Data is the new oil. Now more than ever, it powers businesses and drives their success in countless ways, and the companies that are utilizing data science have an immediate advantage over their competition with the ability to create better products and understand trends before they happen to stay ahead of their rivals.
But what does this mean for data scientists? In this article, we’ll explore a few applications.
Financial institutions were some of the pioneers when it comes to the use of analyzing huge amounts of raw data to draw meaningful insights for strategic decision making. Even before data science was a recognized industry term, financial institutions understood the potential of finding patterns and anomalies in large amounts of data to steer organizations away from financial catastrophes toward financial opportunities.
Whether it’s fraud detection, risk monitoring, automated trading, or helping banks navigate a recession, data scientists play an extremely valuable role in the finance industry.
Applications of Data Science in Finance
1. Risk Analytics
Risk analytics is an important tool for data scientists and business intelligence professionals in finance. It helps companies make strategic decisions about risk, increase their trustworthiness on the market, as well as secure exposure to both themselves and other businesses that are also risking assets under them. In other words — you’re able to generate a representation of what could happen in case something goes wrong, so that no one bites off more than they can chew!
2. Consumer Analytics
Financial institutions are using consumer analytics to measure the customer lifetime value, increase cross-sales, and reduce below zero customers.
One way in which they do this is by taking insights from real time data about people’s behaviors so that appropriate business decisions can be made for them when it comes down their specific needs or wants; like insurance companies who use these types of metrics with regards to measuring risk pools due up on certain products through observed patterns over time (like how many months someone has been paying).
3. Fraud Detection
Fraud is understandably a major concern for financial institutions. The dangers of fraud increase in correlation with the number of overall transactions, but with the growth in analytical tools, it’s much more manageable for institutions to keep track of fraudulent activity within staggering amounts of data.
One of the most widely practiced fraud detection practices lies within credit card fraud, where algorithms trained to spot anomalies ‘hold the line’ against potentially malicious activity.
4. Algorithmic Trading (wink)
Algorithmic trading is one of, if not the most important part of financial institutions, and has been growing at a faster rate than internet adoption between 1990–2000. Complex mathematical formulas and instantaneous computations help institutions devise new trading strategies and capitalize on otherwise unseen opportunities within financial markets.
Big data has played a crucial role in the accuracy and scalability of algorithmic trading, as an average dataset present in a traditional automated trading system contains massive amounts of data streams and parameters that determine actions based on preset conditions, all while an underlying portfolio management model determines position sizing and optimal allocation strategy.
While there are countless roles where data science and the financial industry go hand-in-hand, the most dominant applications lie mostly in the fields of risk management, trend analysis, fraud detection, and automation.
In the coming years, it will be exciting to see how machine learning and data science continue to optimize not just the financial industry, but also just about every other industry you can think of.
What are some future applications you’re looking forward to?