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An important function of big data analytics (BDA) in the banking sector :

-Support in the development and visualization of various indicators of multiple internal and external processes’ ineffectiveness (Bhuvana et al., 2016). Such indicators recognition, for one, is in the case of fraudulent activities. For example, the implantation of big data within banks could aid in the detection of fraud within occurring monetary transactions and in the prevention of negative consequences (Bhuvana et al., 2016).

-Additionally, big data allow banks to cater to their clients various customized products that inclines their performance rate (Bhuvana et al., 2016).

-Algorithmic Trading:

Big data facilitates algorithmic trading by processing large volumes of financial data in real time.

Predictive analytics and machine learning algorithms are employed to identify trading opportunities, optimize trading strategies, and execute trades at high speeds.

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-Big data holds significant importance in financial institutions and banks as it helps to make effective decisions and develop effective policies. Structured and unstructured data is used to create better strategies and to anticipate customer behavior (Sun et al., 2014). We have used a bibliometric analysis to provide an in-depth understanding of the international institution’s role in the global arena (Winther et al., 2009). The procedure of bibliometric ensures how theoretical inquiry relationships can be formed Reynolds et al. (2006). It is essential to note that bibliometric review requires several techniques and software. Initially, dimensions databases were used in this study to gather relevant articles and journals, followed by a VOS viewer where dimensions database files were exported to create citation mapping. Finally, the contextual analysis was presented through tables and figures.

-On the other hand, new companies mostly rely on big data analytics to create innovative products and services. The banking industry also acts as an early stage regarding data-driven decision-making (Hassani et al., 2018a). When it comes to big data, it can be described as “large volumes of high velocity, complex and variable data that require advanced techniques and technologies to enable the capture, storage, distribution, management, and analysis of the information” (Hassani et al., 2018a).

-Big data analytics has proven to be a critical strategy to be implemented within the banking industry. Such significance is due to the growth of the collected and stored operational data, as it spikes the value of big data in the analysis of various internal business processes (Rakhman et al., 2019). It is to be noted that huge, stored data requires a more sophisticated big data mining tools installed. The standard big data software can only analyze 5% of the entire bank’s dataset at a time (Rahman & Iverson, 2015). Therefore, it is crucial for, in this case, banks to invest in an advanced data mining software to achieve an accurate analysis of bank’s data and potential risks. For example, Hadoop is a renown big data software common among service providers to analyze their historical data (Bhuvana et al., 2016).

-Furthermore, Al-Dmour et al. (2021) concluded in their study that the application of BDA will positively impact banks’ performances. Conclusively, Previous research group the big data functions in the banking industry under three categories: “customer relationship management (CRM), fraud detection and prevention, and risk management and investment banking” (Dicuonzo et al., 2019).

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challenges in implementing big data in finance

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challenges in implementing big data in finance

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Data security and privacy

The more extensive the data, the higher the risk of cybersecurity threats. Protecting sensitive customer information remains a significant concern, especially when banks collect and apply users’ data. The financial service industry must invest heavily in robust cybersecurity measures to mitigate these risks.

Regulatory compliance

Banks must adhere to various data storage, usage, and sharing regulations. Compliance becomes increasingly complex with the growing volume of data being processed, and non-compliance can result in severe penalties.

High implementation costs

Implementing big data technologies requires substantial hardware, software, and skilled personnel investment. Smaller financial institutions may find these costs prohibitive, thereby creating a competitive disadvantage.

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differences between databases, data warehouses & data lakes

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differences between databases, data warehouses & data lakes

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Databases

-A database is used to store, search and report on structured data from a single source. They are the simplest to create and SQL can be used to query and report on the data. There are both open source and proprietary databases, making it widely accessible to install and start using on premise or on the cloud.

-A relational database require schemas and are not a fit for unstructured or semi-structured data. Because of this rigid schema, they are not suited to be the centralized place to store data from multiple sources where the raw data varies in format and structure. However, they are popular for data analysis and monolithic applications

Data warehouses

-A data warehouse is used to store large amounts of structured data from multiple sources in a centralized place. Organizations invest in building data warehouses because of its ability to deliver business insights from across the company, and quickly.

-The process of creating a data warehouse requires some heavy lifting in the planning and design stage of examining data structures. Data warehouses are preferred by the business and operations decision makers of the company and a good system justifies its often high costs in proprietary software and storage.

Data lakes

-A data lake stores structured, semi-structured and unstructured data, supporting the ability to store raw data from all sources without the need to process or transform it at that time.

-Only when the data needs to be retrieved, will some structure need to be applied, which is ideal in the hands of data scientists and data analysis developers who can create new data models on the fly but does not provide the same reporting capabilities and ease of use for business users. Storing data in data lakes is much cheaper than in a data warehouse. Data lakes are very popular in the modern stack because of its flexibility and costs but they are not a replacement for data warehouses or relational databases.

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Differences between databases, data warehouse and data lake

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Differences between databases, data warehouse and data lake

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A database stores the current data required to power an application.

A data warehouse stores current and historical data from one or more systems in a predefined and fixed schema, which allows business analysts and data scientist to easily analyze the data.

A data lake stores current and historical data from one or more systems in its raw form, which allows business analysts and data scientists to easily analyze the data.

Databases, data warehouses and data lakes each have their own purpose. Nearly every modern application will require a database to store the current application data.

Organizations that want to analyze their applications current and historical data may choose to complement their databases with a databases with a data warehouse, a data lake or both.