Insurance automation is moving forward rapidly.
Across claims, underwriting, fraud, and customer experience, carriers are continuing to invest in digital intake, straight-through processing, AI-assisted decisioning, workflow automation, and faster settlements. The goal is clear: reduce manual review, improve cycle times, lower costs, and create a better experience for legitimate customers.
But for us, one question kept coming up in conversations around ITC Vegas:
What if the submitted files behind those automated decisions can’t be trusted?
Photos, documents, audio, and video now play a central role in many insurance workflows. They support claims decisions, underwriting assessments, fraud triage, vendor review, payment approval, and escalation. As more of those workflows become digital and automated, the quality and trustworthiness of the submitted files becomes even more important.
Automation depends on inputs. If the files feeding the workflow are unreliable, incomplete, altered, reused, or inconsistent, then faster processing can also mean faster leakage.
Deepfakes Are Bringing the Issue to a Tipping Point
Deepfakes are clearly forcing the conversation.
AI-generated images, synthetic audio, manipulated videos, and increasingly realistic fake documents are no longer abstract risks. The technology is becoming easier to access, harder to detect visually, and more likely to appear in real business workflows.
For insurers, that matters because claims and underwriting decisions often depend on visual or documentary evidence. A photo of vehicle damage, a property inspection image, a repair invoice, a medical or financial document, a recorded statement, or a supporting video can all influence what happens next.
The old approach of looking for obvious defects is no longer enough. Extra fingers, distorted faces, warped backgrounds, strange shadows, and obvious image artifacts may still appear in some AI-generated content, but they are not a reliable defense. As generation tools improve, suspicious files may look normal to the human eye.
That creates a new challenge for insurers: the issue is not only whether something looks fake. The issue is whether a submitted file can be trusted before it influences a business decision.
Deepfakes have brought the industry to the tipping point, but they are not the only problem.
The Risk Is Broader Than Deepfakes
Deepfakes may be the most visible threat, but claim leakage and decision risk can come from many types of submitted file issues.
A claim may include reused photos from a prior incident. An invoice may have altered totals, duplicate line items, unsupported charges, or missing information. A document may be incomplete or inconsistent with the claim details. Metadata may not match the reported timeline. A photo may not match the location, loss description, or vehicle involved. Audio or video may be synthetic, edited, or inconsistent with other submitted materials.
Some of these issues are fraudulent. Some are errors. Some are omissions. Some are inconsistencies that require clarification before a decision can be made.
But operationally, they create the same challenge: the insurer needs to know whether the submitted file supports moving forward, requires more information, or should be escalated for review.
That is why deepfake detection alone is not enough.
Detection can provide an important signal, but insurers need a broader validation workflow that evaluates submitted files in context.
File Validation Becomes the Control Layer
As insurance workflows become more automated, file validation becomes a critical control layer.
Submitted file validation asks a broader question than “Is this a deepfake?”
It asks:
Can this file be trusted for the decision it is meant to support?
That requires more than a single score. It requires evaluating multiple signals, including AI generation indicators, forensic anomalies, metadata, duplicate and reuse patterns, document integrity, missing information, business rules, and consistency with claim, policy, vendor, customer, or workflow context.
For example, a submitted claim photo may not appear AI-generated, but it may have been reused from another claim. A document may not be synthetic, but it may contain inconsistent totals or unsupported costs. An audio file may not show obvious signs of manipulation, but it may not match the surrounding claim context. A file may be authentic in isolation but still insufficient to support the decision being requested.
This is where validation becomes different from detection.
Detection identifies suspicious signals.
Validation helps determine what should happen next.
From Deepfake Detection to Submitted File Validation
The insurance industry is right to pay attention to deepfakes. They are becoming more realistic, more accessible, and more relevant to digital insurance workflows.
But the bigger shift is this: insurers can no longer rely solely on human review, visual inspection, or narrow detection tools to evaluate the files behind critical decisions.
Claims and underwriting workflows now need a broader way to validate submitted photos, documents, audio, and video before those files influence outcomes.
Deepfakes may be forcing the issue, but file validation is the larger operational need.
As insurance automation continues to accelerate, the question every carrier will need to answer is simple:
Can you trust the files behind the decision?
