Data science job opportunities are already high rise. It’s expected that within the next two years, the global job market is going to experience a major hiring boom in the data science job market. How you can Career shift to Data science from BFSI domain? Let’s Discuss.

Unlike everyone else, do you also want to switch to a data science career? But, experiencing a major dilemma as you belong to the Banking, Financial Services and Insurance (BFSI) domain?

Let me first congratulate you for being from the BFSI domain. You might be thinking if I have lost my mind! But what I am going to tell you now will change all of your misconceptions about me.

Do you know, being from the BFSI domain, you hold the most promising career opportunity in the field of data science and artificial intelligence? Yes, I am not kidding. In fact, until now, very few data science aspirants are aware of this real-world truth.

So, your domain is no more a roadblock to your successful data science career transition.  Now, you need to get aware of the steps that will help you achieve a successful data science career switch at the minimal possible time. But, before that, know about the opportunities of data scientists lying across the BFSI sector.

What are the available scopes of data scientists in the BFSI domain?

Scope#1: Financial Fraud Management

With the rising demand for open banking and mobile banking, the risk of financial fraud increases exponentially as the customer database is expanding very fast. Hence, earlier fraud management technology is now nothing but a lame soldier. Here comes the need for data science in BFSI. The machine learning algorithm is the crucial saviour in this regard.  Such an algorithm owns the power of detection and prevention, internet banking, credit card, debit card, and other transaction-related frauds within the big data network.

Scope#2: Customer Segmentation

Customer segmentation is the crucial measure for the BFSI domain. Customer segmentation helps a financial organization observe, analyze their customer’s financial behaviour, and offer product and services accordingly. But segmentation also becomes crucial to identify the sustainability and profit level of different financial products and services and allocate best-fit marketing resources to the different client segments. Now, as the expansion of the database has become so huge, machine-learning artificial intelligence becomes an effective solution. 

Scope#3: Credit Score generation and Customer Lifetime Value (CLV) Prediction

In the BFSI sector, the most critical job is to manage the financial portfolio of customers. Nowadays, with the benefits of online banking, a client invests in multiple financial products and services. Certainly, all those products, as well as services, do not belong to the same company. So managing the profile of a single client is now a matter of headache, then you can easily imagine how complex it will be for all of the existing profiles in the big database. 

Machine learning and Deep learning algorithms have provided several solution strategies to this critical problem. With the help of automated commands and data analysis generated using ML and DL technology, it is now easier to keep a regular eye on client’s credit scores and CLV. Such a score helps the BFSI domain identify the profitable and risk full customer segments in terms of providing loans or recommending any add on services.  

Scope#4: Personalized financial product recommendation

The requirement of every customer is not the same. Wrong recommendation, as well as a wrong decision, can destroy the financial backbone of a customer. If such disaster keeps continuing, then no doubt the responsible BFSI will lose their clients and get collapsed. Unsuitable recommendation and information may also frighten the clients, and they may decide to switch to some other company or services. 

However, the combination of AI and deep learning has made it easy to offer their customers personalized and timely product recommendations.

How to Become A Data Scientist In BFSI Domain? – A Step-By-Step Guide

As you are already in the financial domain, a career shift to data science is easiest for you than for aspirants from other domains.  You may not believe it, but the hidden truth is shifting to a data science career is not as much as easy for an IT professional compared to you.

Advanced level of Mathematical and statistical knowledge is the biggest advantage for you. There is a myth that data science demands programming expertise. The truth is data science needs highly creditable mathematics and statistics talent. You can still manage a successful data science career with basic programming skills but without statistical and mathematical expertise- it’s a matter of joke. 

 So you are already 10-steps ahead regarding the data scientist qualifications. So while choosing a data science course for finance professional ensures that offers crisp and end-to-end learning opportunities for on the following modules:

  1. Dive to Complex mathematical and statistical problems: You own the ability to play with advanced excel, SPSS, etc.  To handle a larger amount of data associated with the data science project, I will be working more with regression, simulation, linear programming, and time- series problems. So, that needs some knowledge of more complex tools like Tableau, Matplotlib, etc. 
  2. Step up your Programming skills: Nowadays, working on the fintech domain demands basic programming knowledge of python and java. So, it’s predictable that you already bear entry-level knowledge of python. As python is the most widely used language in the data science domain, what you need to do is concentrate on the several analytical, numerical, and graphical libraries of the same. Be proficient with NumPy, Matplotlib programming. However, in the financial domain, you may need to program in R and C++ also. So, start learning these two skills along with your python brush-up schedule. Being from a technical background, C++ may seem a bit hard to you. But for sustainable data science career growth in the BFSI sector, it will be best if you opt for a separate C++ learning course.  
  3. Expand Your Database knowledge: In your financial career, you have already gathered plenty of knowledge about ERP. Do you know what database program ERP uses? It’s SQL. If you are passionate about a data science career and researched a lot on the courses and modules, you know SQL will be your next target. Lots of other database management systems, like MySQL, Oracle Database, Amazon Relational Database Service (RDS), to enter the data science domain of the BFSI sector, deep knowledge of SQL becomes a crucial skill criterion. 
  4. Machine Learning and AI:  For a better career opportunity, try to choose a learning program that is more concentrated in Artificial and Machine learning. However, compared to entry-level data science and data analytics courses, AI and machine learning are complex. Still, BFSI is the ultimate hub for AI and ML opportunities. Machine learning needs key skills: Statistical and analytical proficiency, which you already have and extensive programming knowledge to work with ML algorithms, which is not that hard.

You are now well-aware of your data science career transition path. What you need to do now is to choose the right data science and AI course for you. If you need to know more about data science and AI courses, visit

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