Mercurius is an Italian fintech startup that aims at assetizing sports betting markets through the usage of artificial intelligence and machine learning technologies. Founded in 2018 it released Tradr in 2019 delivering positive results to its users since then.

Big data: what it is and how it is changing finance.

Everyday computers process 2.5 quintillion bytes as a crucial element in choosing the right investments in financial markets and algorithms generate 60-70% of stock exchange trades. Artificial Intelligence is playing a huge role in the financial sector, changing it in unexpected ways.

According to the report by the research agency Gartner, in 2021 AI will generate $2.9 trillion in business value and recover 6.2 billion hours of worker productivity, while according to Cb Insights, from 2013 to 2017, the world’s leading banks invested $118 billion in FinTech alone


Technology is clearly growing at an exponential rate and its consequences are hard to foresee. The increasingly complex technology and generating data are changing the way sectors work and finance is no exception. In trading financial assets, machine learning and algorithms are increasingly used to process large amounts of data, perform financial forecasts and make decisions that human beings cannot make.

Trading financial instruments needs accurate inputs in decision-making business models. Numbers were traditionally processed by human beings and decision-making was subsequently based on expected risks and trends. Today computers perform these same tasks, processing data on a massive scale and drawing conclusions almost instantaneously thanks to several different resources.

Let’s take a look at 3 big data implementations

The 3 most important application of big data

  1. Big data analytics in financial models

    Financial analytics is no longer just the analysis of prices and their behaviour, it also takes into consideration factors that could affect them, social and political trends and their levels of support and resistance.

    Big data analytics can also be used in predictive models to estimate return rates and the many possible outcomes of various investments. Access to big data is becoming increasingly larger, resulting in predictions that are more precise and the ability to lessen the inherent risks of financial trading in a more efficient way.

    High frequency trading has always been used, with machines trading without human input. However, the processing time frame rules this method out because seconds are of the essence in this kind of trading and big data need more processing time. However, the paradigm is changing because traders are realising the value and the advantages of accurate extrapolations performed thanks to big data analytics.

  2. Real time analytics

    Algorithmic trading is the latest trend in finance. Machine learning made it possible for computers to make decisions like human beings and trade at such high speeds and frequencies that they can’t be achieved by people. The essence of business encompasses the best possible prices, traded at specific times, and reduces the problem of manual input mistakes.

    Real time analytics have the potential to improve the investing power of companies and people that use high frequency trading algorithms since the data obtained by algorithmic analysis give access to important information.

    Algorithmic trading is such a powerful tool because in theory its abilities are limitless. Structured and unstructured data, like social media, stock market information and news analysis, can be used to make intuitive judgements. This analysis of the situational sentiment is very valuable given that the stock market is easily influenced

  3. Machine learning (Artificial Intelligence)

    We haven’t developed the entire potential of this technology yet, thus the prospects for future applications are countless. Machine learning enables computers to learn and make decisions using information coming from past mistakes and logic. In this way, these techniques can provide incredibly accurate perceptions. Even if this technology is still developing, its possible applications are extremely promising. This branch of research removes the human emotional response and makes decisions based on unbiased information.

    Machine learning allows investors to use huge databases (i.e. posts on social media) to analyse market sentiment in ways no human being can.

The expanding role of big data in finance

In the near future computers have the potential to take over this sector. Big data allows more information to be input in a system that feeds on the knowledge of all the possible factors. Big data not only makes it possible to perform a larger number of tasks, but to do it in a more informed way, dramatically changing the way financial transactions are executed. As market tends to be efficient, making profits will become increasingly difficult and companies will have to implement these new technologies to keep up with the times.