Question 1

What is explainable AI in fraud detection?

Accepted Answer

Explainable AI in fraud detection refers to systems that show the reasoning behind a decision, not just the outcome. Rather than returning a risk score alone, an explainable system surfaces the specific signals, observations, and logic that led to a conclusion. This makes decisions auditable, helps analysts learn from the system, and supports compliance documentation requirements.

Question 2

Why does AI explainability matter for financial institutions?

Accepted Answer

Financial institutions make high-stakes, high-consequence decisions that must be defensible to regulators, auditors, and in some cases the applicants themselves. When AI flags a document as fraudulent or recommends rejecting an application, risk teams need to document why. A black box system that only returns a score creates a compliance gap and erodes analyst trust in the tool over time.

Question 3

What is a black box AI system?

Accepted Answer

A black box AI system is one where the internal reasoning is not visible to the user. The system accepts inputs, processes them using models or rules that aren't exposed, and returns an output, typically a score or decision, without showing its work. In fraud detection, this means analysts can't verify whether a flag is accurate, can't learn from the system's findings, and can't produce documentation explaining the decision.

Question 4

How is non-determinism in LLMs handled in fraud detection?

Accepted Answer

Large language models have a temperature parameter that controls how variable their outputs are. For fraud detection, this is typically set to zero, which means the system is configured for maximum consistency — given the same inputs, it is more likely to produce similar conclusions. Some variance at the infrastructure level is unavoidable with any large language model, but the effect is minimal and the reasoning remains logically stable across runs.

It's also worth separating this from a related but distinct point: an LLM's ability to generalize is a feature, not a liability. A reasoning model that doesn't simply pattern-match on previously seen cases is better equipped to catch new and evolving fraud types — and that capability comes from how the model was trained to reason, not from temperature. You can have both consistency at inference time and strong generalization. The two aren't in tension.

Question 5

What questions should I ask an AI fraud detection vendor about explainability?

Accepted Answer

Start with four: Is there a human in the loop, or is the system making fully automated decisions? Can it produce audit-ready documentation for every decision? Is the reasoning surfaced proactively in the workflow, or only available if you ask for it? And what happens when the system is wrong? Can analysts follow the logic to identify where it broke down? Vendors who can answer these clearly are worth a closer look.

Question 6

How does Inscribe approach explainability?

Accepted Answer

Inscribe's AI Agents are built to show their work. When analyzing a document, the system surfaces the specific signals it identified, the severity of each, and a plain-language summary of its reasoning. The Assistant lets analysts ask follow-up questions conversationally, getting answers grounded in their specific data rather than generic guidance. Every output is designed to be audit-ready and defensible.

Question 7

What is document fraud in the age of generative AI?

Accepted Answer

Document fraud today includes AI-generated and AI-altered documents such as deepfake paystubs, bank statements, invoices, and utility bills. Generative AI allows fraudsters to create highly realistic financial documents at scale, making it increasingly difficult to rely on visual checks or traditional verification methods.

Question 8

Why are document deepfakes such a serious risk for financial institutions?

Accepted Answer

Document deepfakes are dangerous because they are inexpensive to produce, easy to scale, and hard to detect with legacy systems. Fraudsters can generate thousands of realistic documents in minutes, overwhelming manual review teams and slipping through systems designed for a lower-volume, pre-AI fraud landscape.

Question 9

How is AI changing document fraud?

Accepted Answer

AI has shifted document fraud from simple manipulation to fully synthetic creation. Modern tools can replicate formatting, logos, transaction histories, and even natural errors. This means fraud detection can no longer rely on whether a document “looks real,” but must determine whether it can prove authenticity through deeper analysis.

Question 10

Why did Inscribe start focusing on document fraud?

Accepted Answer

Inscribe was founded after firsthand experience with the limitations of manual document verification. The founders saw risk teams overwhelmed by reviews and customers frustrated by slow decisions. From the beginning, Inscribe focused on building AI systems that reason like expert fraud analysts — but operate at machine speed and scale.

Question 11

Why aren’t traditional fraud detection systems enough anymore?

Accepted Answer

Most traditional fraud tools are linear and rules-based, designed to catch known patterns. These systems struggle when fraud tactics evolve or appear in new forms. In a world of generative AI, fraud is dynamic, creative, and unpredictable — requiring systems that can reason, adapt, and investigate rather than just classify.

Question 12

What are agentic AI systems in fraud detection?

Accepted Answer

Agentic AI systems are AI-driven agents that can plan investigations, choose tools, validate evidence, and adapt as new information emerges. Instead of running a single detector, an AI Agent can orchestrate multiple analyses across documents, data sources, and signals to reach a reasoned conclusion.

Question 13

How are agentic systems different from using LLMs alone?

Accepted Answer

Large language models improve document understanding and explanations, but on their own they are still limited by linear workflows. Agentic systems use LLMs to orchestrate actions — running multiple tools in parallel, validating results, and adjusting strategy mid-investigation — enabling deeper and more adaptive fraud detection.

Question 14

Why is agentic AI critical right now?

Accepted Answer

Deepfake document fraud is already routine, not theoretical. Financial institutions need systems that can catch rare fraud, entirely new fraud patterns, and AI-generated forgeries — without increasing operational burden. Agentic AI closes the gap between attack and response by enabling proactive, adaptive defense.

Question 15

What kinds of fraud can agentic systems detect that others miss?

Accepted Answer

Agentic systems can detect:

- Rare fraud that was previously too manual to justify investigating
- New fraud patterns not present in historical training data
- Complex cases involving multiple documents and subtle inconsistencies

This allows teams to defend against the “long tail” of fraud that traditional systems overlook.

Question 16

What results has Inscribe seen with agentic AI?

Accepted Answer

Since launching AI Agents, Inscribe customers have:

- Prevented millions in fraud losses in under a year
- Reduced manual investigation time from ~30 minutes to under 90 seconds
- Detected AI-generated paystubs, synthetic bank statements, and other deepfake documents

These results show that agentic systems can deliver both stronger protection and greater efficiency.

Question 17

What is Inscribe’s long-term vision for fraud detection?

Accepted Answer

Inscribe’s vision is to move the industry from static, rules-based tools to adaptive, agentic fraud defense. The future of fraud detection is systems that can reason across documents, tools, and data sources — and evolve as fraud evolves.

Question 18

Who should consider agentic document fraud detection?

Accepted Answer

Banks, lenders, credit unions, and fintechs that rely on customer-submitted documents — especially during onboarding and underwriting — should consider agentic document fraud detection. This is especially critical for organizations facing rising fraud volume without the ability to scale manual reviews.

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