Financial services firms around the globe are using and building AI.
KMPG insights from late 2024 show that 62% of US companies are using AI to a moderate or large degree, 58% are piloting or deploying generative AI and 52% are using AI specifically in financial reporting.
In the UK, a Bank of England and Financial Conduct Authority 2024 survey of AI and machine learning in financial services found that 75% of firms are already using AI, with a further 10% planning to use it over the next three years.
In Australia, KPMG reports that 76% of companies are using or testing AI in finance, outpacing global averages (which it states stand at around 72%)
Whether your firm has raced ahead with AI or is still strategizing, sense-check how ready your financial services APIs actually are with our checklist of AI-readiness factors.
How AI-ready are your financial services APIs?
To be ready for AI in financial services, you need to get your APIs in order. Assess your current position using these seven AI-readiness factors.
1. Review your API security
Your API security is the foundation for your AI security, so undertake a thorough review of your authentication and authorization, data encryption, input validation, rate limiting, logging, monitoring, and other security and compliance factors. By ensuring everything is in order at the API level, you will be in the best possible position to implement AI with minimal risk of data leaks and security breaches.
2. Understand the extent of your observability
Full visibility of your API ecosystem is crucial not only for security and compliance but also for fast, effective troubleshooting. If there are any limitations to your API observability, it’s important to understand and address these before you add AI into the mix. You’ll need full visibility into AI decision-making for both quality assurance and compliance reasons, so adding it on top of an already opaque API ecosystem is far from ideal.
3. Assess data quality and consistency
If you want your AI model to thrive, you’ll need to ensure that the financial services APIs that feed it are part of a data strategy that delivers clean, structured, and well-labeled data. The accuracy, timeliness and standardization of your data will have a major impact on the quality of your AI outputs. And whether you’re using that data to feed AI-driven credit scoring, fraud detection, customer analytics, or anything else, its quality is paramount. If your data schemas, APIs and AI are misaligned or subject to inconsistencies, you will undermine the performance of your model and increase your risk exposure significantly.
4. Standardize API documentation and versioning
Standardization and consistency in your documentation and approach to versioning are critical to enabling AI systems to consumer and integrate with your APIs smoothly and reliably. Your documentation will need to be both up-to-date and machine readable, with robust version control processes in place. This will enable your AI systems to interpret the behavior and data structures that underpin your APIs, with no room for ambiguity. It will also ensure that your developers can do the same, delivering a win for your human teams as well as your AI-readiness.
5. Evaluate data access and governance controls
If you’re planning to give AI systems, including AI agents, access to sensitive or regulated financial data, you’ll need to look at the granularity of your data access controls. This means ensuring your APIs are equipped with fine-grained access controls, as well as audit trails and consent management mechanisms. All of which must align with relevant regulations such as GDPR, CCPA, and open banking standards.
6. Enable real-time and streaming capabilities
Real-time AI decision-making needs to be underpinned by real-time API capabilities. After all, if your APIs suffer from latency issues or can only serve batch data, how will you support real-time AI-powered use cases, such as fraud detection or instant loan approvals? If this is what you need, consider event-driven and streaming architectures as part of your AI-readiness preparations.
7. Build in extensibility for future AI integration
Being ready for AI in financial services doesn’t just mean being ready for the AI of today; AI is evolving so rapidly that your API architecture will need to support future expansion as well.
Open standards are an excellent way forward here, maximizing your potential for seamless future integrations. You can also embed inference results into workflows, expose model outputs through endpoints, and integrate with external AI platforms to increase your future flexibility.
Avoiding hardcoded logic and rigid schemas is also a good idea, as these can make your AI integration brittle and future updates costly.
Hungry for more? Dive deeper into how you can increase your AI readiness and get more from your AI integrations with our comprehensive ebook: AI, APIs and AI readiness: The strategic blueprint for enterprise.
