From prompt to resilient governance: How GPT-5 is truly transforming banks
One detail from the GPT-5 release presentation stuck with me: you can simply write ‘think hard about this’ to the system, and it switches to a deeper thinking mode. No pressing buttons, no model changes, no complicated workarounds. A simple command that symbolises a major shift: AI no longer works only on command, it acts within clear rules. Prompts become specifications, experiments become robust governance.
From “AI playground” to resilient governance
In many banks, this is the reality to date: Use cases are collected, tools are tested, pilots are launched. Sometimes a prototype succeeds, sometimes it fails. The structure behind it often remains fragile. GPT-5 changes the playing field because it is not only faster or cheaper, but also more reliable. It plans, calls up external systems, intercepts errors and delivers a result – in a reproducible manner. This provides the technical basis for real governance.
Governance in this sense is more than just technology. It means specifying, supporting and controlling: Specifications on processes and limits of AI use, support through tools and standards, control over quality, costs and regulatory conformity. Only when these three elements work together does an AI pilot become a viable operational building block.
The game changer: Significantly fewer hallucinations
One of the biggest leaps of GPT-5 is not in what is visible, but in what is omitted: false facts. The hallucination rate is measurably lower than its predecessors – according to OpenAI, around 45% lower than GPT-4o and around 80% lower than o3 in thinking mode, as measured by anonymized, realistic prompts with web search. For banking operations, this is not a detail, but a paradigm shift: fewer correction loops, lower reputational risk and fewer operational risks due to wrong decisions. Combined with improved honesty – the model is more likely to admit when it doesn’t know something – the result is a quality basis on which to build regulatory-approved processes.
From use cases to working methods
We are not saying that process optimization and specific use cases are outdated. But they are only part of the truth. Nobody would still talk about “use cases for digitalization” today – digitalization is a way of working, not an individual case. AI must be understood in exactly the same way: as a new collaborator that is embedded in workflows and takes on responsibility there. For banks, this means moving away from a catalog of individual AI projects and towards control via clear AI specifications and a central platform that applies to entire process families. The decisive question is not “Which model do we use?”, but rather “How is it used, what rules does it follow and how do we safeguard it?”
Middle office as the first area of transformation
The greatest leverage lies in the middle office. This is where documents are checked, exceptions processed and regulatory reports created – activities with a high degree of coordination and many media disruptions. GPT-5 can plan these work steps, control suitable tools, catch errors and deliver the result in a verifiable form.
Specific examples from our practice:
- Digitizing appraisals: Real estate appraisals are automatically structured, analysed and converted into comparable data sets and decision bases – this significantly shortens the processing time and increases the comparability for credit decisions.
- Real estate appraisal validation: AI-based plausibility check identifies missing sections or unusual assumptions, which speeds up quality assurance and reduces the risk of incorrect valuations.
- Regulatory RAG: Answers to MaRisk, EBA guidelines or the EU AI Act are backed up with sources, versioned and documented in an auditable manner, making audits more efficient and reducing compliance costs.
These examples are not isolated use cases, but express a way of working that has a direct impact on business success: clear guidelines, technical support and effective control ensure faster processes, higher quality and measurable efficiency gains.
Workflow telemetry as a competitive advantage
What banks are building up in the process is a new treasure trove of data: workflow telemetry. Every step, every tool used, every correction becomes traceable. This is not an end in itself, but creates the basis for fact-based control and optimization. For banks, this means that process weaknesses can be clearly identified, regulatory requirements can be met in a targeted manner and collaboration between humans and AI can be continuously improved. In an environment in which the underlying models are interchangeable, this ability to precisely orchestrate and control becomes a real competitive advantage.
The three pillars for operation
At msg for banking, we rely on three pillars:
- AI Strategy: Definition of the governance, rules and SLAs under which AI works.
- AI Factory: Implementation of these guidelines in orchestrated, monitorable workflows.
- AI Academy: Enabling management and employees to understand and apply this new way of working.
This closes the circle: just as a simple command in the presentation triggered the switch to a deeper mode of thinking, GPT-5 marks the transition to a new way of working. It is not simply a better language model, but the first building block for AI that works in banking operations under clear rules, with verifiable results and reproducible quality. Those who now dare to take the step from individual projects to resilient governance are laying the foundation for AI not only to work, but to have a strategic impact – and are thus building a head start that cannot be overtaken by the next generation of models.
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