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Top Banking Analytics Trends Driving Digital Finance

Discover the top banking analytics trends unlocking quicker processes and better insights

For Banks data and analytics (D&A) trends demand large-scale culture shifts. As technological rollouts are taking other major industries by storm, the urgency for real-time data is intensifying.


Banks are expecting more from data, leaving just one requirement open-ended – Innovation!


New-age data collection and delivery use enterprise-wide data lakes and accelerate analytics processes with AI/ML-powered solutions. Because aggressive scaling up of architecture is the only way forward, a few trends that deal innovatively with this demand, have emerged.


Where data is second to none, staying up-to-date with trends helps envision the maximum benefits of system transformation. Since data teams play a major role in driving this, we have made a list of trends most Banks and Financial Entities should keep track of today.


Increased ROI from Machine Learning


Artificial intelligence (AI) and machine learning (ML) is showing no signs of slowing down. Frequently bringing new business use cases, the planned return on investment (ROI) still needs to be clearly explained as we progress into the new era of digital banking.


Research shows that by 2022, 90% of banks had explicitly mentioned AI as a core analytical competency, and investments have grown consequentially. Return on Investment (ROI) in Banks will be majorly expected from these areas

  1. Fraud Detection and Prevention

  2. Risk Management Data Analytics

  3. Customer Segmentation and Personalisation

  4. Enhanced Customer Service and Support

  5. Automated Compliance and Regulatory Reporting

  6. Operational Efficiency and Data Collaboration

Cloud Computing


Cloud access lets financial entities proactively store, access, and innovate with data, enabling easy scalability for future operations. Internal and third-party applications via the cloud provide them with potential cost savings and performance enhancements while enabling teams to generate insights and analytics in a more cost-effective manner.


Evolving Access to Real-time Data


Many key insights and correlations across finance can now be automated using machine learning (ML) and its counterpart artificial intelligence (AI). This enables credit monitoring teams to analyze much larger data sets (from hundreds of columns to millions of columns) and new sources of alternative data (e.g. social media, credit card spending data, and financials of top executives).


Environmental, Social, and Governance Data


The increasing availability of Environmental, Social, and Governance (ESG) data is enabling financial entities to develop comprehensive strategies for lending, credit monitoring, and collections. On a larger scale, the ability to build thematic strategies can be enhanced by the addition of ML and AI models, which help investors analyze data in real-time.


Keeping up with these trends will allow you to anticipate change and manage uncertainty. It also helps proactively monitor, experiment with, or then decide to aggressively invest in key trends based on their urgency and alignment with your strategic business priorities.


Stay ahead of the curve and ensure your teams are up to date with the latest trends in fintech solutions. Connect with our team of experienced banking fintech professionals today and gain valuable insights into the evolving landscape of financial technology.


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