How Data Science Is Revolutionizing Banking Sector?

Nitin Kumar August 7th, 2020

Data Science and Machine Learning are playing a critical role in various industries like Healthcare, Retail, Automotive, Transportation, and more. They also offer a huge potential for the Banking and Financial sectors. Data Science has a strong presence in different aspects of banking such as risk management, customer outreach, pricing, marketing outreach, product development, and cost and revenue allocation. Data Science is providing smarter ways for the banking industry to manage the different challenges it faces today. Nowadays banks are utilizing machine learning to power myriad operations.

Today, banks generate a large amount of internal data such as credit scoring, payments, customer accounts, assets, etc. Banks need to figure out the linkages of internal data with external data such as customer preferences, interest rates, and macroeconomic variables. Further, banks are generating this data at the lightning speed with the help of non-traditional or digital touch-points that include social media, IVR systems, internet, ATMs, mobile, and more. This exponential growth in the volume, velocity, and variety of data is making it compulsory for banks to utilize the advanced analytics to derive useful insights from the huge and complicated sources to make real-time decisions to on-top of the competition.

The following are some areas where AI and ML are useful with the power of Data Science:

Customer Support

Data Science provides banks with access to important customer data, investment patterns and cycles that enable them to create a robust customer support framework. Further, it is helpful to analyze the plans that customers have and the credits they don’t have. Therefore, it helps banks to provide customers with the best offers that are suitable for them.

Fraud Detection

Machine Learning plays an important role in the successful detection and prediction of fraudulent activities that involve frauds related to insurance, accounting, credit cards, etc. Detection of fraud is essential to provide security to customers and employees.

Customer Data Management

Banks are forced to collect, examine, and store a large volume of data. However, instead of doing it as a compliance exercise, Data Science and AI technologies open the possibilities for banks to get familiar with customers and drive new revenue opportunities.

Risk Modeling

Risk Modeling is very important for banks as it allows them to develop new strategies for assessing their performance. The most important aspect of Risk Modeling is credit risk modeling. Now banks are integrating Stress testing and using tools like R, Python, and SAS to check their financial health.

Customer Segmentation

Customer segmentation is to separate the groups of customers based on behavioral conduct or specific attribute such as age, region, income, etc. Data Science offers tons of strategies like decision trees, clustering, logistic regression, etc that enable us to learn custom lifetime value for every customer segment.

Operational Efficiency

Artificial Intelligence and Data Science can also help organizations in the prediction of demand by analyzing future events and recorded information with the help of propelled time-series analytics. This kind of insight can help businesses to assess volumes that teams need to handle in back-office function and call center traffic. By using these insights organizations would be able to make advanced asset planning, strategic resourcing, and so on.

Final Note

Technology is evolving every day and delivering quality solutions for businesses. The power of Data Science cannot be neglected and there is huge potential of this technology in the banking sector too. By utilizing the power of Data Science, banks can gain competitive advantage and create strategies and techniques based on the insights from data by involving it in the decision-making process.