Digital Witnesses: How Anti-Fraud Logs Are Transforming into Irrefutable Evidence and Changing the Principles of Court Proof.

[Professor]

Legal traps: how evidence from anti-fraud systems is used in court.​

Evidence automatically generated by modern anti-fraud systems (Riskified, Forter, Kount, and retailers' internal systems) is more than just "logs." It's timed, contextualized, and interconnected digital narratives that act as "silent witnesses" with impeccable memory in court. Their use undermines traditional defenses built on denial and ignorance. By 2026, such evidence will have become the de facto standard in cyberfraud cases.

What Exactly Ends Up in Court: The "Accounting" of Digital Crime​

The anti-fraud system records not only the purchase, but the entire user session as a single, timestamped file. It includes:

1. Technical session metadata (non-contestable basis):
   • A complete digital fingerprint of the device: User Agent, screen resolution, installed fonts, plugins, WebGL/Canvas settings, and a list of installed typefaces. This proves the uniqueness of the device.
   • IP address, proxy, VPN data: Including geolocation and provider. If a residential proxy was used, its identifier.
   • Precise timestamps down to the millisecond for every action: visiting a website, adding to a cart, entering each form field, clicking a button.
2. Behavior and interaction data (digital twin profile):
   • Input speed and patterns: Time between keystrokes, copy-paste usage (which is recorded as immediate field completion), mouse movements (recorded via JavaScript events).
   • Sequence of actions: A typical user logs into their account → goes to the catalog → selects a product. A fraudster might click directly on the product link, bypassing the catalog.
   • Normality analysis (scoring decision): Real-time system verdict: risk_score: 0.94, reason: "device fingerprint mismatch, robotic behavior, high-velocity card testing pattern".
3. Financial and logistics chains (connection of events):
   • Linking a card and payment method to this specific session.
   • All changes to the order: Re-routing attempts, address changes, support requests from the same session.
   • Connection to other incidents: The system automatically flags that the same device fingerprint, IP, or behavior pattern has been used in X other fraudulent orders on the same or other websites (data from collaborative fraud networks).

How this evidence undermines the classic defense in court​

The defense in carding cases was typically built on the line: "It wasn't me. Someone stole my data. My card was compromised." Anti-fraud logs render this position useless.

• Against the "Card Stolen" alibi: The system shows that the stolen card details were entered from a device previously used to log into your personal email or social media (by coincidence, fingerprint or cookies). Or that the order was placed from an IP address that was used to log into your Netflix account an hour earlier.
• Against the alibi "My device was infected/used without my knowledge": Behavior logs show consistent, meaningful actions (product selection, CVV entry, 3DS verification), not an automated script. The court and experts distinguish the actions of a bot from those of a human.
• Against the "I was just sharing an account with a friend" alibi: Device fingerprint analysis proves that the "friend" is the same person who committed other criminal acts from other accounts, simply using the same computer.
• Denial of intent: The speed and patterns (e.g., entering 10 different cards in 2 minutes with balance check) clearly indicate targeted fraudulent activity (card testing) rather than random user error.

Procedural Aspects: How "Logs" Become Evidence​

1. Evidence Securing (Forensics): Retailer or bank specialists extract logs from the anti-fraud system according to a strict protocol, ensuring the integrity of the chain of custody. An independent IT expert is often engaged.
2. Authentication of evidence:It's not enough to simply print the logs. You need to prove to the court that:
   • The system operates correctly and reliably (certificates, audits).
   • The data has not been modified (digital signature of logs, hashing is used).
   • Collection and storage methods comply with standards (e.g. GDPR, but with caveats for investigations).
3. Presentation in a clear way: Visual diagrams (timelines), graphs and session dumps are created that explain the sequence of technically complex events to the jury or judge.
4. Expert's Conclusion: A computer forensic expert provides an opinion in which he or she interprets the technical data, explaining why the combination of parameters (fingerprint + behavior + timestamps) uniquely points to a specific person or device.

Weaknesses and counterarguments of the defense (what else can be contested)​

A skilled defense can attack such evidence along the following lines:

1. The "dark nature" of algorithms: Demand disclosure of trade secrets — the internal logic of the scoring model. Retailers will resist with all their might, but the court may side with the defense if the algorithm operates as a "black box."
2. Probabilistic: Claiming that scoring is merely a probability, not a fact. "95% risk" is not "100% fault."
3. Device spoofing/compromise: Claim that the device fingerprint may have been spoofed or stolen by malware (though this is difficult to prove).
4. Data Errors: Find inconsistencies in metadata (e.g. server time discrepancies).

The Future: Predictive Evidence and Legal Implications​

An even more difficult challenge looms on the horizon: predictive evidence.

• The system not only recorded the fact but also predicted the intent to commit fraud several steps in advance. Could this be used in court?
• Profiling and the Presumption of Innocence: If a system labels a person as a "potential fraudster" based on their digital profile, does this violate their rights?

Result: The court of evidence versus the court of people​

Evidence from anti-fraud systems has quietly revolutionized criminal proceedings. It has shifted the focus from human testimony (which can lie or make mistakes) to that of a machine — an impassive recorder of thousands of parameters.

For law enforcement, this is the ideal witness : impervious to pressure, with a perfect memory. For the carder, it is a verdict, written at the moment of action. Every click, every entered character, every millisecond delay becomes a brick in the indictment.

The legal trap springs when technical data ceases to be mere "logs" and becomes a coherent, logical, and compelling narrative of a crime, where the defendant is the protagonist and their device the co-author. In this new reality, the best defense is not denial, but the understanding that every action in the digital space is potentially eternal and can be reproduced in the courtroom with inexorable precision. Carding has become not only a technological crime but also an evidentiary one, with evidence generated automatically in real time by the system being attacked. The game has changed forever.