Popular Applications of Data Science in Finance.
Data is omnipresent and when we talk about finance, data is there. Raw data, structured data- every type of data. Interestingly, before the world came up with the term ‘data science’- finance was already using it.
Data is an essential tool in today’s industries and putting science in it is to interpret and analyze. It is to make important decisions for your business, to tell you whether taking a financial risk would be optimal at a particular stage or not. Financial data science is putting the applications of data science techniques into the field of finance.
What is Financial Data Science?
This field uses statistical methods to gauge the problems of finance. It works on the fulcrum of two disciplines put together- econometrics and data science. Machine Learning, Prescriptive and predictive analytics are the prominent tools through which understanding is tabled on financial data and other related problems.
Let’s discuss these applications in detail.
Algorithmic Trading
In this, complex mathematical formulas and high-speed computations are used to make new trading strategies. The trading is done by a computer program that makes a trade happen through already given instructions. Theoretically, the trade generates profit because of its impossible speed and frequency that is unable to be achieved by a human trader.
It rules out the impact of human emotions in the trading practices as these instructions work upon various factors like timing, mathematical model, price, or quantity.
Some worth noting benefits of Algo-trading-
Reduces human error. For example, the risk of manual error while placing the bet and eliminating the mistakes based on emotional responses.
Execution in real-time, placed at the best possible and accurate price.
Multiple automated checks on market conditions simultaneously.
Credit Allocation
Credit Allocation is the process of streamlining financial resources collected from different processes, borrowers, projects to different categories like Government Securities, Bonds, Mutual Funds, Infrastructure Projects. Since each has its intrigues when it comes to returns and risks- credit allocating has to be judiciously planned.
Financial Data Science helps in assessing and identifying the best asset classes to allocate these resources to maximize its profitability. This is possible through a digital footprint- it is the trail of information left behind as a person accesses or registers a website. It is stacked with a large dataset and machine learning algorithms that are supported by big data and high computational power can understand and make analyses of this data. For example- the factors on its customer behavior and insight whether the person/organization can predict how likely they can pay back the credit.
Customer Analytics
There is no one type of clientele that is received by the bank. There is a social-economic diversity with ethnic and demographic differences. Even the priorities are different, Hence, to make sure that the bank is getting benefits and the customers are also profiting in dealing with them, customer analytics comes as a handy tool in this discipline.
Network Analysis
Understanding how the existing members influence the new ones to invest or how their members are getting affected- are they losing or benefitting from it. Feedbacks in the forms are analyzed and worked on.
Sentimental Analysis
This is interesting as it seeks to understand the sentiments behind the review given and understand it along with the information given and then apply the useful insight into creating the desirable product.
Customer Segmentation and Predictive Analytics
Through these techniques, you can know the target customers for a certain product and understand the maximum profit you can make of a customer segment.
Risk Management
One of the most important applications of Financial Data Science is in reducing and managing the inherent risks in financial processes. This needs a variety of knowledge of fields like Statistics, Computing Methods, Domain Knowledge, Discrete Mathematics.
The 2008 financial crisis was a cautionary tale on the weakness of traditional risk management. Here, data science helps in increasing financial regulation and limits risk-taking. It is to estimate and manage the risk across multi-platforms and organizations using big-data analytics and machine learning. Credit and market risk exposures are assessed in real-time, saving you from hell-time risks.
Fraud Prevention
There is an increase in fraud activities with the rise in digital banking and online transactions. However, with the growth in big data and analytical tools, financial tools can help keep a track of frauds. Credit card fraud is one of the most prominent and is improved because the algorithms can catch anomaly activity easily. Furthermore, an alert is passed to block the account to minimize the losses.
Examples of Data Science in Finance
Through the application of data science to finance, it helps provide insights into financial data in various forms by building systems and various systems. This has led to improvement in risk analysis and anomaly detection making the financial transactions sans money laundering activities. Let’s try to understand some examples of how financial services use this discipline-
In customer service-
Forward-looking banks and fintech provide efficient services by using data that tells them about their customers’ transactional and behavioral data, using various data science algorithms. The world’s most prominent banks are already applying these strategies to previous customer purchases, engagements, and accounts that would benefit them.
It also provides insights into how well a product sells or who will purchases that product and end up providing you a good proposal of your service/ product. Hence, through these interpretations- the financial services can likely make policies that could develop products and services that have more outreach. They also take the help of external data to analyze their customers’ behavior during a recession or when the housing market is stagnant to make decisions regarding the interest rates and whatnot.
Wrapping Up
As you can see, Data Science has become an important element in the Financial Industry used to terminate risk and provide analyses. Decision-making is improved and profits are reaped through it. Intelligent tools are also used for the same.
