AI in Financial Services

AI in Financial Services: A COO’s Practical Playbook for Real-World AI Adoption

5 min read

Over the past 18 months, in every COO conversation I’ve had in financial services, AI has moved from buzzword to boardroom priority. But in our highly regulated UK market, where the FCA and PRA keep a keen eye on risk and efficiency, it’s not enough to be optimistic about AI’s potential. You must be relentlessly pragmatic.

This playbook is designed for COOs in financial services who need to deploy AI in a way that delivers real, tangible outcomes while strengthening compliance and enhancing operational controls. Let’s take a hard look at how you can turn AI into a competitive advantage, using actual FSI credentials to guide you.

1) Start with What Moves the Business

AI isn’t a magic bullet; it’s a tool to drive outcomes that matter. In the world of retail banking, insurance, and asset management, the key levers are clear:

  • Risk & Compliance: Reduce false positives, streamline AML checks, and lower operational risk and improve fraud detection
  • Cost-to-Serve: Cut manual processing in high volume tasks, accelerate transaction processing, and boost efficiency.
  • Customer Experience: Shorten onboarding times, personalise interactions, and enhance service reliability.
  • Operational Resilience: Ensure that every AI initiative can withstand both internal, external disruptions and regulatory scrutiny.

For example, one of our clients, a leading UK retail bank, used AI to drastically improve its client on-boarding compliance process. By harnessing machine learning for intelligent AML alert triage, they reduced false positives by over 30%, freeing up valuable compliance resources and boosting trust with regulators.

2) Leverage Your Existing Technology Assets

Most organisations in FSI already have powerful platforms like SAP, Oracle, or Microsoft Dynamics. The potential for AI is right there, often underused. Before adding new expenditure, ask: what can we unlock from our current investments?

Consider these initiatives at some of our current clients:

  • AI-Powered Compliance at a Major Retail Bank: 
    A top-tier retail bank integrated AI-driven data extraction and pattern recognition into its existing system, automating the complex compliance reporting process. This solution streamlined the extraction of unstructured data from multiple channels, boosting accuracy and cutting turnaround times dramatically.
  • Claims Processing Transformation at a Leading Insurer: 
    In the competitive insurance space, one insurer adopted AI to revolutionise claims intake and adjudication. By automating document capture and initial assessments, the company not only reduced cycle times by 25% but also enhanced the accuracy of claim evaluations, which in turn lowered costs and improved customer satisfaction.
  • Digital Onboarding in Challenger Banking:
    A forward-thinking challenger bank reimagined its onboarding process through AI automation. Leveraging embedded AI within Microsoft Dynamics, it reduced onboarding time by using facial recognition and dynamic data verification techniques. The result was a smoother, faster, and more secure customer experience.

Translate these proven patterns for your bank, insurer, or asset manager:

  • SAP: Use Document Information Extraction and SAP Analytics Cloud to transform compliance data workflows.
  • Oracle: Activate intelligent document recognition to streamline AP/AR processes.
  • Microsoft: Deploy Dynamics 365’s AI features to accelerate digital onboarding and transaction monitoring.

If it’s part of your current licence and roadmap, make it work first.

3) Build “Safe by Design” Controls

In financial services, regulatory expectations aren’t negotiable. Speed must be matched with rigorous control. A “safe by design” framework is essential to ensure your AI adoption delivers benefits without triggering compliance headaches.

Key steps include:

  • Define and Enforce Policies:
    Establish exactly which AI applications are permitted. Maintain a live register of AI models—including vendor-supplied solutions—and integrate them with your existing governance systems.
  • Data Governance and Lineage:
    Ensure that every data element used by AI models is fully traceable, from source to final decision, and compliant with PII and UK/EU residency standards.
  • Model Risk Management:
    Implement performance thresholds and bias testing, with robust monitoring protocols. Ensure that rollback procedures are in place should an AI application underperform or trigger unexpected risks.
  • Human-in-the-Loop:
    Define clear roles and responsibilities for human oversight. Regular quality assurance, sampling, and audit trails make regulators and investors more comfortable with innovative solutions.

Good control isn’t a barrier; it accelerates you to market by giving regulators confidence that you know what you’re doing.

4) Deploy a 100 Day Roadmap

Quick wins build momentum. A focused 100 day plan can turn strategic vision into operational reality.

Days 0–30: Diagnose and Design

  • Align AI initiatives to your most pressing levers: risk, cost, or customer impact.
  • Catalogue the AI capabilities already embedded in your systems.
  • Choose two pilot projects, one focused on reducing manual effort, the other on improving a control process.
  • Set baseline KPIs such as false positive rates, processing times, or compliance turnaround.

Days 31–60: Deliver and Validate

  • Configure your chosen pilots, leveraging existing platform features (like AI-powered AML triage or claims automation).
  • Engage teams through short, targeted training sessions and enablement workshops.
  • Monitor progress using a simple weekly dashboard and gather qualitative feedback.

Days 61–100: Scale and Institutionalise

  • Roll out successful pilots across more business units.
  • Convert improvements into run rate benefits and secure funding for further rollouts.
  • Establish a dedicated AI enablement team, a product owner, data/ML specialist, and change lead, to drive future projects and continuous improvement.

By day 100, you should see measurable impacts: reduced manual effort in key processes, fewer compliance alerts, and an embedded culture that trusts AI to enhance productivity without sacrificing control.

Final Thoughts

AI in financial services isn’t about chasing trends, it’s about pragmatic, disciplined execution. Use what your current systems offer. Align every initiative with core business levers. Build a robust control framework that satisfies regulators and reassures investors.

As COO, your ability to turn potential into performance defines your leadership. Which process will you transform with AI tomorrow? The answers lie in the capabilities you already own—ready to be activated for real, measurable impact.

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