What is Data science?
Data science is the detailed study of information obtained by analyzing vast amounts of data arriving from the organization’s data warehouses. Modern data science deals with inferring data and developing related algorithms by using appropriate technology. The demand for data scientists makes data science courses more popular.
Applications of data science include healthcare, targeted advertising, image recognition, voice recognition, gaming, augmented reality, etc. The introduction of data science in banking has got a lot of acceptance from half the planet. On the other hand, there are a lot of disagreements from high-end techies owing to the fact that banks can start performing data analytics in banking transactions, causing a breach of customer privacy.
Every bank account or credit card transaction can give data about your spending patterns and spending powers, which can be processed into precise information. This information can then be used for targeted advertising and marketing at the customer.
Few Applications of Data Science
As of now, data science has been widely applied in the following domains:
Data science can be used in image analysis to detect abnormalities and genetics to detect genetic patterns on DNA. It can also be used for drug development by solving chemical equations and analyzing biochemical effects on the human body.
b) Targeted Advertising
Data science algorithms decide the advertisement that you see while logging into web pages and applications in the backend.
c) Image recognition
One of the historical applications of data science is in the image recognition field, and most media companies have started using image recognition for user authentication into their systems.
d) Speech recognition
Apple launch Siri years ago, which uses data science behind it. The same applied to Google Voice, Amazon Echo, and Cortana.
The gaming industry has over 2 billion players worldwide, and there will be a lot of data gushing in. this data needs to be analyzed to generate insights for game improvements, monetization methods and induce game addiction among players
f) Augmented reality
Augmented reality creates an overlapping real world and virtual world and provides an interactive experience. The most trending game that came up with augmented reality was Pokemon Go, which exhibited heavy dependency on applications of data science.
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Top 10 Applications of Data Science in Banking
HDFC Bank in India was the pioneer in introducing data analytics in the banking segment in India.
This was started in early 2004, and back then, the main idea was to segregate the ACTIVE bank accounts and to make a decision on the inactive bank accounts. It took a few years for the banking segment to get into the data science model, but since then, there has been a steady rise in the dependency.
The use of data science in banking was an add-on long back, but now has become a necessity to keep up with the competition.
Especially after the global financial crisis of 2008, the use of data analytics in banking for customer predictions, fraud detections, and financial advisories to identify and predict market trends. Banks and financial institutions have a lot of user data. They know in and out of their customers, including complete personal details, income, spending patterns, purchase powers, investment risk appetites, etc.
Knowing all these and the formulating all these into an automated system based on data science and analytics helps banks to make credit decisions more accurately. This helps to provide customized offers to customers, launch new products and market them, etc. It also helps to segregate loyal customers from the rest so that banks can invest more on the relationship with the most loyal customers.
a) Fraud detection
Banks are now utilizing the data science to proactively detect fraud and provide customers with a high level of security. This is done by monitoring and analyzing user’s banking activities and to find out any suspicious or malicious patterns.
b) Customer segmentation for grouping customers
Classifying customers based on various factors helps banks to serve customers better. One famous algorithm for clustering in K-means, an interactive grouping algorithm that tries to find the ideal number of groups by executing the algorithm steps in loops.
c) Relative customer comparison
The availability of all customer’s data helps banks to compare individuals with the same spending or behavioral patterns. This helps the bank in qualitative predictions about customer requirements and in marketing similar products to similar customers.
d) Managing customer data
With the emergence of digital banking, every tom and harry has got a bank account. This led to zettabytes and yottabytes of customer data being stored on the banks’ storage repository. Not all these data are going to be useful and relevant. Data science helps banks to isolate the necessary and appropriate data and use them to predict customer behaviors iteratively, interactions, and preferences.
e) Prediction of Customer Lifetime Value (CLV)
Banks need to predict future revenues based on inputs from the past. This is best done using predictive data analytics in banking to calculate the future values of each customer. This helps in segregating customers, identifying the ones with high future value, and investing more resources on them in terms of customer service, offers, and discounted pricing. The primary data science tools used for this purpose are Generalized Linear Models (GLM) and Classification and Regression Trees (CART).
f) Risk Modeling
Risk prediction has become a primary concern for banks with risky credit products as well as investment banks.
Credit risk modeling
This allows banks to predict how their loans are going to be repaid and to foresee a defaulter based on past history and credit report. The risk modeling calculates a risk value for each case and the Credits Team only sanctions loans based on this Score.
Investment Risk modeling
Risk modelling is also used in investment banking, wherein risk-rewards ratios are calculated for risky investments. This helps in giving investment advice to customers as well as making the right decision in internal investment to generate profits for a fund.
g) Personalized marketing
Business earns their bread and butter when customers purchase new products. Data analytics in banking provides opportunities to model customized offers that suit individual customer needs. These offers are then applied to the right products and delivered to the right customers at the right time.
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h) Recommendation engines
This is one of the simple applications of data science wherein algorithms are used to filter and analyze user activity in order to suggest related and relevant items. Algorithms take in a lot of historical data, including transaction information, profile data, and similarity calculations to predict the most accurate associated items or products. There are two types of algorithms that work in customer-centric fashion or a product-centric fashion. They are known as “User-Based Collaborative filtering” and “Item Based collaborative filtering” respectively.
i) Real-time predictive analysis
In the banking sector, every user transaction is treated as a source of data on which analytics can be applied to decipher useful prediction and information. Predictive analysis is the process of using computational algorithms to predict futuristic events and trends. The use of data analysis in banking has come to rise mainly for these predictions. There are mostly two major prediction types
- Real-time analytics allows banks to understand the current problem that disrupts their business
- Predictive analytics that enables banks to realize a future problem and to select appropriate steps to take necessary preventive actions to mitigate any possible impact on business.
j) Customer Support
Excellent customer support helps maintain a long-term relationship with customers. It includes responding to customer’s queries and addressing their issues and complaints in a wholesome and timely fashion. Data science help create a targeted customer support service by automating and accurately presenting customer data to support specialist while he is on communication with the customer.
To keep up with the competition, almost all the major players in the banking segment have adopted data science techniques to deliver best businesses and value to customers. These are just a few areas of implementation of data science in banking. In the coming areas, the number and potency of the applications is only expected to increase further.
Is Python useful in the case of investment banking?
It is known that Python is user friendly due to its simple syntax. Apart from it being easy to learn and use, it has applications in many fields. Banking is one of those. Python is being used by banks to solve problems related to risk management, trade management and pricing as well.
How is big data useful for banks?
Big data has managed to change the bank culture by building service history and customer profiles effectively. No doubt, individualized customer profiles do create a positive impact on clients and make them rely on the bankers. Customers can now transfer money, deposit checks, and pay bills all from their mobile devices due to online banking. With big data analytics, the human contact that has historically been utilized to study consumer behavior and provide solutions for trouble areas has gone digital.
Do ML based algorithms function in a better way when it comes to fraud detection in banks?
Unlike rule-based systems, ML based systems recognize and detect thousands of patterns quickly. Without being explicitly programmed, machine learning can evaluate millions of data sets in a short amount of time to improve outcomes. To identify fraud, automate trading processes, and give financial advice services to investors, machine learning algorithms are utilized.
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