Data Science for Finance – Top 4 Examples & Use Cases

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Last updated on
23rd Mar, 2022
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23rd Mar, 2022
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Data Science for Finance – Top 4 Examples & Use Cases

Have you ever imagined predicting the future or understanding when to invest in stocks or how to properly do asset allocation or analyse the sentiment of the market or how the banks approve or reject loans or how they detect fraudulent transactions and so on? Then you’ll not be surprised to know that data science plays an active role in finance, as in other fields of work in our world. If you’re curious about how you can start learning Data Science, a simple search for Data Science with Python courses, will lead you to options that suit your time and budget.   \

Let us now look at what data science is and how it is transforming the finance industry.   

What is Data Science?

Data science incorporates computer science, mathematics, statistics, data analysis, and visualization skills. It relies on statistical methods and algorithms to extract structured and unstructured data insights. People who practice data science are called data scientists. 

A Data Scientist’s job is to develop processes for collecting, cleansing, and storing data, mining those data for patterns, and using that to develop and deliver strategic solutions to key problems. As data is termed as the new oil, there is a lot of data sitting in databases, and using data science can be helpful to interpret that untouched data to bring meaningful insights and drive the organizations and societies around the globe to make better decisions.  

Terms like Artificial Intelligence (AI), Machine Learning (ML), and Data Science are often used together, so let’s first  understand these in brief. 

AI enables machines to replicate human intelligence and Machine Learning is the subset of AI, which focuses on understanding historical data and drawing insights to drive informed decisions. 

The machine learning field can be classified as follows: 

  • Supervised Machine Learning: Given a dataset, consisting of features and target, machines try to derive insights and learn from the provided datasets also known as experience so next time a similar data point is encountered machine can easily predict the target. In short, Supervised ML can be applied to a labeled dataset. Example: Predicting the salary of an employee using experience, qualification, and several other features  
  • Unsupervised Machine Learning: Given an unlabelled dataset, machines try to infer patterns in the dataset and try to group the dataset from the derived features. Example: Clustering people into different groups for targeted marketing 
  • Reinforcement Machine Learning: This type of learning is based on reward function. The machine learns from its experiences in the environment by following the trial and error method to maximize long-term rewards. Example: A machine learns how to play chess by playing chess in the simulated or real environment. 

Learn more about data science course, the live case studies and projects you will come across when you enroll for a data science training at Knowledgehut.  

Data Science Lifecycle

Data Science Life Cycle follows the following steps 

  • Business understanding: First, we need to set the objectives for the problems to be solved 
  • Data Mining: We need to gather relevant data for the problem at hand 
  • Data Cleaning: It is a fruitful step in the lifecycle as data is not always found in clean and ready to use format, we need to clean and pre-process it before using it for analysis and modeling 
  • Data Exploration: Once data is cleaned and pre-processed, we can understand the data by exploring it on different fronts, visualizing and analyzing the data to understand different views 
  • Feature Engineering: We need to select meaningful features from the data, as not all data can be used for modeling as some might contain redundant or noise in the dataset 
  • Modeling: Building machine learning models, evaluating their performance, using those models to make predictions, and most important, explaining why the model made those predictions 
  • Communicating Insights: We need to communicate the findings to the key stakeholders, that we can achieve using plots and business metrics that a non-technical person can understand 

Data Science Life Cycle

Data Science is used in several industries and sectors, including Finance, Healthcare, Retail, and many more. Let’s understand how it is used in the finance industry.  

How Is Data Science Used in Finance?

Data Science in Finance industry is about applying advanced statistical, machine learning techniques to financial data sets and solving some of the challenges faced by the finance industry. With the help of data science, the finance sector can understand the pile of data sitting in their system to take calculated risks to make profits. Working in the field of Data Science not only required technical skills but also domain knowledge, and how to use both hand-in-hand to solve complex problems. 

Benefits of Using Data Science for Finance

Let’s understand the benefits of Data Science in Finance through some of the use-cases. There are a ton of data science applications in finance, some of the data science use cases in finance are as follows  

1. Fraud Detection & Prevention

Banks and financial firms need to detect and prevent frauds to reduce their losses. Data science in banking and finance is the solution to the problem. The menace of frauds is increasing as with the increase in the transactions, with data science and machine learning, we build algorithms that can process large datasets to find patterns in the user behavior and flag the transaction as fraudulent or not, and these detections alert the financial institutions to take measures to prevent frauds either by blocking the customer account. The algorithm’s accuracy can be improved by feeding as much data we have at our disposal which in turn helps to reduce false negatives, the scenario when there is a fraud (Positive class) but the algorithm detects no fraud (Negative class). Let’s see how a real-life credit card fraud data set looks like.

Data Science for Finance

Image Source: Kaggle

The dataset has features such as transaction date and time, merchant, category, amount of transaction, city, state, date of birth, job title, and some other features with is_fraud as target (0: no fraud, 1: fraud). With Data Science, we can visualize the data, find some insights and also apply some machine learning algorithms to train a model to detect fraudulent transactions in real-time which is hard to analyse manually as the dataset size could be millions of rows or more.  

If you are interested in how to build the ML model then check out the Kaggle link above.  

2. Risk Management

Data Science helps financial institutions to measure and manage risk across the organization and increase security. These institutions face risks in several forms such as credit risks, market risks, etc. Market risk exposures and valuations can be accurately simulated and help them to monitor the risk factors. Also, banks can manage their risk on loans by analyzing the customer profile and predicting the default score, that is how likely a customer could default or be unable to repay a loan.   

3. Customer Analytics

Banks and financial institutions analyse the customer’s data to get insights about their credit history, behavior and offer them different services and products recommendations based on their profile. Also, they cluster the similar behaving customers for directed marketing and understand the cross relations based on transactional and behavioral factors. Using Natural language understanding, financial firms can detect the sentiments of customers on different offerings to improve their services and customer experience and to retain customers as it is always way cheaper to retain a customer than to acquire a new one. For example, if a competitor’s bank is running some promotional campaign and is receiving positive sentiment from people then the bank might need to consider running a similar campaign for their customers.  

4. Algorithmic Trading

Data Science is applying statistical equations to data to get insights. Applying forecasting algorithms helps to analyse the vast historical data to predict future trends and to make informed decisions, which in turn can be automated and deployed as a trading strategy. Artificial Intelligence-powered algorithmic trading trades securities at an extremely faster rate as compared to human traders, and could potentially reduce risk and maximize returns. Sentiment analysis is also used to predict people’s sentiment towards a particular stock, which can be done from public tweets, interaction on social media, blogs, news articles and could be used as a factor while making a buy/sell decision of securities. Let’s see how stock market data looks like.  

Data Science for Finance

The dataset has a Date column, which accounts for historical timestamp and other columns as Open, High, Low, Close, Adj Close, and Volume. We can use this dataset to visualize the historical trends and train a machine learning model to predict the closing price of a stock, one day ahead or one month ahead based on the requirements.   

Data Science for Finance

We have talked about the Reinforcement learning concept, we can use RL to build trading bots. Bots can learn from the stock market environment using a trial and error strategy to optimize the maximum profits. With RL-powered bots, we can save time and can generalize across different stock industries.  

You can read more about the impact of reinforcement learning in the finance in this article. 

11 Roles and Responsibilities of Data Science in Finance

A Data Scientist has to put on several hats depending on the enterprise they work with. The core responsibility of someone working in Data Science is to make sense of the data, learn about the past and hypothesize how it can be used to make a decision in the future. 

There is a lot of overlap between the roles in data science, General role and responsibilities are as follows: 

  • Data Analyst: Collecting, cleaning, and extracting data. Generally, works in SQL 
  • Database Administrator: Manages database instances. Responsible for performance, availability, and security of the databases 
  • Data Modeler: Creates data models, entity-relationship models, and design databases. They make sure the data is representative to use 
  • Data Engineer: Ensures data quality, build ETL pipelines to extract, transform, and load data to data storage solutions 
  • Data Architect: Responsible for designing and maintaining data pipelines. Focuses on scalable and cost-efficient data handling solutions 
  • Statistician: Understands business needs, develop hypotheses, and statistical experiments 
  • Business Intelligence Analyst: Collects business and functional requirements, they also report, present, and communicate analytical results to key stakeholders 
  • Business Analyst: Understands business and industry requirements to structure scope and technical objectives 
  • Quantitative Analyst: Uses quantitative methods to help companies make business and financial decisions 
  • Data Scientist: Analyse data to extract actionable insights to drive the business. Communicating findings through visualizations and exploring areas that can make use of data-driven decisions 
  • Machine Learning Engineer: Building machine learning models, moving the models/ solutions to production to get real-time insights from the data 

If you want to dive deeper into Data Science, then check out these data science with python courses. 

How Much Can Data Scientists in The Finance Industry Earn?

Now for the money talk, the salary differs based on geo-location, years of experience, relevant skills set, and many more factors subject to market trends. However, we can get an estimate of how much one can expect in the Finance industry in a data scientist role. According to an article on Forbes, an average salary range between $90000 to $150000, in US depending on experience.  

Data Science for Finance

Image Source: Forbes

For detailed analysis, you can refer to this article. This is just an estimate; however, the sky is the limit as the role requires continuous learning and being updated with the market trends.  

Learning & Earning Paths

I hope this article has ignited the fire to start your data science career in the finance industry.  

Let’s summarise what we have learned and explored through this article. 

A lot of financial institutions design predictive systems to understand and manipulate data sets, gather insights from vast amounts of data, and which help to make more informed investment decisions. With the help of Data Science, they can provide a more detailed understanding of market trends and can help investors to stay in the pool of profits, or to avoid any fraud to remain themselves in profits.  

For investors to properly manage their portfolios, they need to visualize datasets, find useful patterns and gain valuable insights such as stock daily returns and risks, and with the help of tools and technologies available in the market such as python programming language, we can create interactive visualization and estimate stock returns. We can even use reinforcement learning to automate the trading through bots and deals in risk optimization in peer-to-peer lending, however, we should proceed with caution. Check out the KnowledgeHut Data Science with Python courses if you are looking to enhance your current data science skills and make that big jump in your career.  

Frequently Asked Questions(FAQs)

1. Is Data Science Good for Finance?

Data Science helps in better analyzing the data which in turn leads to better decisions that have a direct positive impact on the profits of the financial institutions.  

2. How is data science used in finance?

Data Science is used in the finance industry in areas such as algorithmic trading, fraud detection, customer analytics, risk management, and many more.

3. Is Data Science Helpful for CFA?

CFA is about finance and investment analysis, and with the help of data science, these tasks can be performed more precisely and also help to bring automation to it.  

4. How Is Data Science Revolutionizing the Finance Industry?

Data science in the finance industry helps in identifying patterns from the existing customer transactions data, stock market data and provides enriched results/analysis which leads to strong decision-making.  

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