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How Predictive Analytics Are Redefining Banking and Financial Services!



By 2030, the predictive analytics market is projected to reach $56.11 billion. The Asia-Pacific region is the highest adopter of predictive analytics and is expected to be a key driver of its growth, with a CAGR of 23.4% in the region from 2022 to 2030. This has the potential to completely transform banking operations.


Here is how predictive analytics is helping the complete financial ecosystem reinvent itself.


Customer-Centric Products and Services


AI-driven analytics uses disparate data to discover (often hidden) consumer patterns, satisfaction, and engagement levels across all touch points. These analytics help banks to better understand customer expectations and evaluate their services. Banks use machine learning (ML) techniques with these analyses to:

  • Personalised banking experiences that meet individual’s (high) expectations.

  • Create customer profiles and segment them to deliver directed promotional content and upsell products.

  • Offer an intuitive and personalised customer experience.

  • Make more informed decision about customer requirements without directly conversing with customers, thus leading to more conversions.


This produces customer satisfaction, builds trust, and facilitates long-term customer association with the bank, resulting in customer delight.


Improved Agility


The modern business and banking landscape is diverse and dynamic. The ability to process large quantities of data helps financial institutions to make timely, research-backed decisions. Improved accuracy and speed lead to preparedness for upcoming business expansion opportunities. Banks can analyse market demand and offer relevant products at reduced costs and improved operational efficiency.


Risk Minimisation


Financial institutions have to take risks to deal with the competitive landscape. Precise risk calculations are essential in this industry. Predictive analytics helps to identify the market and internal risks by discovering avoidable mistakes and thus avoiding highly risky investments, mitigate risks well in advance, and identify and fix loopholes before any catastrophic outcomes. Another area where risk analytics helps immensely is to automate most of the routine work thus unlocking executive bandwidth for more high-impact decisions.


Fraud Detection


The finance industry is a target for scammers and fraudsters. Real-time analytics identifies unusual customer behaviour using customer history and publicly available data from sources like social media and recruitment websites. Predictive analysis helps banks to recognise the need for and automatically initiate processes like instant blocking of transactions and larger-than-usual cash withdrawals. Analytics helps to detect cyber fraud by locating discrepancies at the individual level or on a larger scale, by monitoring spending patterns. Credit scoring is a fast way to determine the creditworthiness of a prospective borrower and promotes quick loan disbursals.

With fintech becoming the torchbearer of innovation, the banking industry is undergoing a digital redefinition to accommodate the requirements of digital-first customers. Here is where predictive analysis has come in, to accelerate and facilitate the digital transition.


Co-Authored by Yogesh Thakker & Parth Pandey

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