“Data is the new oil of the digital economy”  – WIRED

Data science has played a crucial role in modern businesses helping them improve their services and profits – and this is especially true in the finance sector.

JP Morgan, one of the biggest banks in the world, plans to invest at least $11.5 billion a year in new technologies related to data science – such as automating risk analytics.

There is no specific definition of data science or an established curriculum. However, a useful working definition is – Data science involves extracting, cleaning, organising, combining, analysing, and presenting data to develop and deliver strategic solutions to crucial problems. While data may be a new oil, a ton of data is sitting idly in the database – hence by employing data science knowledge this helps make sense of this data to give organisations and societies worldwide better information to help them make better decisions.

According to the Institute of Management Accountants, more accounting professionals are now implementing big data analytics in their business processes. This will continue in the future with many shifts to advanced technology. A recent report from the World Economic Forum predicts that 463 exabytes of data will be generated daily by 2025. That’s equal to 212 million DVDs daily, with incredible actionable insights.


Data science in the finance industry is about applying advanced statistics and predictive analysis techniques to financial data sets and solving common challenges faced by users. With the employment of data science, the finance sector can understand the vast amount of data sitting in their system and take calculated risks to make profits. Working in the field of Data science requires not only technical skills but also domain knowledge and critical analysis skills to solve complex problems in the business. According to a Chartered Financial Analyst (CFA), data science within finance encompasses a wide range of opportunities for investment careers. 


Machine learning is defined as a subset of data science that uses statistical models to draw insights and make predictions. It is making significant inroads in the financial services industry, from developing programs to supervising and generating outcomes without active involvement from the workforce by using data only. The magic of machine learning lies in the model being able to learn new things without being instructed by the user to. 

Simply put, the data scientist selects the model and feeds it with data. The model then automatically adjusts its parameters to improve outcomes. Data scientists train machine learning models by applying well-trained calculation/models to data related to the industry or projects and then transform real-life problems into solutions. The more data you feed, the more accurate the results are.

The Application of Machine Learning in Finance

Despite the challenges, many financial institutions have already taken advantage of this technology. 

The figure below shows that financial services take data science very seriously, and they do it for a couple of good reasons:

By using machine learning and data science in financial services, business will be able to :

  1. Reduce operational costs from process automation.
  2. Increase revenues thanks to better productivity and enhanced user experiences.
  3. Enjoy better compliance and reinforced security.

Machine learning can enhance many aspects of the financial ecosystem thanks to the quantitative nature of the financial domain and large volumes of historical data. That is why so many financial companies are investing heavily in machine learning research & development.

Examples of Successful Technological Advancement in Business

Technology can replace manual labour and boost production. Machine learning enables businesses to save on expenses, enhance client experiences, and expand services. Examples of how machine learning is being used in finance automation include:

  • Chatbots
  • Call-center automation
  • Paperwork automation

Below are some examples of process automation in banking:

  1. JPMorgan Chase has introduced a Contract Intelligence (COiN) platform that uses Natural Language Processing, one of the many machine learning methods. The solution processes and extracts vital information from legal papers. Typically, manual evaluation of 12,000 yearly commercial loan agreements would require around 360 000 person-hours. In contrast, machine learning permits evaluating the same amount of contracts in hours.
  2. Wells Fargo utilises a Facebook Messenger chatbot powered by artificial intelligence to engage with people and assist them with their passwords and accounts.

Applications of Data Science in the World of Finance

Fraud Detection 

The banking sector has to deal with fraudulent transactions frequently. With increased volume as financial services become more accessible and digitlised, the occurrence of fraud increases.

As big data and analytical tools have grown, financial institutions are now able to better identify fraudulent transactions. Credit card fraud is one of the most common types of fraud faced by banks.

This kind of fraud can be found because algorithms have improved, making it easier to find strange spending patterns. Also, these detections let companies know about strange financial purchases, which makes them block the account to keep losses as low as possible.

Different machine learning tools can also find irregular patterns in trading data and let financial institutions know they need to look into it further.  Using several different clustering algorithms, companies can separate and group data patterns that look odd and investigate it further. Banks can also utilise this tool to deal with other types of fraud such as insurance fraud.

Algorithmic Trading

In algorithmic trading, machine learning aids in executing superior trading decisions. A mathematical algorithm analyses the news and trade outcomes in real time and identifies patterns that can drive up or down the stock prices. The system can then proactively sell, hold, or purchase stocks based on its projections.

Machine learning algorithms can simultaneously examine thousands of data sources, a feat impossible for human traders.

Algorithms for machine learning assist human traders in achieving a marginal market advantage. However, given the vast volumes of trading operations, that small advantage often translates into significant profits.

One company that implements this in their trading business is Citadel Securities, which is based in the United States of America and owned by Ken Griffin. It is one of America’s largest market makers. With regards to equities – the firm accounts for more than 25% of all U.S. trades, 40% of retail trades and more than 30% of stock options volume. 

Ken Griffin works with knowledgeable mathematicians and scientists using cutting-edge tools like artificial intelligence, machine learning, and predictive analytics to evaluate vast amounts of data rapidly. He also hired Peng Zhao, a 40-year-old data whiz from Beijing. Zhao, a Berkeley graduate with a degree in statistics, is well-suited to guide Citadel’s foray into trading digital assets. As a result, Citadel Securities is expanding into new markets and growing more rapidly than Griffin’s hedge fund.

Another prominent individual is Jim Simons, who might be better known as the “Quant King”, who studied mathematics at the Massachusetts Institute of Technology. He established his own company called Renaissance Technologies, which uses 100% quantitative strategies to profit from market opportunities. The secret to Jim Simons’ trading tactics is his massive data collection and analysis, which he uses to identify statistical patterns and non-random events across various markets. Jim Simons has also assembled a dedicated covert staff that produces testing strategies. Unfortunately for us outsiders, few of Medallion’s strategies are implemented outside their offices because the employees have a stake in the outcome. However, it accounts for the majority of the yearly gain. The Medallion Fund may be the most successful fund ever, having the best performance among Renaissance Technologies’ funds. From 1988 to 2018, it has generated more than $100 billion in profits for its owners. In addition, Jim Simons’ trading approach uses scientific methods to combat cognitive and emotional biases. They make hypotheses, test them, and either apply them or revise them to attain the required result.


Given the career options available to youths in the job market, it may be worthwhile to differentiate between the careers available so there’s better awareness.



Key Roles
  • Determining what data to analyse in order to answer specific questions
  • Identifying trends in data
  • Responding to questions by explaining numbers and types of data
  • Preparing graphs, diagrams and other reporting methods
  • Providing financial advice to business stakeholders
  • Projecting financial performance based on historical data
  • Reviewing financial statements to determine the economic value of a company
  • Preparing financial reports, graphs and presentations
Duties Data analysts concentrate on various areas of a business Financial analysts concentrate on the financial performance of a company.


Thanks for transparency check out Malaysia PayGap to gain more insights:

Based on the data according to MalaysiaPayGap, the salary for entry level data scientist is RM3,500 – RM4,000 and can go above RM10,000 for senior level. Based on the data according to MalaysiaPayGap, the salary for a financial analyst is RM2,100 to RM2,500 and increases to RM 4,800 for senior level with 3 years of experience. 


In a nutshell, data science has contributed significantly to businesses, and this is no different in the finance industry. With continued innovations – this hopefully can create a safer environment for users and well as enhance existing processes. One would only expect the number of data scientist to enter the finance industry to grow, given the high demand and lucrative pay it attracts.


Researchers: Hariz Izzudin Bin Mohamad Shaharizad

Reviewers: Bahari, Marcus Wee

Editor: Marcus Wee

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