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Optimizing Bank's Infrastructure Costs With AI-Powered MIPS Prediction

The Costly Chaos of Legacy Banking Infrastructure

A leading European bank with over 9 million customers faced the ongoing challenge of managing mainframe infrastructure costs. With billions of transactions processed daily —deposits, withdrawals, trades—, the bank's IT infrastructure relied heavily on mainframe computing power measured in MIPS (Millions of Instructions Per Second). Without predictive capabilities, the bank struggled to:

  • Anticipate resource demands during peak periods
  • Optimize infrastructure investments
  • Align technology spending with business value

The bank needed to move from reactive to proactive infrastructure management to control costs while maintaining customer service quality.

Our Approach: Transforming Banking with AI-Powered Infrastructure Solutions

SEIDOR Opentrends collaborated with the bank to design and implement an AI-driven solution leveraging Google Vertex AI with a K-Nearest Neighbors (KNN) model at its core. Our approach integrated the bank's existing MLOps framework with advanced AIOps capabilities. Here’s how it unfolded:

  • The Foundation: We tapped into their systems, pulling years of consumption data into automated pipelines. No more manual guesswork—just clean, real-time insights.
     
  • The Brain: Our data scientists crafted a KNN model that could spot patterns to forecast MIPS needs with uncanny accuracy.
     
  • The Engine: We embedded this into an MLOps framework that hummed like a well-oiled machine: Continuous Integration for flawless updates, Continuous Deployment for speed, and Continuous Training to keep the model sharp as demand evolved.
     
  • The Delivery: A sleek interface put these insights into the hands of their infrastructure team—turning raw data into decisions they could act on.

 

MLOps architecture diagram for a bank by opentrends us

 

Business Wins from Predictive Analytics in Financial Tech Services

The predictive MIPS consumption system delivered significant value through:

  • Cost Optimization: The bank gained the ability to accurately forecast infrastructure needs, eliminating overprovisioning while ensuring capacity for peak demands.
  • Data-Driven Decision Making: Infrastructure teams are transitioning from intuition-based to data-driven capacity planning.
  • Operational Efficiency: Automated MLOps practices reduced the time and effort required to maintain and update predictive models.
  • Business Alignment: IT resources were more effectively aligned with business priorities based on predictive analytics.

 

Why AI Matters for Banking and Financial Tech Success

Legacy mainframe systems today remain critical infrastructure for financial institutions. By applying modern AI techniques to traditional infrastructure, the bank transformed a cost center into a data-driven operation that contributes directly to the organization's financial health.

If you’re a C-level leader staring down rising IT costs or wrestling with legacy systems, this is for you. SEIDOR Opentrends helps enterprises transform their technology operations through data-driven solutions and AI innovation.

Contact us with the button below to learn how we can optimize your infrastructure costs through predictive analytics.

 

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