Before data science, data mining was a popular term used to describe the process of extracting patterns from data. In the early 2000s, statisticians took advantage of computing power to create a new field: data science. Data science is the insights found by analyzing data sets and the subsequent application of the insights. With computing power, statisticians gained a plethora of uses for their data sets. The interdisciplinary field extracts knowledge and insights from unstructured data and applies them to a large array of applications.
Applications in Finance
Financial data scientists apply data science techniques to financial issues. Data science is constantly transforming the world of finance.
Risk management is the mitigation of unpredictability in an investment. In the financial world, risk is any deviation from the expected outcome. Risk management occurs when a fund manager examines the potential losses of a particular investment and takes the proper steps to mitigate the risk. Risk management is a massive sector of the finance industry. Without the potential of risk, there is no potential for return. It comes in many forms: a simple example is when an investor chooses a low-yield and low-risk bond over a high-yield and high-risk bond. On the other hand, a more nuanced approach is when firms use derivatives to take an opposite position in an asset that they already hold.
Data science’s use in risk management is divided into 2 major categories: qualitative and quantitative risk management. Qualitative risk management, the rapid prioritization of risks based on subjective aspects, uses data science to calculate risk probability and helps to determine which risks must be managed. Assessing the risk of an investment is done with qualitative analysis. On the quantitative side, data science methods are used to quantify the risks of investments.
A large aspect of many financial corporations is trading shares in the stock market. Traders first started using algorithms in the 70s to gain an edge on their competition. So, what has changed in the last half-century? In short, the algorithms have insurmountably improved and evolved. The trading world has forever been transformed by the arrival of data science. Trades occur much faster and efficiently (high-frequency trading) due to the real-time insights provided by data science based on the complex stores of data: market patterns/history, stock information, etc. With the introduction of machine learning, algorithms for trading are ever-changing; the sky is really the limit for algorithmic trading.
Fraud protection is an extremely knowledge-oriented field and quantitative analysis methods can easily be applied. Such methods can be applied through artificial intelligence:
- Data mining is used to cluster and segment data to automatically find associations in the data and signify when relevant patterns are detected.
- Pattern recognition is used to search for approximate patterns of suspicious actions and to connect given inputs.
- Neural networks are used to individually create forecasting that can be compared against internal audits or financial documents.
These methods are artificially intelligent, meaning the machines are given very little oversight. This allows the computers to prevent fraud at much greater speeds and accuracy when compared to traditional fraud prevention methods.
Machine learning is also a large aspect of fraud prevention. Earlier, human analysts would create data analysis techniques. But now, machines learn from past data and create their own data analysis to search for fraud and subsequently provide insights on how to prevent it best.
Future of Data Science and Finance
As data science evolves, so does the world of finance. There is no way around it: computers are smarter, faster, and more efficient than humans. Computers have been taking over the finance field for the better part of a century. So, it is a race to see whose data science methods are most effective in analyzing and executing tasks on behalf of their respective corporations.