Systems | Development | Analytics | API | Testing

From Manual Mayhem to Automated Assurance: How Test Automation is Revolutionising Core Banking!

Gone are the days when core banking teams relied solely on long-winded manual test cycles, midnight war rooms and crossed fingers before a go-live. Today, the industry stands at the edge of a seismic shift, driven by the power of test automation. Having worked extensively in the complex and highly regulated world of core banking systems, we’ve seen this transformation unfold firsthand.

11 Financial Model Examples & Templates for 2025

Enterprises actively use financial modeling to guide their financial planning and strategic decision-making. Financial models offer data-driven, quantitative analysis that tells you where your company stands and where it’s heading. That being said, one model can’t do it all. As a finance professional, you’ll need different types of financial analysis and modeling for different situations.

Why Software Testing in Financial Services is More Critical (and Complex) than Ever

For QA professionals in financial services, the pressure is unlike any other industry. Whether you’re supporting a multinational bank or a regional credit union, one thing remains the same: even the smallest software error can have massive consequences. A glitch in an online payment system could affect thousands of customers. A regression bug in account processing might trigger a compliance breach.

Key Takeaways from Accelerate: How Financial Services and Manufacturing Companies Leverage Data and AI for Measurable ROI

For many organizations across industries, the era of experimental AI has given way to the era of practical implementation. Even those companies still testing and evaluating AI solutions are shifting away from the art of the possible to focus more closely on what will soon produce measurable ROI. “It will no longer be enough for your organization to merely use AI to win the approval of company leadership,” says Samuel Lee, Product Marketing Director for Financial Services at Snowflake.

Banking Application Testing: Cost, Process & Timeframe

Banking applications are the backbone of the modern financial system. They manage billions of transactions daily, secure sensitive user data, and ensure seamless financial operations across the globe. Just as a high-performance car requires rigorous testing before hitting the road to avoid accidents and breakdowns, banking applications must undergo extensive testing to prevent financial losses, security breaches, and operational failures.

FinTech Application Testing: Cost, Process & Timeframe

Imagine you’re building a high-speed train—one that needs to get passengers (or in this case, financial transactions) from point A to point B in record time, with absolute precision, and without a single glitch. Now, would you let that train run at full speed without rigorous safety checks? Not. The same logic applies to financial technology (FinTech) applications, where even a minor bug can result in security breaches, compliance failures, or financial losses running into millions.

The FinOps Advantage: Aligning Data Teams with Financial Goals in Databricks Environments

Aligning technical operations with financial objectives is crucial yet challenging. Join us for this session in our Weekly Walkthrough drop-in series, "Controlling Cloud Costs," where we'll explore how to unite data teams and finance for Databricks success. You will gain the knowledge and tools to create a FinOps framework that aligns your Databricks operations with your organization's financial goals. With Unravel's Data Actionability Platform, you can see deep insights into your cloud spending and make informed decisions that balance innovation with cost-efficiency.

Agentic AI in Financial Services and Insurance

Many financial services companies are experimenting with AI through pilot programs, but several challenges remain for adoption. Key concerns include data security, the accuracy of large language models (LLMs) and the rigorous scrutiny from regulators regarding AI’s role in financial decision-making. Current use cases are largely internal, with some customer-facing chatbot solutions addressing noncritical service inquiries.

Best 13 Free Financial Datasets for Machine Learning [Updated]

Financial services companies are leveraging data and machine learning to mitigate risks like fraud and cyber threats and to provide a modern customer experience. By following these measures, they are able to comply with regulations, optimize their trading and answer their customers’ needs. In today’s competitive digital world, these changes are essential for ensuring their relevance and efficiency.