With the advent of latest technologies and an increase in awareness, Data Science’s reach has only widened.
Organizations have felt the need to mine data and thus produce useful insights to help them augment business decisions.
To keep up with the modern needs, one industry that has adopted data science to the best use is BFSI. Banking, Financial Services and Insurance together constitute the realm of this sector.
Need of Data Science in BFSI
Data science has not just improved processes but also provided a better and safe transaction environment to customers. This is an important factor when increased data certainly means an increase in unwarranted activities.
Data science is thus, a necessity for the BFSI sector. It helps in weeding out frauds, retaining existing customers, attracting new ones and enhancing performances.
How is Data Science Transforming the BFSI Sector?
After assessing the need for data science and its importance for the BFSI sector, I will now walk you through the applications of data science in the BFSI industry.
These are the ways to show how data science is transforming the BFSI sector. If you are in the BFSI industry, you must have already worked with data to either ensure data safety, update customer records, real-time defect detection, quick grievance redressal etc.
1. Customer Segmentation
With an aim to deal with them in a personalized way, banks group their customers on the basis of common factors like purchasing habits, characteristics (like place, income, age etc.), patterns, behavior etc.
Clustering and Classification machine learning techniques work towards this segmentation and recognizing new customers.
Customer segmentation also helps the banks recognize which customer will be more profitable for them as compared to others. This then becomes the differentiation criteria. The process results in healthy and profitable bonds with customers, offering customized services, analyzing groups to provide and improve services.
2. Fraud Detection
With increased use of data science in the BFSI domain, scamsters cannot get away with a fraud. Banking institutions can now easily detect any suspicious dealing or irregular transaction.
This might be the start of a big fraud. The ease of banking through your mobile or internet has also increased the chances of phishing, scam and fraud. But with banks adopting continuous analysis of transactional patterns, timely fraud detection is possible.
Time is important because the sooner a fraud is detected, the faster the activity is restricted for that particular account.
Support Vector Machines and K-means Clustering are two such personnel always on guard. They pre-process and classify the data. While the latter is used for feature selection, the former is then used to classify data as fraud or genuine.
3. Risk Modeling
One of the top priority tasks for banks, risk modeling lets them prepare appropriate strategies for performance analysis.
One of the most important parts is Credit Risk Modeling. Through this, the banks analyze the way borrowers will repay their loans. There might be defaulters too, and risk modeling helps a bank recognize the defaulters, assess the default rate and modify their lending policies.
Risk modeling quantifies and tracks a bank’s performance through analytical tools.
4. Recommendation Engines
Do you undertake credit transactions more than debit transactions? Do you fall in the higher range of tax slab?
All this information and much more is used to provide customized experience and recommended products to customers. Banks also assess the products that are hit and the ones that are not accepted well, based on the customer’s purchase history. This includes their own products and the products of partner companies.
Will product-centric offering work more or customer-centric? Did no-questions-asked loan scheme work better than the 10% interest scheme?
Many such considerations, product performances and customer preferences are taken into account before making any recommendation.
5. Lifetime Value Prediction
A Customer Lifetime Value provides the discounted value of all the revenues to be paid by the customer in the future.
This comes in handy when banks wish to predict future revenues and know which of the existing customers will stay for long and help in future revenue generation. Predictive analytics is one such tool that helps bank group potential customers and assign a specific future value.
This is helpful in judging whether a company should invest their resources on these customers and if it will be beneficial at all.
6. Real-time Predictive Analytics
When computational techniques are used to predict future events, the process is known as predictive analytics.
Real-time analytics and predictive analytics are the two types of analytics techniques that come to a bank’s rescue.
With real-time analytics, it is easy to understand the problem. On the other hand, predictive analytics facilitates the choice of solution for the problem.
7. Robo Advisor
A robot-advisor isn’t a new concept in the finance domain.
With an intention to provide precise and personalized recommendations, Robo advisors are being roped in. A Robo Advisor will do an in-depth study of customer’s data and then suggest the best-suited insurance plan. Do you know customers prefer them over their human counterparts?
Less fees and precise recommendations are the prime reasons.
8. Algorithmic Trading
Algorithmic trading is one area that is greatly affected by real-time analytics.
Every second counts and is put to stake. With this, comes an opportunity for the financial institutions to make real-time fruitful decisions. What’s the base of this decision? This is done after analyzing updated information from traditional and non-traditional data.
Seems easy? It is not; data changes within seconds, so what is valuable now might not be within a short span of time.
What is the need of the hour? To ace the competition, the businesses should have an agile methodology to analyze it. Machine learning techniques offer methods to speed up the process of forecasting market opportunities.
What’s the key here? These techniques aren’t stagnant but constantly evolving and improving as per the market needs.
Data generation won’t stop and so will the need to analyze it. The BFSI sector is expected to benefit a lot from data science. After all, strategic planning, decision-making, predicting customer behavior, implementing suitable policies, etc., the core areas of this sector, are data-driven.
Expect to witness more such applications due to the constant advancement in the field.