Predictive Analytics for Customer Segmentation in Banking

Banking is now more convenient than ever thanks to the practically widespread use of mobile devices. According to Ron Shevlin, a banking expert, "As of May 2021, mobile banking penetration has grown to 95% of Gen Zers, 91% of Millennials, 85% of Gen Xers, 60% of Baby Boomers, and 27% of Seniors." We can access our accounts from anywhere, send money via the bank or credit union app, and deposit checks with a single quick click. Most clients now consider mobile banking convenience to be standard. 

In situations like these Bankers use Predictive Analytics in banking. Predictive analytics is a tool that financial organisations can employ to comprehend customer behaviour, keep existing customers, and draw in new clients. Discussed below are the use of Predictive Analytics in banking and advice for creating and using predictive models in retail banking.

What is Predictive Analytics

Predictive Analytics in retail banking refers to the application of computer models that make use of Data Mining and Artificial Intelligence to analyse vast volumes of information and forecast future client behaviour. Predictive Analytics can benefit banks by giving them deep insights into customer preferences, launching innovative goods and services, providing highly customised and exceptional experiences, inspiring the development of new business models, and transforming the industry by utilising new procedures and technology. 

Besides these, Predictive analytics also has the following advantages:

• Deep understanding of customer needs: Better customer insights allow lenders to target their clients more successfully with pertinent and considerate services at the right time.
• Launch new products and services: Redesign them more successfully using customer research, segmentation, and analysis, as well as improved portfolio strategies and pricing.
• Giving Personalized Customers outstanding experiences increased convenience and consumer engagement by delivering a synchronised, convenient, and consistent experience across all devices and interaction channels.
• Innovation and disruption of business models: Quicker adoption of new business models, digital products, pricing, and packaging to satisfy client expectations.
• Process/technology transformation: Enhancing the efficiency and effectiveness of non-customer-facing processes as well as the introduction of new goods and services that offer superior user experiences.

Top Impacting Factors of Predictive Analytics

• Increase in Use of Advanced Technologies for Fraud Detection: In recent years, there has been a noticeable increase in fraudulent activity in banking and financial institutions. Financial frauds including money laundering, payment card fraud, and fraudulent loans have increased as a result of people using financial services through a variety of channels. Advanced technology, such as predictive analytics and fraud detection tools based on machine learning algorithms, can, nevertheless, diminish such fraudulent actions. Banks can detect online fraud with the aid of machine learning-based fraud detection, which also provides swift action suggestions to decision-makers. Predictive analytics-based fraud detection software is now being used by a number of significant banks to find fraudulent actions occurring across several payment processing channels. Additionally, these organisations use mobile apps with predictive analytics for remote ordering.
• Surge in the Number of Risk Management Roles: For the longest time, risk management has been one of the most difficult tasks faced by banking companies. Any mistakes made by these organisations in managing risks could have a negative impact on an organization's profitability. These multinational financial organisations have intensified their efforts to manage a variety of risks, including operational risk, credit risk, operational risk, and client risk. Predictive analytics can use the massive amount of data that the banking sector produces every day to construct a variety of risk functions, including internal audits, stress testing, bank collapse prediction, and operational and liquidity issues. Additionally, the use of predictive analytics by banks helps identify high-risk accounts early on, which lowers the incidence of fraud and default.
• Competitive Analytics: Alteryx Inc., Fair Isaac Corporation, IBM Corporation, Microsoft Corporation, Oracle Corporation, SAP SE and TIBCO Software Inc. are the major firms highlighted in the predictive analytics in banking sector analysis. To grow their market penetration and solidify their positions in the business, these main companies have implemented a variety of measures, including product portfolio expansion, mergers & acquisitions, partnerships, geographic expansion, and collaborations.