2025 is the year of AI Agents in financial services
The term "AI agent" has become increasingly popular, but it’s worth clarifying what that actually means.
An AI agent isn’t just a chatbot or a single-purpose script. It’s a system built around a large language model (LLM)—like Claude or GPT-4—that has been given specific objectives and the tools it needs to complete them.
At Inscribe, our agents are equipped with forensic detectors to analyze the signals embedded in documents, external databases to enrich its context, and even reasoning capabilities to detect semantic inconsistencies or anomalies—just like a human analyst, only much faster and more scalable.
Four use cases financial institutions are already deploying
We’re seeing real-world traction now across four major use cases:
- Document Categorization and Extraction: This sounds simple but takes an immense amount of time for human teams. Automating this step reduces onboarding friction and improves speed without compromising accuracy.
- AI-Powered Communication: Voice, email, chat—agents can now handle back-and-forth communications like requesting missing documents or explaining fraud risk education to consumers.
- Data Enrichment and Augmentation: AI agents pull in external data and format it for better decisions, consolidating fragmented sources into one cohesive view.
- Natural Language Search: Instead of filtering manually or querying a BI team, analysts can simply ask questions like “How many AI-generated documents did we receive last month?” and get answers in seconds.
Each of these unlocks not only efficiency, but also empowers teams to work smarter—not just harder.
Real-world AI Risk Agent examples
Take our X-Ray detector: it reveals hidden layers in PDFs, exposing common fraud techniques like overwriting real data with fake information. Another example is our AI-generated document detector, which catches synthetically created fakes that are becoming harder to detect by eye.
We’ve also added reasoning-based tools that can look across thousands of documents and identify risks at both the document and customer level. Our AI agents summarize findings, calculate risk scores, and even pre-generate fraud reports—giving analysts all the context they need with full transparency and explainability.
Talking to your Agent: Natural language search is here
One of the most exciting new capabilities is natural language search. Imagine a world where your fraud team can ask:
- “What document type triggered the most fraud alerts last quarter?”
- “Which customers have uploaded AI-generated docs in the past month?”
They type those questions into Inscribe and our agent automatically generates and executes SQL queries behind the scenes, then returns the answers in plain English. No more digging through spreadsheets or dashboards—just immediate, actionable insights.
And we’re not far off from voice-based interfaces either. Talking to your agent like you would to a colleague? That’s coming.
It’s augmentation, not replacement
There’s a misconception that AI agents are about replacing humans. That’s not the case—at least not today. Instead, they act as smart filters, preparing better data for your teams and enabling them to make faster, more confident decisions.
When used correctly, agents reduce errors, improve throughput, and help teams focus on high-value work. It’s about creating leverage, not layoffs.
Building trust through oversight
That said, trust is critical. We don’t just deploy an agent and walk away. At Inscribe, we have an internal risk ops team (shoutout to Darragh and Jessica!) that acts as the human oracle, constantly testing, validating, and calibrating our agents.
We also use structured evaluation frameworks (evals) and backtesting against historical data to ensure our systems don’t hallucinate, exaggerate, or introduce bias. We believe explainability, auditability, and human oversight must be baked in from day one.
The future is agentic (and we're just getting started)
2025 marks the beginning of a long and transformational journey. AI agents are already enhancing fraud detection, automating tedious workflows, and surfacing insights faster than ever before. But the real change will come as organizations move beyond experimentation and start scaling these tools across operations.
We're excited to lead the charge. If you’re a financial institution thinking about your AI roadmap, consider this your call to action. Start with one use case. Test. Learn. Scale. You don’t have to go all in right away, but you do need to start.
My co-founder Ronan and I would also like to make ourselves personally available to any fraud fighters or risk leaders want to learn more about AI Agents. We’re always happy to consult on AI strategy, answer any questions, share a preview of our product roadmap, or just get to know others in the risk space better.
Feel free to put time on our calendar here.
We’re only just scratching the surface of what that means for financial institutions.
