support positively influences the effectiveness of BDT utilisation.

Rationalising Factors Influencing the Effective Utilisation of Big Data in Malaysian Fintech Companies
International Journal of Management and Applied Research, 2023, Vol. 10, No. 1 -57 -

Regulatory Intervention
Government plays a vital role in enabling or hindering new technology adoption. Policies and initiatives that encourage new technology adoption are often considered as a positive sign by all stakeholders (Maroufkhani et al., 2023), whereas laws and regulation that are designed protect privacy restricts the accessibility of data and hinders the use of Big Data to a certain degree (Baig et al., 2019). The following proposition is therefore formulated on the basis of the above argument: P10: Regulatory intervention positively influences the effectiveness of BDT utilisation.

Vendor Support
For organisations looking to adopt Big Data, vendor support such as training and technical support is considered as a prominent factor by telecommunication providers (Anawar et al., 2022) and IT professionals working in the banking sector (Salleh and Janczewski, 2019). Therefore, on the basis of the above argument, the proposition is as follows: P11: Vendor support positively influence the effectiveness of BDT utilisation.

Discussion
In practice, data-driven transformation has proven to be a multi-year journey, not without missteps and failures over time, and is a complex process that requires the alignment of business, technology and organisational perspectives and practices (Kwon et al., 2014). It requires an organisational change of mindset, thinking and approach to running a data-driven business (Bean, 2020 The Technology-Organisation-Environment TOE framework has emerged as the most common model for understanding and analysing the Big Data adoption factors in the reviewed academic studies. These studies have different research objectives and research designs, and each has diverse findings with regard to the significance of each variable. There is a need for researchers to propose, validate and extend the existing theoretical models in order to fully cover the findings according to research context. In the Malaysian Fintech context, there has been limited theoretical research on the use of Big Data. Therefore, to fill in the research gap, the authors of this study formulate twelve research propositions and suggest a research model based on existing empirical research. By its very nature, the financial industry is changing rapidly. New start-ups are entering the competitive landscape with new technologies or user-friendly apps that offer many different ways to invest or save money. Unlike SMEs in other industries, the Fintech industry is tech-savvy and thus Fintech companies have more technological resources compared to SMEs in other industries. Small Fintech companies could continue to thrive as they tailor their products and services to a specific market segment (Feyen et al., 2021) with continued improvements within their product and service offerings. Larger players in the financial industry, namely risk-averse banks (Samad, 2017), are slowly entering the Fintech space. Compared to start-ups, banks' agility is limited by complex work processes with lengthy governance and approval procedures (Siew and Farouk, 2023).

Conclusion
With the growing importance of Big Data in Fintech, there is a need to assess the influencing factors that determine the use of Big Data technology in Malaysian Fintech companies. This study seeks to address the following research questions: