Information Processing with FINTECH

Hello everyone. Welcome all of you to the latest article in the series of FINTECH. We have already discussed about how FINTECH has changed traditional way of lending and borrowing.

In this article I’m going to explain how data processing in finance sector has changed with the technology.

Data is Money

In the current context data is extremely important. More data you have, more information can be processed. More data you have, more accurate the information you process. More accurate information you have, more money can be made.

Impact of Data Processing in Finance Sector

I’m sure most of you are familiar about equity markets or in other words share market (Eg: Colombo Stock Exchange, New York Stock Exchange). Investors, hedge funds, trusts etc. who are investing in these markets are predicting the future price movement of listed companies’ shares. So how do they do that? They analyze company details, macro economic factors, micro economic factors and any other factors which can affect the price movement of shares. Sooner you predict the price movement accurately, higher the amount of profit you make.

This mechanism of forecasting is not only important in share trading but also in business acquisitions, mergers, valuations and also for any other corporate actions such as bonus issues, rights issues, further issue of shares etc.

Data Processing

You must have learned about data processing when you learn the basic computer science lessons. It works like follows.

  1. Data collection – gather data from variety of sources such as surveys, social media, newsfeeds, websites, mobile apps etc.
  2. Preparation – Screening and cleaning data for errors before processing.
  3. Input – Cleaned data is converted in to a language which can be understood and read by machines.
  4. Processing – Process the data by a machine to get a predetermined output. (Eg. CPU). However in digital age, a range of co-processors such as Artificial Intelligence, machine learning and cloud based software applications are available to do the processing.
  5. Output – Processed data to be translated in to a readable language to the users.
  6. Storage – Store the processed data for future use.
Use of Digital Age Data Processing in Finance Sector.

Artificial Intelligence (AI) and Machine Learning, use computer algorithms to process massive amount of historical information of publicly traded stocks, to predict trends in the market. Financial Statements, Announcements and statements issued by the public companies are also considered as sources of insight in to the financial health of those companies. In addition to that, web based data which again is a massive amount of information also taken in to consideration. All these information will be processed using AI and Machine learning to help investors make better informed decisions and make better returns. It explores the methods and tools that can be used to create new trading and investment strategies.

Testing trading and investment strategies is another crucial process in capital markets. AI is being used to test these strategies by implementing them in historical data to measure the level of accuracy.

AI also being used by banks and financial institutions to track fraudulent or suspicious transactions by analyzing the trends and patterns.

Advantages of Using AI and Machine Learning.
  1. Can process massive amount of data in a considerably shorter time period which is beyond human capacity.
  2. High accuracy of the output information
  3. Can detect fraud in card based transactions
  4. Can provide robo advisory services in wealth management and investing
  5. Can communicate with humans effectively. (Eg: Chatbot)
  6. Ability to learn and get better at the given task
Disadvantages and Limitations.
  1. High cost
  2. Data processing can get slow when the amount of data increases
  3. Data storage is expensive.
  4. Vulnerable for data security threats.
  5. Can affect many jobs and lead to unemployment issues
  6. Lose of competitive advantage for the investors when used by majority.

These technologies are being used successfully in some part of the world while most of the applications are still in experimental stage. However the use of technology will continuously change the data processing in finance sector by progressively improving the data driven predictions or decisions without static program instruction from a human.

Below are few companies which are currently in this business segment. Please visit their websites to have a better idea about how they operate.

The rise of FINTECH solutions and the massive increase of data and the technological solutions to process it have evolved simultaneously over the past decade. The potential advantages offered by useful and predictive data analysis are highly sought after in the financial services industry. However in the competitive investment industry, the information that can be delivered these advantages is closely guarded which is a challenge for FINTECH companies to monetize these services.

Hope you got a better idea about how data processing is improving and changing in finance sector with FINTECH solutions. In the next article I will explain about Artificial Intelligence/Machine Learning and how it works. Stay Tuned. Stay Safe.

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