Financial Data Science: 3 Comprehensive Aspects

This article will briefly introduce both Data Science and Financial Data Science to give you a better understanding of these similar terms. Moreover, it will explain the skills required to thrive in the field of Financial Data Science. Also, the article will discuss the popular techniques used in this field and the major areas of its application. Read along to learn more about the major aspects of Financial Data Science!

Introduction to Data Science

Data Science uses models and methods from Statistics, Mathematics, Computer Science, Information Technology, Graphic Design, Art, Organizational Theory, and Domain-specific knowledge to arrive at its deliverables. Here, Domain-specific knowledge means knowledge about the specific domain to which Data Science is being applied. It’s essentially an interdisciplinary field that is evolving into a new subject in itself.

Data Science has contributed immensely to the improvement of business processes by providing insightful information and predictions in a timely fashion. It has led to the adoption of new practices by old businesses, and also resulted in the creation of altogether new business opportunities. Data Science is being enriched continuously by technological innovations and Domain-specific improvisations, making it the field of the future. 

Also, as Big Data, its close counterpart, is having an immense impact on the world, Data Science has become imperative today. 

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Introduction to Financial Data Science

As the name suggests, its Data Science applied to the Financial Domain. The term “Financial” is related to, domains like Banking, Insurance, Transactional, Investment, Stock Markets, Government Budgets, and Personal Expenses. At the bare minimum, Financial Data Science means collecting and analyzing Financial Data and provide information that leads to better decisions.

Broadly speaking, Financial Data Science has the following goals:

• Improving the business processes of Financial Institutions and bringing them closer to their business objectives.
• Gain a competitive advantage and leverage it to maintain growth. 
• Reduce risks and minimize losses, for both the consumer and the business. 
• Understand and serve the clientele better, enhance customer experience.
• Predict and understand the changes in their environment and adjust to those changes better.
• Detect fraudulent practices and anomalies quickly, secure existing processes.
• Envision the need for a new product(s) and identify those approaching obsolescence.
• Enhance surveillance, improve social media interaction and social media image.

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Skills Required in Financial Data Science

Simply speaking, you need to have a keen interest in analyzing things, and an interest in fields like Mathematics, Logic, Statistics, etc. If you enjoy finding valuables in a haystack and could spend all your day crunching numbers or sifting data to gain information, then Financial Data Science is the field for you. Technically speaking, to get started as a Financial Data Scientist, you must be knowing skilled application of more than one of the following subjects:

• Data Analysis and Quantitative Methods
• Statistics, Econometrics
• Operations Research
• Database Population and Management
• Data Enhancement
• Data Security and Encryption
• Visualizations and Graphical Reporting

Also, you must know the best ways to collect data, prune the unnecessary data, enhance data quality, and fill in the gaps with appropriate assumptions or Empirical Data. 

A Financial Data Scientist is also expected to have domain knowledge of Finance, e.g. Financial processes like Loans, Deposits, Trades, Markets, etc., and Financial instruments like Bonds, Stocks, Cash, Cheques, Agreements, etc.

Techniques Used in Financial Data Science

Initially, as a Financial Data Scientist, you have to define the problem statement and expected outcomes and also gather data and cleanse it to make it usable. Afterward, you need to order it and apply Statistical and Mathematical methods to that data and test certain hypotheses. Lastly, you have to use Machine Learning and AI algorithms to devise a sustainable and intelligent solution.

Essentially a combination of the following techniques and their variants find frequent usage in Financial Data Science:

• Data matching: As the name suggests, it’s matching of data from 2 different Sets to find similarities or their differences, and use this information to identify patterns & marketing opportunities, detect frauds, or efficacy of security methods applied. 

• Regression analysis: It’s the process of estimating relationships between a Dependent Variable(outcome, effect, or result) and a group of other Independent Variables(covariates, predictors, or cause). It’s often used in Predictive Analysis, to support decisions and find a correlation(if any) between the Dependent and Independent Variables. Often, as a by-product, it can unravel new insights and help in optimizing some of the processes involved. 

• Gap analysis: It involves analyzing the gap between expectations and what really happened. Using numbers to know whether the targets were exceeded or how much away something is from its desired state.

• Matching algorithms: Compare previously known models/profiles of user behavior, to the current sequence of actions, aiming to detect anomalies or predict the change of taste. 

• Time series analysis: Analysing time-dependent data, to know more about seasonal demand upsurges or down surges, and how certain parameters change with time. It can also help in predicting products getting obsolete or needing changes to suit their time of usage. 

• Machine learning: Making the computerized system intelligent enough to learn from its mistakes, change its methods based on changes in incoming data, and/or devise new strategies based on environmental changes. 

• Patterns Analysis: Even subtle differences in a consumer’s purchases or credit activity can be automatically analyzed and flagged as potential fraud.

Applications of Financial Data Science

Financial Data Science because of its data-driven approach holds great importance in the following areas:

• Fraud Prevention
• Risk Management
• Stock Market Prediction
• Customer Analytics
• Credit Allocation

1) Fraud Prevention

You must have come across financial frauds and in this time of online transactions, financial frauds are happening with more frequency as fraudsters have gained another weapon in their arsenal. With the rise in Digital Banking and Online Transactions, fraudsters no longer need to meet you in person or go to any bank, they can just say at their home and execute their dubious methods. fraudsters, entice people to divulge their financial passwords or OTPs, make them transfer money through mobile wallets/online accounts, and transfer financial instruments in an unethical manner. 

Financial Data Science can not only mitigate such risks but prevent most of the frauds that happen or could happen. Through Financial Data Science, one can unravel risky behavioral patterns, identify sources from where fraudulent practices emanate and activate checks and measures at appropriate junctures through a financial transaction. For example, Banks store customer information like Income levels, Age, Place, Travel, and Buying patterns, etc. 

Next, using Pattern Analysis, Data Mining is done to detect suspicious chains of events. Machine Learning and some AI are used to make these systems more intelligent and smart. Any false positive is fed as a lesson in the system, as well as any mistakes are used to make the systems better. Any previously learned patterns combined with rules engines are used to identify problematic activity.

If a request lands on a Bank website via some e-mail or is forwarded by a suspicious website, trying to log in a customer who normally logs into his account by directly specifying the Bank’s URL, creates an alert! This alert triggers a sequence of further steps like checking if it’s coming from different geography, if the usage pattern is different or if any dubious parameters are observed. 

Now, if many such alerts are emanating from a particular geography (or a set of IP addresses ), then that geography can be labeled as troublesome and all further requests from that source will be thoroughly scrutinized strictly before allowing them. Moreover, since the data is in digital format, Banks share such information immediately, so other potential sources for the fraudster dry quickly.

2) Risk Management

Another application of Financial Data Science is in mitigating and managing the inherent risks in financial processes. This is a cross-disciplinary exercise, requiring knowledge of Statistics, Discrete Mathematics, Computing Methods, and Domain Knowledge. 

Some functions and processes that are included in risk management are Increasing Product Acceptance, Reducing Credit Risk and Defaults, Managing Market, and Environmental Risk. Incorporations that work in multiple countries, there is a need to address the risk of Foreign Exchange or Currency Rates Fluctuation. Companies can identify and quantify the factors that contribute to highs and lows in Currency Rates. Their inter-relationships can be defined and pruned. These factors are then continuously monitored and their combined effect on the Exchange Rates can be predicted to quite a large extent. 

Regression Analysis can be used in Financial Data Science to arrive at an optimal Portfolio allocation, balancing high-risk high return with low-risk low return investments. Also, forecasting can be used at regular intervals to assess the Portfolio and divert funds between asset categories to regain balance and profitability.

The above diagram depicts potential future exposure, after maturity prices of items in a Portfolio are simulated via Regression and Parametric Modeling. 

Environmental Risk is an encompassing term that includes risks that are inherent in the market that financial institutions work, for example, Volatility Risk, Foreign Exchange Risk, and Inflation Risk, etc. With this information on hand, decisions, practices, and policies can be directed towards maximizing profits and minimizing risks for the investors as well as the business. Proactive risk management in Financial Data Science also reduces the probability of a bunch of wrong decisions snowballing into a catastrophe.

 3) Stock Market Prediction

Financial Data Science is not only being used for stock market prediction but also commodities and futures markets, and securities/bonds. Essentially, it’s identifying the reasons for fluctuation in a share’s price, and then trying to predict how much these reasons will affect the price in the future and in which direction. 

Regression analysis comes in handy here again, as it does not differentiate between domains. It gives you the required numbers and you must interpret those as per your domain. 

Time series analysis coupled with weighted averages being assigned to factors that influence the market, where the weights can be changed according to environmental conditions can be used to predict the rise and fall of a share’s price. The factors that influence a share’s demand and pricing include the Financial ratios of a company, Market sentiment, Overall market and political conditions, Fundings and Exits, etc. 

Sentiment Analysis is another tool that can be used here. Based on the presence and the position of words in a piece of text or a pattern of actions performed, Sentiment Analysis can detect the sentiment or the thoughts which have gained precedence over other thoughts. This in turn can result in influencing a sell/buy decision for an individual, and a market upswing on the downswing as a collective whole. 

Predicting the outcomes of the “current sentiment” is a very popular tool in Automated Trading. By using Machine Learning and AI, Data Scientists can create a learning model that is smart enough to apply computing methods on its own based on external changes and predict price fluctuations based on its learnings from the past. Hence, it can help in deciding whether to hold, buy or sell a stock. Still, Financial Data Scientists are needed to create new learning models and improve the existing ones, as new information and technologies emerge.

4) Customer Analytics

Any financial institution or service has a diverse clientele with its consumers coming from varied financial and social backgrounds, diverse geographies and demographics, and have varied priorities. Additionally, they invest variable amounts, have diverse goals and their social conditions are susceptible to change. To satisfy, profitably engage and nurture them is an ongoing and sometimes uphill task. Financial Data Science is a very effective tool to achieve the same. Now, Customer Analytics can be of the following types:

• Network analysis: It can give some useful insights here, like how existing customers influence new ones to invest in a company or how the family members of an existing customer stand to benefit/lose based on management decisions. Feedbacks in the form of reviews are continuously collected from the customers and analyzed. 

• Text analysis and Sentiment Analysis: It helps in classifying reviews as positive/negative/neutral, and in knowing the sentiment behind what was written in the review. This information coupled with the user footprint or what the user went through, can give useful insights into the impact that product delivery is creating.

• Customer Segmentation and Predictive Analytics: Through these techniques, you can know the target customers for a particular range of products and fine-tune offerings for the maximum benefit of a customer segment. 

• Competitor Analysis: Knowing what and how are competitors offering their services. This can lead to insights like the reasons why people are being attracted to and investing in competitor offerings. Thereby providing opportunities for improvements and the ability to tap a new set of customers. Improving the Customer experience and removing any hurdles in the smooth availability of services help in increasing customer confidence and retention.

5) Credit Allocation

Credit Allocation is the process of dividing the financial resources of a bank/institution into different Processes, Borrowers, and Projects. For example, a bank may take deposits from its customers and invest that into different asset categories like Government Securities, Bonds, Mutual Funds, Borrowers, Infrastructure Projects, etc. Each of these investment sources or asset classes have their own returns, risks, and intrigues. Hence, Credit Allocation has to be a meticulously planned and frequently calibrated process. 

Financial Data Science can help in identifying the best asset classes, continuously monitor them, redistributing credit between them to ensure maximum profitability. First, it can be used to assess the current Creditworthiness of a client. Personal Creditworthiness depends on many factors like Income and Expenditures, Liabilities and Existing Loans, Financial Discipline and Repayments, Lifestyle, and Future Value of current assets. 

In the case of a business, some parameters used to judge Creditworthiness are Revenues and Taxation History, Dividends Paid, Assets and Liabilities, Write-offs, and Credit Declines, Maximum and Minimum Credit Balance months, Employee Satisfaction, etc. Data about all the above activities is collected and continuously monitored to arrive at the current credit score of a person/business. In many existing Financial Data Science practices like Regression, Time Series, and Gap Analysis, Risk Assessment is used. 

Machine Learning can help in identifying future defaulters and in assigning a probabilistic score to future profits/losses. The available investment avenues can then be graded based on Inherent Risk, Profitability, and Sustainability. Next, based on the exposure strategy of the participating investors, low but secured returns vs high returns coupled with more risk, decisions about credit allocation can be made. 

These insights and scores can guide the fund managers on how much to invest in a class, how long to stay invested in a company/asset category when to withdraw, and future avenues of a good investment.

Conclusion

This article provided a brief introduction to Data Science and its derivative field, Financial Data Science. Moreover, it explained the various skills that are required in this field. Also, the article discussed the numerous techniques that are used in Financial Data Science. The article also listed the various applications of Financial Data Science in the current business context.

Share your understanding of Financial Data Science in the comments section!