Data Science in Finance

Atif M.
5 min readAug 31, 2021

Every year, there’s an increase in the number of transactions in the finance market.

With more transactions, we get more data, and, with plenty of new Internet of Things (IoT) devices now in circulation, the types of financial data in action have become more varied.

Besides the famous business intelligence tools, companies now need to log and analyze unstructured data, in the form of reviews and social media comments, and also traditional forms of structured data. Therefore, it is becoming increasingly difficult to find a way to make sense of the relationship between the data.

The problem became so challenging that a distinct profession for handling several data sources has taken shape, called Data Engineering that transforms data so that data science and machine learning can utilize it.

Data science is the ever-evolving method of extracting meaning from large-scale and complex data. It allows your company to focus straight in on those insights that are useful to your business’s financial attributes, letting you forecast the future accurately and minimize the risk factor when making financial decisions.

Find out how all of this can benefit your finance business with these data science application examples:

Let’s explore how that we’ve discussed can benefit your finance business with the data science application as follows:

How is Data Science Used in Finance?

Risk Analytics

Risk is an inevitable aspect of business, especially in the financial sector.

Setting up the risk factor before making any decision is important. Risk analytics, computed through data science, is the best way of safeguarding the business against potential security threats. The more a business’s risk-related data belongs to the ‘unstructured’ category, the more challenging it is to analyze it without data science technologies and the more it is susceptible to human error.

Data collected over time can give the best insight into where losses were incurred and why. The importance of the loss and how frequently it is incurred can help in highlighting the particular areas that pose the most risk, so these situations can be prevented in the future. Once a threat is…

--

--

Atif M.

CEO @ Inqline — inqline.com l AI Hub events l Hosting 30-hours data science hackathons l Philanthropist l World Traveler l Athlete