Our ability to gather, compile, and mine for data is greater than it ever was and continues to grow by the minute. Big data analytics is affecting nearly every industry, but there are few sectors where it’s having as profound an impact than on the world of finance.

We’re already seeing robo advisors using improved data sets, artificial intelligence, and machine learning to outperform human advisors, and more traders moving towards algorithmic trading to maximize portfolio returns and apply complex mathematical calculations to historical data. Let’s take a closer look at the impact big data has and could have on the future of financial services.

The Rise of the Robots

Robo advisors used to be seen as an oddity and mainly used by Fintech startups. But more reputable financial institutions such as Schwabb, Fidelity and Vanguard all have embraced the technology.

Robo advisors have many advantages over their human counterpart, one of the biggest one being costs. Institutions can now offer services to clients with less capital, as adding them does not take time away from actual advisors. These costs can then be passed down to clients who don’t have to spend as much on financial advice.

The Move to Quantitative Trading

Manual trading strategies are gradually getting pushed to the side by quantitative analysis. Quantitative models and computer programs are used to crunch large amounts of data at unprecedented speed, which allows them to use multiple trends and patterns, and often allows them to project outcomes much more accurately.

Groups like the China Fund for instance use quantitative research to allow foreign investors to get a clearer picture of the Chinese market and make better decisions. China’s bull market offer great opportunities, but without having feet on the ground and understanding of China’s regulatory landscape and trends, can be a risky one. Qualitative and quantitative research allows investors who might be wary to invest in China to understand the subtle nuances about the market. And while quantitative trading used to be limited to big financial institutions, smaller investors and Forex investors are slowly coming in.

Risk Minimization

Another way that big data is revolutionizing the world of finance as we know it is that it can be used to mitigate the risks associated with human error when it comes to online trading. Financial analysis has also evolved to integrate factors that have an influence on commodity, political and social pricing trends.

Financial analytics are also influenced by machine learning. Machine learning allows programs to learn the mistakes that have been made in the past and use the data to continually fine tune strategies and eventually make more profitable trading decisions.

Machine learning is also often used in conjunction with algorithmic trading, which is a method that uses a set of predetermined mathematical rules to make rapid and precise financial decisions. One of the reasons why algorithmic trading is so efficient is that it completely takes the emotional component out of decisions and reduces the risk of human error as well.

Financial and Sentiment Analysis are Merging

But one of the most promising applications of big data analytics is when it comes to evaluating market sentiment. Sentiment analysis, also commonly referred to as opinion mining, is a form of data mining that focuses solely on categorizing and identifying market sentiment. Advanced data mining tools now allow analysts to get a quick snapshot of the market’s opinion of a certain commodity, economy, sector, or currency. Predictive models and opinion mining can now be used to complement traditional financial analysis, and help make better trading decisions.

Conclusion

The disruptive force of big data analytics should not be underestimated, and those who still aren’t taking advantage of it are bound to be left behind. Big data will become an indispensable tool for any financial institution, and one that could completely turn the current model on its head.