Big Data in the Finance and Insurance Sectors SpringerLink

In some ways they are like the Uber or Airbnb of the asset management industry. For example, social trading platforms allow retail and institutional investors to copy the trades of other traders. And, robo investing platforms automatically create and rebalance portfolios.

How is big data being used in trading

Machine learning, predictive modeling and artificial intelligence tools are now widely deployed and becoming mainstream capabilities for leading enterprises. The types of data being collected, stored and analyzed get more diverse with every new generation of technology. More recently, a broader variety of users have embraced big data analytics as a key technology driving digital transformation. Users include retailers, financial services importance of big data firms, insurers, healthcare organizations, manufacturers, energy companies and other enterprises. Big data analytics can provide insights to inform about product viability, development decisions, progress measurement and steer improvements in the direction of what fits a business’ customers. Big supply chain analytics utilizes big data and quantitative methods to enhance decision-making processes across the supply chain.

Data analysis

It is likely that some edges will be eroded as more learning algorithms find relationships between data sets. The result will be that only those firms committed to finding new data and new methods of using A.I. Regardless of the type of algorithm, or the available processing power, the algorithms predictive ability depends on two attributes of the data being used. Firstly, there needs to actually be a pattern or relationship within the data set. And, secondly, the data sample used for learning needs to be large enough.

How is big data being used in trading

The strategy will increase the targeted participation rate when the stock price moves favourably and decrease it when the stock price moves adversely. Buying a dual-listed stock at a lower price in one market and simultaneously selling it at a higher price in another market offers the price differential as risk-free profit or arbitrage. If you see the price of a Chanel bag to be US$5000 in France and US$6000 in Singapore, what would you do? This is risk free profit at no cost, by earning a spread between the 2 countries. Similarly, if one spots a price difference in futures and cash markets, an algo trader can be alerted by this and take advantage. It can be tough for traders to know what parts of their trading system work and what doesn’t work since they can’t run their system on past data.


Also, Tariq et al. emphasized that software automation plays an important role in e-health and generally improves health care services for individuals by bringing efficiency to the systems. Moreover, many countries around the world are trying to smooth out the curve of the epidemic with the help of smartphone apps. Because of this, it is very important to carefully carry out strategies to protect data privacy when utilizing big data. Improvements in technology can offer several benefits, but they can also pose a risk of breaching privacy. Governments and companies in many industries use big data as a basis for automating processing and extracting important insights to aid decision-making. While big data has been confirmed as being useful in analysis and prediction, it is important to implement security procedures for maintaining confidential data on big data systems (Haafza et al., 2021; Rafiq et al., 2022).

How is big data being used in trading

In the past few years, big data in finance has led to significant technological innovations that have enabled convenient, personalised, and secure solutions for the industry. As a result, big data analytics has managed to transform not only individual business processes but also the entire financial services sector. The best stock market data analytics solution can track, centralize, integrate, and process all the relevant data needed — a financial institution’s own organizational data, the broader capital markets data, and other streaming data feeds. They can be roughly divided into pre-built models and personalized solutions. As a testimony to the opportunities opened by Big Data on the international scene, customs offices worldwide seized the opportunity to leverage Big Data technology. New Zealand Customs Services developed a new strategy for intelligence-led decision-making based on their collected data.

Role of Big Data in Algorithmic Trading

Along with vast historical data, banking and capital markets need to actively manage ticker data. Likewise, investment banks and asset management firms use voluminous data to make sound investment decisions. Insurance and retirement firms can access past policy and claims information for active risk management. Big data transforms trading strategies by empowering traders to make better-informed decisions regarding when to purchase and sell securities.

  • This list isn’t exhaustive, but these are the most high-value benefits big data provides to financial companies.
  • The financial and insurance industry has vast repositories of structured data in comparison to other industries, with a large amount of this information having its origin inside the organization.
  • Companies must examine where their data is heading and growing, instead of focusing on short-term, temporary fixes.
  • According to a 2020 study by MicroStrategy, businesses that use big data-driven processes will likely have a 43% competitive advantage over their rivals.
  • Social media use also has a lot of potential use and continues to be slowly but surely adopted, especially by brick and mortar stores.
  • Passive fund management, which in many ways overlaps with quant funds took off in the 1990s when exchange traded funds were introduced.
  • Mutual funds expanded the potential client base for asset management companies greatly.

Because data is sourced from so many different systems, it doesn’t always agree and poses an obstacle to data governance. Data management solutions ensure information is accurate, usable, and secure. While some pre-built data analytics frameworks don’t require experience to use, those still need some level of technical support with implementation and data integration to set up and onboard. Algorithmic trades can also be used to rebalance investment portfolios automatically. For instance, if the application analyzes a portfolio with an 80/20 stock-to-bond allocation and finds it uneven, it can sell stocks to purchase more bonds whenever the stock value reaches the 80 percent threshold. Most importantly, with a constantly growing amount of data available, it could also teach itself to predict future markets.

Provide better strategy for customers

The processing time for many applications is reduced in parallel processing. Being able to store unstructured data has boosted flexibility with onboarding and retrieving data. This is crucial when looking for data from non-traditional sources and while managing large amounts of textual information. This is arguably one of the biggest ways that the stock market is responding to changes in big data. Firstly the trading system collects price data from the exchange , news data from news companies such as Reuters, Bloomberg. Some algorithm trading systems may also collect data from the web for deep analysis such as sentiment analysis.

How is big data being used in trading

Predicated upon years of excellence, the New York Stock Exchange has evolved tremendously.“BIG DATA”is the power horse that shares all data throughout the network of each stock market. Finally, employ these data mining techniques on other stock markets, such as GCC, Middle East countries and global markets. The three highest variables that affect the direction of the rate of returns in Boursa Kuwait are firms, the Kuwait index and EPS. The decision tree is used to predict the direction of the rate of return in Boursa Kuwait and is constructed by RStudio software. Table 1 shows the confusion matrix of multinomial logistic regression for training data and testing data.

for Big Data in the Finance and Insurance Sectors

The strategy focused on a large volume of coordinated, personalized marketing communications across multiple channels, including email, text messages, ATMs, call centers, etc. Gartner defines Big Data as the high-volume, high-velocity and/or high-variety of information assets that demand cost-effective, innovative forms of information processing to enable enhanced insight, decision making, and process automation. As time goes by, the benefits of big data will be largely impactful as business activities continue to pose a huge environmental risk and many people begin investing dependent on the impact of these businesses. Companies that fail to consider the environmental and social factors that determine the investing decisions people make will likely face risks they’re not currently thinking about. Big data is changing the nature of the financial industry in countless ways.

Applications of Big Data in Government

Besides being incredibly useful, big data is expected to grow to an astounding $274 billion by the end of 2022.

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