Manual software testing has been steadily giving way to automation for years. Data testing, however, has remained one of the industry’s most labour-intensive disciplines.
Despite huge advances in test automation, financial institutions still rely heavily on manually written scripts, bespoke validation rules and painstaking reconciliation exercises to ensure the data feeding regulatory reports, risk models, payment systems and customer applications is accurate.
As banks modernise data platforms and embrace AI-driven software development, that approach is becoming increasingly difficult to sustain.
The volume of data moving through modern financial institutions has exploded. Cloud migrations, real-time analytics, AI models and increasingly complex data pipelines mean quality teams are being asked to validate more data, more frequently and with greater regulatory scrutiny than ever before.
That is why AI agents are attracting so much attention.
Unlike first-generation AI assistants that simply help engineers write code or generate test cases, agentic AI promises something more ambitious: software capable of carrying out testing tasks with a degree of autonomy.
From profiling data and generating validation rules to maintaining test suites, triaging failures and expanding test coverage, AI agents have the potential to remove much of the repetitive work that continues to consume data testing teams.
The obvious question for financial services is whether the technology is ready. Banks cannot afford to experiment blindly when poor data quality can lead to inaccurate regulatory reporting, flawed risk calculations or customer-facing errors.
Any move towards autonomous testing must also satisfy governance, auditability and explainability requirements that are becoming more demanding under regulatory frameworks such as DORA and other operational resilience initiatives.
Those practical questions will be the focus of QA Financial’s next webinar, Using AI Agents for Data Testing: How financial firms can switch from manual testing… today, presented in partnership with iceDQ, a specialist in data testing, monitoring and observability platforms.
The session will be led by Sumit Mudliar (below), who will cut through much of the current hype surrounding AI agents to focus on what financial institutions can deploy today.
Rather than presenting futuristic concepts, the webinar will explore where AI agents are already delivering measurable value across the data testing lifecycle, where human expertise remains essential, and how firms can introduce agent-driven testing without weakening governance or regulatory controls.
So what to expect on September 3? Among the topics covered will be why traditional, script-heavy approaches are struggling to keep pace with modern data platforms; which testing activities AI agents are genuinely suited to today; what it means to use agents continuously rather than as occasional assistants; and how firms can ensure agent-driven testing remains transparent, auditable and fully under control.
A good reason to attend is because the discussion will reflect a broader industry shift. AI is no longer simply changing how software is written. It is beginning to change how software, and increasingly, the data underpinning it, is tested.
As development accelerates and financial institutions face growing pressure to deliver trusted data at scale, the ability to automate repetitive testing while maintaining confidence in governance could become a significant competitive advantage.
Whether AI agents are ready to fulfil that promise remains an open question. What is becoming increasingly clear, however, is that data testing is emerging as one of the most compelling real-world applications for agentic AI in financial services.
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Looking for more news on regulations and compliance requirements driving developments in software quality engineering at financial firms? Visit our dedicated Regulation & Compliance page here.
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