Data remains the ultimate fuel for the finance industry. Data science in finance is now picking up pace and as the adoption of data-driven methodologies, tools, algorithms have started contributing to the growth of the financial sector. It tends to identify patterns from the existing customer data, providing insightful results. As data science makes use of advanced computational techniques it leads to strong strategies for business productivity and decision making.
Ever wondered why data science and finance make such good allies? Worry not ! In this blog we are going to cover the relation that exists between data science and finance and how data science tends to complete finance while simplifying processes and reading the cost for processing data.
How Data Science in Finance Complements Each Other ?
Some of the popular reasons that can make data science in finance an inseparable part are mentioned below –
Tends to simplify the processes, thereby reducing cost for processing data.
Supports process as well as operational transformation within the organizations
Helps in managing and optimizing inventories effectively.
Help in making quicker decisions in terms of customer experience, touchpoints and customer journey.
Provides accurate knowledge for making informed decisions.
Accurately segment the customers and evaluate the risks of investments.
Allowed for advanced monitoring of the competitors in real time.
The advantages of data science in finance are quite massive. To name a few- algorithmic trading, automated risk management, consumer analytics, and fraud detection. We now can understand how data science opens up new roadways in exploring a promising career.
What Does A Financial Data Scientist Do ?
A data scientist in finance gathers data from financial technology, insurance funds, investment banking, and retail banking. They use data science techniques for interpreting and understanding financial data. Data Scientists also build statistical analysis and financial model techniques for the financial industry. Making recommendations for financial decisions is the primary job role of these data science professionals.
Other common responsibilities of finance industry data scientists are as follows :
Detecting fraud that is using machine learning algorithms to identify errors in transactions.
Examining behavioral data for identifying opportunities for growth within the firm.
Building risk models for helping banks and other financial institutions. Such risk models could easily analyze the potential risk of an investment.
Developing financial modeling strategies and dashboards for visualizing findings for stakeholders.
Offer assistance to develop targeted experiences. This job role helps finance businesses in improving the customer experience.
Recognizing opportunities for reducing costs and multiplying efficiencies.
Data extraction from databases and then the extracted databases is used for advocating experiments, projects and products.
Data analysis for measuring data quality and product performance.
How to Become Data Scientist in India
To become a financial data scientist, around 50 % of aspirants have obtained a Master’s degree. Research shows that only 34 % of Bachelor’s Degree earners could make a place as data scientist in finance. For a minimum qualification to adhere to, a degree in computer science, economics, finance, business studies, statistics and mathematics can help you land a lucrative career in data science in finance.
Here are some of the steps to become a finance data scientist in India
1. Earn a Bachelor's Degree
First and foremost, consider earning a bachelor’s degree. A bachelor degree in Maths, Statistics would help you gain fundamental knowledge about the concepts related to the data science domain. You can also choose subjects like Science, Computer Science, Economics, Statistics, Physics, and Engineering too.
2. Learn Programming
To be a financial data scientist in India, you must be an expert in programming in a variety of languages. Try to acquire hands-on experience with languages like C/C++, Java, MATLAB, S, and Python.
3. Master Database Skills
Financial data scientists have to work with huge sets of databases. Hence, this career path demands a clear understanding of relational database management systems. Try to gain skills working with classic RDBMS like SQL server, MySQL, and PostgreSQL.
4. Learn Data Handling
As a finance data scientist, you have to handle large sets of data from financial streams. So, you must develop a clear cut understanding of how to organize raw data. Also, learn the process of data interpretation. All of these help data scientists make recommendations about financial decisions.
5. Consider Earning Certification
Online data science course offers certification that verifies your skills. So, you must pursue certifications related to data science & financial industry. Remember, these certifications display your knowledge helping you in recruiting.
How is Data Science helpful in the Finance Domain?
1. Risk Analytics
Risk Analytics is one of the key advantages of Data Science in finance. A company depends on a data scientist to analyze and manage risk. Risk analytics aids data science to help a company know about the potential risks that can damage its existence. Financial data scientists help financial firms make strategic decisions and increase the security of the company.
2. Consumer Analytics
Consumer personalization is an extensive function of financial institutions. Financial data scientists rely on data insights from consumer behavior to make appropriate business decisions. Financial institutions and insurance organizations rely on consumer analytics to evaluate the customer lifetime value, enhance cross-sales & optimize the losses.
3. Algorithm Trading
Algorithm trading is a highly lucrative application of data science in the financial industry. Finance and investment firms rely on ML and AI-based models to estimate stock prices. This helps in funds protection thereby helping businesses generate large profits.
4. Data Management
Advantages of Data Science in finance count in data management. A financial domain generates a large amount of data. The data leverages are of high velocity, high volume, and also different varieties. Have you thought about how financial institutions keep track of all the transactions? It's with the help of financial data scientists & Big Data techniques.
5. Personalized Services
Financial Institutions manage and organize personalized services for their customers. And this is possible only due to a variety of data science techniques. Big data, speech recognition, and natural language processing-based software help financial firms analyze customer information and generate insights into their interactions. Furthermore, financial institutions can now take actionable insights as per their customer needs which multiplies profit.
Financial Data Scientist Salary – India
The salary offered to a professional in data science in finance varies. The average annual salary figure depends on your skill level, area of expertise, job title, and the organization you're working for. Below, we have estimated how much data scientists might be able to earn in the finance sector.
In India, Financial Services Data Scientist's salary ranges from ? 8.2 Lakhs to ? 14 Lakhs. The mean average annual salary is worth INR 9.4 Lakhs.
A mid-career Data Scientist (in financial service companies) with 4-9 years of experience earns an average salary of ?15.9 Lakhs annually.
Conclusion
In this post, you explored the job role, roadmap, advantages, and salary of data science in the finance sector. Remember, the increment in statistical techniques and modern technologies have transformed the way the finance industry works. So, a career in data science as a financial analyst is worth exploring. If you're interested in a career in financial data science, you must try online data science/analytics short courses offered by The IoT Academy.
