Homebreadcumb forward arrow iconBlogbreadcumb forward arrow iconData Sciencebreadcumb forward arrow iconTop 7 Data Science Use Cases in Finance Industry [2023]

Top 7 Data Science Use Cases in Finance Industry [2023]

Last updated:
30th Aug, 2022
Read Time
11 Mins
share image icon
In this article
Chevron in toc
View All
Top 7 Data Science Use Cases in Finance Industry [2023]

Finance is one of the most critical sectors in the world. Finance management used to require a lot of effort and time, but not any more. Using Data Science, now one can quickly analyze finance and make a better decision to manage finance. The use of data science in financial markets has helped the industry in many ways.

Data science works as the backbone of the firm. Without proper data science tools, a firm could not perform effectively. The significance of data science for finance industry has grown manifold in recent years.

Now Data Science is being used in the Finance Industry for the same reason. Data Science is a field that is used for many finance areas such as algorithmic trading, fraud detection, customer management, risk analytics and many more.

It is through the use of data science for finance that firms are able to have a better bonding with the consumers by knowing their choices, which in turn leads to an increase in their sales, thus increasing their profit margin. It also helps to identify risks and fraud and protect the firm. Thus, a data scientist is the most important asset to a firm without which an organization cannot simply perform.

Read more about Data Science applications.

Data Science Applications in Finance Industry

There are many applications of data science in the field of finance. Let’s discuss these below.

1. Risk Analytics

Every company has some risk while doing business, and it has become essential to analyze the risk before taking any decision. Risk management is the method by which the risks involved while doing business can be identified, assessed, and measures must be taken to control the risk.

It is through proper risk management only that the profits of the business can be increased in the long term. Thus, it is very important to analyze the risks that a firm is facing properly. The use of data science in financial markets has made the method of risk management easier. Analyzing the threat has become crucial for large companies for their strategic decision making and is called Risk Analytics. For Business intelligence and data science in Finance, Risk Analytics has become vital areas.

A company can increase the security and trustworthiness of the company using risk analytics of data science. Data is the core of Risk Management and Risk Analysis because it measures the gravity of the damage and multiplies it with the frequency of loss. The knowledge of problem-solving, statistics and maths is essential in the field of Risk Management for any professional.

Raw Data majorly consists of unstructured data which cannot be inserted into a standard excel spreadsheet or a database. Data science plays a significant role in this using their frameworks to analyze the data.

A company faces various kinds of risk which can originate from the market, credits, competitors, etc. The first step in managing the risk is identifying the threat. After that, monitoring and prioritizing the risk is essential.

A risk analyst has first to analyze the loss and the pattern of the loss. It also has to identify the source of loss. Financial data science helps to create models that help in analyzing areas.

A company can use massively available data like financial transactions and customer information using which they can create a scoring model and optimize the cost. This is an essential aspect of risk analysis and management which is used to verify the creditworthiness of a customer.

The past payment records of a customer must be studied, and then it must be decided whether the loan is to be paid to the customer or not.  Many companies now employ data scientists to analyze the creditworthiness of customers using machine learning algorithms to analyze the transactions made by customers.

Read: statistics for data science free courses

2. Real-Time Analytics

In the earlier period, data were processed and analyzed in batches which means one by one and not real-time. It had a huge disadvantage of data being old by the time it was processed and analyzed. The decisions taken on historical data will not be very helpful or accurate.

Real-time analytics means a huge amount of data is being generated and analyzed continuously. It is a continuous process. The use of data science in finance makes the process much easier. Machine learning algorithms perform real-time analytics of data. Real-time analytics is very important because old data is of no use to the firm. It is a setback for the firm. Firms need to analyze the constant changes to be at par with the market and preferences of the customers.  

Many various industries started having a problem because they require real-time data to gain insights about present circumstances. However, now it is possible to access the data with minimum delay due to the development of dynamic data pipelines and advancements in technology.

Data Science has now become a very crucial part of Finance and Financial Institutions to keep track of all financial attributes, credit scores and transactions without any issue of latency. Read about the contribution of Data Science to the banking industry.

3. Consumer Analytics

Many financial institutions have consumer personalization as their major operation. With the help of data scientists, companies can gain insight into the behaviour of consumers in real-time with the help of real-time analytics to make better strategic business decisions. Data Science is being used in many financial institutions such as insurance companies to understand the consumer to reduce losses by eliminating below zero customers, to increase cross-sale and to measure the lifetime value of a customer.

By observing the behavior of different customers by means of financial data science, a data scientist can make the best use to increase the purchasing habits and frequencies of potential customers by following the below-mentioned techniques –   

Bifurcation of customers as per their habits – Customers can be bucketed into different segments, for example, a tech-savvy customer from an online platform is expected to buy latest products more frequently than the ones who purchase only based on the requirement. There are other segments of customers who generally wait for special sales offers. 

Offer to enhance customer purchase capacity – With this tool, a business house lures a periodic customer to purchase 25 to 30% more of its periodic purchase value by offering him attractive schemes. 

Prevention of attrition – Sometimes, customers tend to switch over service providers or brands in view to get more value for their money. Switching between different brands of cell phones or cellular data service provider is the most common example of attrition cells. Business houses try to capture the customer grievances and address them with value-added offers thereby preventing attrition.

upGrad’s Exclusive Data Science Webinar for you –

Explore our Popular Data Science Courses

4. Customer Data Management

Firms record the purchases and buying habits of the customers. Their choice and preferences are taken into account. Data science in finance helps in analyzing the purchasing trends of the customer and serving the customers according to their choices. 

Data is everything, and the financial institution needs customer data for processing and analyzing the information. The functioning of financial institutions has completely revolutionized after the introduction of big data in the world of data science. Large numbers of transactions and social media have contributed a lot to the variety and volume of data.

There are mainly two types of data available, which are Structured Data and Unstructured Data. It is effortless to handle, process and analyze the structured data as it is already in a specific format. But in the case of Unstructured Data, It is not as smooth as structured data to process and analyze it. Various top data science tools are used to process and handle unstructured data. 

One of the most important aspects of Big Data is Business Intelligence which is extracted by using machine learning to gain insight about the customers and their behaviour. Meaningful insights from data can be generated using various tools such as text analytics, data mining, Natural Language Processing (NLP) and many more. A thorough analysis is conducted on the data of customers using machine learning algorithms to analyze the changes and trends in the financial market and values.

Read: Career in data science and its future growth

5. Personalized Services

Through consumer analytics and consumer data management, firms analyze the choices of the buyers and the kinds of products they are interested in. Financial data science makes this process much more hassle-free. Personalized services are a great way for the financial institution to build a good relationship with its customers and increase their sales by offering them what they are interested in.

Insights are generated and analyzed the customer information related to the interaction by financial institutions by employing many kinds of tools and techniques. Natural language processing and speech recognition based software is handy to financial institutions nowadays for better communication with consumers.

Actionable insights are gained, and the needs of the customers are understood from the information provided by customers to the financial institution, which leads to higher profit to them. Those insights are also used to provide better services, and business strategies are optimized according to each customer.

Read: Career in finance and its Scope.

6. Financial Fraud Detection

One of the major concerns for financial institutions is a fraud. As the number of transactions is increasing, it is also increasing the chances of fraud. But now, financial institutions can keep track of scams and frauds in a better way by using the analytical tools to analyze the big data.

Identity thefts and or consecutive abnormally high purchases from credit cards indicate financial fraud. Banks often block such credit cards to prevent financial fraud. Following this way the banks can protect the company as well as the customers from financial loss. Data science tools have helped in fraud identification to a great extent and it has helped the film to protect itself from any bad debt. 

Credit card fraud will remain at the top of the list of financial scams. There has been an improvement in the detection of these types of fraud because of the development of algorithms. Anomaly detection is much easier now with higher accuracy. The losses from the scams are promptly minimized by the detection alert received at real-time by the companies about the anomalies in financial purchases.

Unusual patterns in trading data are identified using various machine learning tools. Financial institutions are alerted, and the anomalies are taken for further investigation. There are many other types of fraud also which are detected by understanding the pattern of the data which seems to be suspicious and many insurance companies are using several clustering algorithms to segregate the data and understand the cluster pattern of information.

Read our popular Data Science Articles

7. Algorithmic Trading

One of the essential parts of financial institutions is Algorithmic Trading which is used to compute complex mathematical formulas at lightning speed which helps in devising new trading strategies by financial institutions. Big Data has wholly revolutionized Data Science and Algorithmic Trading in a much bigger way which has lead to a complete change in the functioning of the industry.

Massive amount of data are streamed which are processed through algorithmic trading, and a data model is produced which describe the information about the data streams. There are various kinds of data streams like stock data, fundamental data, financial news data, futures data, macroeconomic data, etc.

These different types of data can be obtained from Yahoo finance, wall street horizon, RBI, google news, and many more. By the understanding of massive datasets in a better way, financial institutions can make better predictions for the future market, and that is the aim of the analytical engine.

Our learners also read: Top Python Free Courses

Top Data Science Skills to Learn

Let’s Wind Up

Data Science has become very important in the Finance Industry, which is mostly used for Better Risk Management and Risk Analysis. Better analysis leads to better decisions which lead to an increase in profit for financial institutions.

Companies also analyze the trends in data through business intelligence tools. Accuracy in the detection of anomalies and fraud have improved with the use of Data Science. This has helped to reduce risks & scam, minimizing the losses and saving the reputation of the financial institution. 

If you are curious about learning data science to be in the front of fast-paced technological advancements, check out upGrad & IIIT-B’s Executive PG Programme in Data Science and upskill yourself for the future.


Rohit Sharma

Blog Author
Rohit Sharma is the Program Director for the UpGrad-IIIT Bangalore, PG Diploma Data Analytics Program.

Frequently Asked Questions (FAQs)

1How does data science contribute to risk analytics?

Risk analysis is an important aspect of running a business. Every company analyzes the risks before making a decision. For business intelligence and data science in finance, risk analytics has become a vital area. Since data is the core of risk management, data science plays a crucial role and measures the gravity of the damage and multiplies it with the frequency of loss. Analyzing the risk analysis with data science can increase the security and trustworthiness of a company. The companies initially have raw data which can not be inserted into excel sheets directly so we use data science techniques to first sort this unstructured data. The financial transactions and customer details can be used to create a scoring model and can optimize the cost.

2How can one imply data science in the finance sector?

Data science is tremendously efficient when it comes to the finance sector. It provides modern approaches to the companies. To be able to implement data science for finance, one must excel in the skills that are mentioned here: Statistics and Probability are the two most important mathematical concepts of Data Science. Descriptive statistics including mean, median, and mode, linear regression, hypothesis testing are some of the topics of statistics and probability. You must go with one programming language and master it to code in it. There are plenty of languages out there but Python is the most preferable language due to the libraries and modules it provides. Machine Learning and Deep Learning are two separate domains and the subsets of Data Science at the same time. These topics will help you to get far in data science.

3How do personalized services add value to the finances of a company?

Personalized services help institutions to improve their relationships with their customers. Services like natural language processing and speech recognition increase the customer base as more and more people can use the features in their own mother tongue. Cookies play a major role in finding the customer’s needs and preferences so that the institutions can work upon them.

Explore Free Courses

Suggested Blogs

17 Must Read Pandas Interview Questions & Answers [For Freshers & Experienced]
Pandas is a BSD-licensed and open-source Python library offering high-performance, easy-to-use data structures, and data analysis tools. Python with P
Read More

by Rohit Sharma

04 Oct 2023

13 Interesting Data Structure Project Ideas and Topics For Beginners [2023]
In the world of computer science, data structure refers to the format that contains a collection of data values, their relationships, and the function
Read More

by Rohit Sharma

03 Oct 2023

How To Remove Excel Duplicate: Deleting Duplicates in Excel
Ever wondered how to tackle the pesky issue of duplicate data in Microsoft Excel? Well, you’re not alone! Excel has become a powerhouse tool, es
Read More

by Keerthi Shivakumar

26 Sep 2023

Python Free Online Course with Certification [2023]
Summary: In this Article, you will learn about python free online course with certification. Programming with Python: Introduction for Beginners Lea
Read More

by Rohit Sharma

20 Sep 2023

Information Retrieval System Explained: Types, Comparison & Components
An information retrieval (IR) system is a set of algorithms that facilitate the relevance of displayed documents to searched queries. In simple words,
Read More

by Rohit Sharma

19 Sep 2023

40 Scripting Interview Questions & Answers [For Freshers & Experienced]
For those of you who use any of the major operating systems regularly, you will be interacting with one of the two most critical components of an oper
Read More

by Rohit Sharma

17 Sep 2023

Best Capstone Project Ideas & Topics in 2023
Capstone projects have become a cornerstone of modern education, offering students a unique opportunity to bridge the gap between academic learning an
Read More

by Rohit Sharma

15 Sep 2023

4 Types of Data: Nominal, Ordinal, Discrete, Continuous
Summary: In this Article, you will learn about 4 Types of Data Qualitative Data Type Nominal Ordinal Quantitative Data Type Discrete Continuous R
Read More

by Rohit Sharma

14 Sep 2023

Data Science Course Eligibility Criteria: Syllabus, Skills & Subjects
Summary: In this article, you will learn in detail about Course Eligibility Demand Who is Eligible? Curriculum Subjects & Skills The Science Beh
Read More

by Rohit Sharma

14 Sep 2023

Schedule 1:1 free counsellingTalk to Career Expert
footer sticky close icon