FINTECH: WHEN TECH MEETS FINANCE

FINTECH: WHEN TECH MEETS FINANCE

The word ‘technology’ is so widely used today that we tend to forget the times when this word didn’t really exist. Every individual has their own perception, their own definition of technology. Sometimes it has been described as ‘the idea of developing tools in order to make our lives easier’ whereas people like Bernard Stiegler have described it as ‘the pursuit of life by means other than life,’ and as ‘organised inorganic matter’ in Technics and Time, 1.

On the other hand, Finance is a very broad term that describes the idea of management of large amounts of money through activities associated with banking, debit, credit, money and investments.

With the continuous advancements in the field of science and technology, there has been the involvement of technology in almost every branch of knowledge that exists in this world be it security, food, currency, architecture, medicine, art, astronomy and so on. Simultaneously, technology has also proved to be fortuitous in the field of finance and that too on a very high scale. Thus, the branch which deals with the symbiosis between finance and technology and consists of software, applications and other digital services that are used by the consumers for activities like mobile banking, investments and loans, is referred to as ‘FinTech,’ a portmanteau of Financial Technology. 

‘Tech’ in ‘Fin’- Applications

Customer Service is one of the major sectors that has exploited Financial Technology in the last few years. A decade ago, an efficient customer service team was essential in order to carry out the financial tasks involved within a company. But, with the advancements in FinTech, AI chatbots have made consumers’ life way too easier and act as an avenue for customers to interact with. The chance of error is significantly reduced and the workload on humans is also minimised. Earlier we needed a separate team to carry out the bank work but now with the evolution of FinTech, we don’t even need to go to the bank personally to open a bank account, transfer cash or update the details. Everything can be done conveniently by using banking apps and other such software on smartphones.

In finance, computer programming has been proved to be extremely useful in a wide range of situations which include setting up and managing electronic trading systems, pricing derivatives, risk management, trade management platforms and so on. Python, in particular, is important for the latter three. Python can also help in creating analytical tools and models and can even modify Excel Spreadsheets to provide greater efficiency. Another advantage of using this language is that it provides a large array of libraries that can be effectively used in finance to build financial models and perform other mathematical operations. 

Some useful Python libraries and packages include –

  • numpy: This package is used for performing scientific and computational tasks on python. Besides that, it is also used in numerical programming, finance, industry, academia and so on. With its roots in all these fields, this library specializes in basic array operations.
  • pyfin: If the user wants to perform basic options pricing in python, they can simply use the pyfin library.
  • ffn: This is a financial function library for python. It is basically used to quickly carry out analysis of trading strategies and financial asset price series and contains many useful functions for those who work in quantitative finance.
  • QuantPy: This is a framework created primarily for quantitative finance in python. It has a Portfolio Class that can import daily returns from search engines like Yahoo.
  • zipline: This is a Python based algorithmic trading library. Besides being an event-driven system, it also reinforces backtesting and live trading.
  • TA-Lib: This is a software that is widely used by software developers involved in trading and required to perform the analysis of financial market data technically. Another advantage is that it has an open-source Application Program Interface (API) for Python.
  • trade: This Python framework is used for the development of financial applications. Once the user informs the items he has in stock and a series of subsequent occurrences with those or other items, trade calculates the effects of those purchases, sales and so on and by and by gives back the new amounts and cost of items in stock.
  • QuantSoftware Toolkit: This is an open-source software framework on Python, designed to support portfolio construction and management. This QS Toolkit is primarily for finance students, computing students as well as quantitative analysts with programming experience.
  • finmarketpy- This is a python-based library that enables us to analyse market data and simultaneously backtest trading strategies using a convenient Application Program Interface, which already has the templates to define backtest.
  • pyfolio:  This is a Python library used primarily for risk analysis and performance of financial portfolios. It works efficiently with the Zipline open-source backtesting library.
  • finance: This is also mainly used for Financial Risk Calculations. It has been so optimized such that it is easy to use because of features like Class Construction and Operator Overload.
  • qfrm: It is abbreviated form of Quantitative Financial Risk Management. It constitutes amazing Object Oriented Programming tools for measuring, managing and visualizing risk of financial instruments and portfolios.
  • visualize-wealth: If the user wants to backtest, construct, analyse or evaluate portfolios and their benchmarks then he can easily use this library in Python to perform all the tasks mentioned above.
  • empirical: This is used by both zipline and pyfolio and is used for common financial risk analysis and also in performance metrics.
  • statsmodels: If the users want to explore data, estimate statistical models or perform other statistical tests then they can use this Python module.
  • ARCH: This is used to perform financial econometrics in Python.

Java is another popular language that is used in the banking industry. This is primarily due to its secure and stable design. Java is a platform-independent and portable language and thus it can easily run on the newer versions as well even when a change was made. It is necessary to install a security system since banks handle a lot of sensitive and confidential information. Java acts as a stimulus and helps banks perform all the tasks while maintaining an appropriate level of confidentiality.

Data Visualization in FinTech

Most of the people in this world are visual learners i.e., they prefer to visualize a certain concept to understand it more thoroughly. Charts and graphs allow the users to understand the growth or depreciation of a certain thing by comparing it with the previously stored data. Histograms are the best means to represent such data statistically. A manager can take large quantities of data, can see the bigger picture more clearly and can provide a concise report.

Data visualization is the creation of visual representations of data that clearly communicate insights through charts and graphs. These charts and graphs help leaders and decision-makers make better, data-based decisions more quickly than the traditional data table. And it keeps them from getting lost in a Where’s Waldo-style puzzle maze of stats and data points. (Sisence, 2020)

Data visualization allows us for quick interpretation of results. We can easily create a chart of the data and see the trends of that particular data over a period of time or over any other variable. For example-

Open Profit Margin

This visual data representation is split into a percentage gauge in addition to a detailed bar chart and will help you to accurately calculate your Earnings Before Interest and Tax (EBIT).

Data Visualization can be easily done using the Financial Toolbox in MATLAB. It provides numerous easy-to-digest functions for mathematical modelling and statistical analysis of financial data. We can analyse, backtest and optimise investment portfolios taking into account the turnover, transaction costs, semi-continuous constraints and minimum or maximum number of assets. This toolbox also enables us to estimate risk, analyse yield curves, price fixed-income instruments and European options, and measure investment performance. (MathWorks, 2021)

Stochastic Differential Equation(SDE) tools let us model and stimulate a variety of stochastic processes. Time series analysis functions can also help us perform various productive tasks. They let us perform transformations and regressions with missing data and convert between different trading calendars and day-count conventions. (MathWorks, 2021)

There are various other libraries in our hand which are used for data visualization in finance-

Matpolib Plots
Some Matplotlib plots | Adapted from packtpub.com

Matplotlib- This has established itself as the benchmark for data visualization and is a robust, reliable and efficient tool modelled after MATLAB’s plotting capabilities. It can be used to create static image files of almost any plot type. (Foy,2021)

Some Seaborn Plots | Adapted from medium.com

Seaborn- This is another common data visualization library that is based on Matplotlib and provides a high-level interface for drawing attractive and informative statistical graphs. (Foy,2021)

Data Visualization with Plotly & Dash | Adapted from blog.mgaudin.fr

Plotly & Dash- Since all the previous graphs were static and could not be altered and interacted with in any way. Plotly library solves this problem using which python creates interactive plots as .html files. Users can zoom in, select, hover and perform several such tasks with these plots but in order to regenerate a plot to see updates you need to re-run the .py script. Dash helps to resolve this problem. Instead of creating a .html file, Dash produces a dashboard web application at your localhost which you can then conveniently visit and interact with. (Foy,2021)

Some other prominent libraries include Pandas and Time Series Visualization which are extremely useful as well.

Business Intelligence (BI)

Business Intelligence has been defined as “a set of methodologies, processes, architectures, and technologies that transform raw data into meaningful and useful information used to enable more effective strategic, tactical, and operational insights and decision-making.” (Forrester, 2021) Thus, Forrester refers to data preparation and data usage as two separate but closely linked segments of the business-intelligence architectural stack.

Business intelligence can help companies make better and appropriate decisions by showing them the present data as well as the historical data within their desired context. BI can be used by analysts to provide performance and competitor benchmarks to make the organization run smoother and more efficiently. Market trends can also be easily spotted to increase sales or revenue.

The best way to present BI is through Data Visualization.

Listed below are few ways in which business intelligence(BI) can help companies make smarter, data-driven decisions:

  • Identify ways to increase profit
  • Analyse customer behaviour
  • Compare data with competitors
  • Track performance
  • Optimize operations
  • Predict success
  • Spot market trends
  • Discover issues or problems

Conclusion

BI is rapidly evolving according to the needs of the business foundations and technologies. Companies are striving to be more data-driven and efforts to share and collaborate data are continuously increasing. In the future, we can definitely say that data visualization will be even more essential to work together across teams and departments.

And with the continuous advancements in technology and science, we can definitely hope to see a new phase, a new era of modernization where FinTech would rule the world of finance.

References

Evelson, B., & Nicolson, N.(2008). Topic overview: Business intelligence.  https://www.forrester.com/report/Topic+Overview+Business+Intelligence/-/E-RES39218#

Foy, P. (2021). Python for Finance: Data Visualization. https://www.mlq.ai/python-for-finance-data-visualization/#:~:text=%20Python%20for%20Finance%3A%20Data%20Visualization%20%201,Python%20data%20visualization%20library%20based%20on…%20More%20

MathWorks. (2021). Financial Toolboxhttps://in.mathworks.com/products/finance.html

Finance Train. (2019). Best Python Libraries/Packages for Finance and Financial Data Scientistshttps://financetrain.com/best-python-librariespackages-finance-financial-data-scientists/

8020 Consulting. (2020). The Growing Power of Data Visualization in Financehttps://8020consulting.com/data-visualization-in-finance/

CFI. (n.d.). Programminghttps://corporatefinanceinstitute.com/resources/knowledge/other/programming/

Rayyan Ahmed
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