Thinking about the influence of big data on the financial sector and its services, the process can be highlighted as a modern upgrade to financial access. In particular, online transactions, banking applications, and internet banking produce millions of pieces of data in a single day. Because managing these internet financing services has major impacts on financial markets .

Big Data in Finance

Because data is sourced from so many different systems, it doesn’t always agree and poses an obstacle to data governance. The technology is already available to solve these challenges, however, companies need to understand how to manage big data, align their organisation with new technology initiatives, and overcome general organisational resistance. The specific challenges of big data importance of big data as related to finance are a bit more complex than other industries for many reasons. Companies like Slidetrade have been able to apply big data solutions to develop analytics platforms that predict clients’ payment behaviours. By gaining insight into the behaviours of their clients a company can shorten payment delay and generate more cash while improving customer satisfaction.

Data Extraction

An estimated 84 percent of enterprises believe those without an analytics strategy run the risk of losing a competitive edge in the market. The finance industry is faced with stringent regulatory requirements like the Fundamental Review of the Trading Book that govern access to critical data and demand accelerated reporting. Innovative big data technology makes it possible for financial institutions to scale up risk management cost-effectively, while improved metrics and reporting help to transform data for analytic processing to deliver required insights.

Big Data in Finance

With Lenddo’s algorithm, your credit score falls if you are online friends with a delinquent or frequently use negative words such as “car accident” or “unemployed” on your social media. In the case of small businesses, Lenddo ascertains credit rating based on the business’s reputation and activities. Amex has received positive customer feedback for their personalized, social media based marketing campaign. With Amex Sync, customers can connect their social media accounts to their AMEX credit card to receive personalized discount offers. For example, if a customer likes a brand or restaurant on Facebook or Twitter, companies are able to provide discount coupons and relevant information directly to the customer, creating more targeted marketing opportunities than ever. The technology behind smartphones, tablets, and the Internet of Things has made it easier than ever for consumers to use online resources to communicate with companies, research products, purchase items, and even perform banking tasks.

Financial Markets and Investment Analysis

In this sense, the concept of data mining technology described in Hajizadeh et al. to manage a huge volume of data regarding financial markets can contribute to reducing these difficulties. Managing the huge sets of data, the FinTech companies can process their information reliably, efficiently, effectively, and at a comparatively lower cost than the traditional financial institutions. In addition, they can benefit from the analysis and prediction of systemic financial risks .

  • So extracting and analyzing big data can provide insights for investors when making investment decisions.
  • Last but not the least, the quality of data that you get to work with, actively determines the quality insights you base your decisions on.
  • This helps to reduce the risks for financial companies in predicting a client’s loan repayment ability.
  • Therefore, financial institutions should re-look at the process of collecting data.
  • Even worse, if employees using the technology uncover insights for improvements that get ignored because management isn’t prepared for change, it can have a negative and deflating effect on the morale and motivation of employees.
  • More complex datasets create value for finance researchers if they measure economic activities that cannot be captured using simpler data.
  • We believe such collaborations will expand the tools and scope of research in finance and economics and help researchers overcome big data challenges.

The advent of cloud storage and computation services, however, comes at the expense of data security and user privacy. In DSS, visualization is an extremely useful tool for providing overviews and insights into overwhelming amounts of data to support the decision-making process. Model-driven DSS emphasises access to and manipulation of statistical, financial, optimization, and/or simulation models. Models use data and parameters to aid decision-makers in analysing a situation, for instance, assessing and evaluating decision alternatives and examining the effect of changes.

How Is Big Data Being Used in Finance?

With big data sources available to them, ABC Insurance is able to implement machine learning algorithms that predict risk on a case-by-case basis. Pulling from the huge amounts of data available in real-time, the outcome is highly predictive and free from human error. Just because big data contains an enormous amount of information, it does not mean that it reflects a representative sample of the population. Therefore there is a risk of misinterpreting the information produced and liability may arise where reliance is placed on that information. This is a factor that financial services organizations have to take into account when looking at using big data in analytical models and ensuring that any reliance placed upon the output comes with relevant disclaimers attached. Any information related by a third party that is subject to big data analytics is likely to be confidential information.

CFI is the official provider of the Business Intelligence & Data Analyst ®certification program, designed to transform anyone into a world-class financial analyst. Data privacy is another major concern tied to the implementation of cloud computing technologies. Companies are worried about putting proprietary information in the cloud, and though some have created private cloud networks, such projects can be costly.

What is Big Data in Financial Services?

This helps to reduce the risks for financial companies in predicting a client’s loan repayment ability. In this way, more and more people get access to credit loans and at the same time banks reduce their credit risks . The concept of big data in finance has taken from the previous literatures, where some studies have been published by some good academic journals. This paper seeks to explore the current landscape of big data in financial services. Particularly this study highlights the influence of big data on internet banking, financial markets, and financial service management. This study also presents a framework, which will facilitate the way how big data influence on finance.

Big Data in Finance

As a result, hundreds of millions of financial transactions occur in the financial world each day. Therefore, financial practitioners and analysts consider it an emerging issue of the data management and analytics of different financial products and services. Therefore, identifying the financial issues where big data has a significant influence is also an important issue to explore with the influences. The connection between big data and financial-related components will be revealed in an exploratory literature review of secondary data sources. Since big data in the financial field is an extremely new concept, future research directions will be pointed out at the end of this study. The Finance and insurance sector analysis for the roadmap is based on four major application scenarios based on exploiting banks and insurance companies’ own data to create new business value.

Real-Time Analytics and Marketing

Data science has improved financial services by speeding up processes that would have usually taken a long time. For example, SafeGraph helped one of their financial services clients by providing them with data to assess whether or not customers would walk into a bank during the COVID-19 pandemic. This helped the client make an accurate assessment of how the pandemic would affect that particular bank, and aided the bank in making the right business decisions moving forward. Investors can rely on machine learning’s unbiased output from alternative and financial data to predictive analytics, helping identify potential risks or great investment opportunities. Banks use these strategies to analyze business borrowers’ potential defaults, for example.


The data being presented by your analytics solution needs to be safe, accurate, and actionable. Only then can you confidently take data-backed decisions that will positively impact your enterprise. The rapid evolution of technology and the adoption of IoT devices has led to a massive surge in Big Data. Legacy systems are becoming increasingly incapable of handling the volume, variety, veracity, and velocity of the data influx. Data management is technology dependent, and you have access to powerful tools that can help manage your data and extract actionable intelligence. “The chief aim of data analytics is to apply statistical analysis and technologies on data to find trends and solve problems,” said Thor Olavsrud, contributor to CIO.