Banking Sector Leveraging Top Data Science Trends

List of top data science trends in the banking sector.

Financial institutions have started to apply data science in their process to enhance processes and user experience. Leveraging data science in the banking sector has become a trend. It has become a necessity to survive with the competition. Banks have already realized the importance of big data technology and how it can help them to focus on their resources efficiently and thus make smarter decisions and enhance performance.

The banking sector has been applying data science technologies to fasten operations and increase flexibility. Data science has played a significant role in boosting security, particularly through improved identity management.

Here is a list of data science trends in the banking sector.

Automation

Many vital insights and correlations across finance can be automated using the technology of machine learning. Automation allows investment professionals to inspect much larger data sets and new sources of alternative data for example social media, satellite imagery, credit card spending data, weather data and enables them to make informed investment decisions more quickly and construct new investment strategies.

Cloud computing

Cloud computing to be precise is the delivery of computing services (such as servers, networking, databases, analytics, intelligence, and storage) over the cloud (internet) to provide flexible resources, economies of scale, and faster innovation. Cloud computing helps in reducing operating costs, running infrastructure more effectively, etc. Thus, it allows banks and other investors to instantly access and store data and compute resources so that they can easily scale their operations on-demand. Cloud computing also enables the financial industry to break free from limitations while offering better service outcomes.

ESG data

The intensifying availability of ESG (Environmental, Social, and Governance) data is allowing investors to create strategies that focus on the societal influence of companies. It helps investors to better analyze and understand which companies are influenced or impacted. To be precise ESG data offers the ability to develop thematic investing strategies.

Customer Lifetime Value (CLV) Prediction

CLV refers to the predicted value of the net profit. It is a value that a business will achieve from a customer during their entire relationship. The banks leverage different predictive analytic approaches to predict the revenue that can be produced by any customer in the future. This helps the banks in separating the customers into specific groups based on their predicted future values.

Recommendation Engines

For any industry, offering those exact goods and services to the users which they want is always the key to success. Data science tools help industries to identify the most suitable items for the customers by scanning customer activities. The recommendation engines can be created by using two algorithms. The first one is the collaborative filtering method that can be either customer-centric or item-centric. The second one is the Content-Based Filtering algorithm, it suggests the most similar items to the user that are inspired by the products.

Other than the above-mentioned trends, alternative data has been utilized by the banks which helps in benefiting the investment processes within the investment banks. Alternative data serves banks with deeper, more timely insights that help in better business decisions.

The banking sector is increasingly drifting beyond traditional financial statements, company filings, and management information. One of the biggest trends in the banking industry is the rise of alternative data use in investment decision-making and ESG analysis.