How Data Science Is Revolutionizing Banking Sector?


Machine Learning and Data Science are playing a crucial role in different industries today including Healthcare, Automotive, Retail, Transportation, etc. They also provide a huge potential for the Finance and Banking sectors. Data Science holds a strong presence in varied aspects of banking like risk management, pricing, customer outreach, product development, marketing outreach, as well as cost and revenue allocation. Data Science offers smarter ways for the banking industry to tackle different challenges faced by it today. These days, banks are taking advantage of machine learning to power different business operations.

Today, banks are generating a large volume of internal data like credit scoring, customer accounts, payments, assets, etc. And, the Banks are required to find out the linkages of internal data with the external data like interest rates, customer preferences, and macroeconomic variables. Moreover, banks are generating data at a very high speed due to the use of non-traditional or digital touch-points such as social media, the internet, IVR systems, ATMs, mobile, etc. The explosive growth in the amount, velocity, and variety of data make it inevitable for banks to use advanced analytics tools to take out useful insights from these large and complex data sources to make real-time decisions and stay on top of competitors.

Here are some areas in which AI and ML are playing important roles due to the power of Data Science:

Customer Support

Data Science enables banks to obtain important customer data, investment patterns, and cycles that allow these organizations to build a robust customer support framework. And, this information is helpful in analyzing the plans that customers have as well as the credits that they don’t have. Hence, it enables banks to offer the best offers to customers that are suitable to them.

Fraud Detection

Machine Learning is quite helpful in the successful detection and prediction of fraudulent activities. So, it helps in identifying malicious activities that involve frauds related to accounting, credit cards, insurance, etc. Detection of fraud is important to offer proper security to your employees and customers. 

Customer Data Management

Banks have to collect, check, and store a huge amount of data. However, instead of making it just a compliance exercise, the Data Science and AI technologies provide more possibilities for banks to analyze data to know about customers as well as to generate new revenue opportunities.

Risk Modeling

Risk Modeling is also crucial for banks because it enables them to create new strategies for evaluating their performance. The more important aspect of  Risk Modeling is credit risk modeling. Banks are these days integrating Stress testing and utilizing tools such as R, Python, and SAS to assess their financial health.

Customer Segmentation

Customer segmentation is about segregating customers into groups based on behavioral conduct or specific attribute like age, income level, region, etc. Data Science provides lots of strategies such as clustering, logistic regression, decision trees, etc that allow us to know about the customer lifetime value for every customer segment.

Operational Efficiency

Artificial Intelligence and Data Science help organizations in the prediction of demand based on the analysis of future events and recorded information through the propelled time-series analytics. This type of data insight helps businesses to evaluate volumes that teams have to handle in back-office function as well as call center traffic. With the help of these insights, organizations can make advanced strategic resourcing, asset planning, and more.

Final note

Technology is getting evolved every day and offering quality solutions for all people including businesses. The huge potential of Data Science cannot be neglected and an organization can harness this potential of the technology in the banking sector to generate huge revenues. By using the power of Data Science, banks can obtain a competitive edge in the market as well as build effective strategies and techniques using the insights from data and involve them in their decision-making process.        

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