[Paper Review] Blockchain-based Immutable Evidence and Decentralized Loss Adjustment for Autonomous Vehicle Accidents in Insurance

Mehmet Parlak|arXiv (Cornell University)|Mar 29, 2023

Blockchain Technology Applications and Security4 citations

TL;DR

This paper proposes a blockchain-based decentralized application (dApp) that ensures the immutability and provenance of autonomous vehicle accident footage using cryptographic hashing and smart contracts, while decentralizing loss adjustment through an incentivized economic model. The system prevents deepfake fraud and enables transparent, tamper-proof claims processing by anchoring evidence on a distributed ledger and combining it with AI-driven anomaly detection.

ABSTRACT

In case of an accident between two autonomous vehicles equipped with emerging technologies, how do we apportion liability among the various players? A special liability regime has not even yet been established for damages that may arise due to the accidents of autonomous vehicles. Would the immutable, time-stamped sensor records of vehicles on distributed ledger help define the intertwined relations of liability subjects right through the accident? What if the synthetic media created through deepfake gets involved in the insurance claims? While integrating AI-powered anomaly or deepfake detection into automated insurance claims processing helps to prevent insurance fraud, it is only a matter of time before deepfake becomes nearly undetectable even to elaborate forensic tools. This paper proposes a blockchain-based insurtech decentralized application to check the authenticity and provenance of the accident footage and also to decentralize the loss-adjusting process through a hybrid of decentralized and centralized databases using smart contracts.

Motivation & Objective

• To address the lack of a clear liability framework for autonomous vehicle (AV) accidents involving multiple stakeholders.
• To counter the rising threat of AI-generated deepfake media in insurance claims, which can evade traditional forensic detection.
• To develop a tamper-proof system that verifies the authenticity and provenance of AV accident footage at the point of capture.
• To decentralize the loss-adjusting process using smart contracts and economic incentives, improving fairness and reducing manipulation.
• To integrate blockchain with AI-based anomaly detection for a hybrid fraud prevention model in automated insurance claims processing.

Proposed method

• Utilizes cryptographic hashing to generate unique digital fingerprints (hash values) for all claim-related digital media, ensuring data integrity.
• Deploys a permissioned blockchain (Ethereum-based) to store time-stamped, immutable records of accident footage hashes and metadata.
• Employs smart contracts to automate and enforce rules for evidence verification, claim submission, and loss adjustment workflows.
• Introduces an economic incentive model where participants are rewarded for honest reporting and penalized for fraudulent claims.
• Combines in-line prevention (blockchain at capture) with in-line detection (AI-based anomaly and deepfake detection) for multi-layered fraud protection.
• Uses a hybrid database architecture—part decentralized (blockchain) and part centralized (for performance)—to balance security and scalability.

Experimental results

Research questions

• RQ1 How can blockchain technology be leveraged to establish the authenticity and provenance of autonomous vehicle accident footage?
• RQ2 What role can smart contracts play in decentralizing and automating the insurance loss-adjusting process for AV accidents?
• RQ3 How can a hybrid system of in-line prevention (blockchain) and in-line detection (AI) mitigate the risk of undetectable deepfake fraud in insurance claims?
• RQ4 What economic incentives are needed to ensure honest participation in a decentralized loss-adjusting system?
• RQ5 How can the integration of blockchain and AI improve transparency, fairness, and efficiency in automated insurance claims processing for AVs?

Key findings

• The proposed dApp successfully anchors claim-related digital media on a blockchain, ensuring that any alteration to the footage results in a mismatched hash value, thus proving tampering.
• The system prevents deepfake fraud by securing the provenance of evidence at the point of capture using cryptographic hashing and blockchain immutability.
• The decentralized loss-adjusting model, governed by smart contracts and economic incentives, enhances objectivity and reduces the risk of manipulation in claims assessment.
• The hybrid approach of in-line prevention (blockchain) and in-line detection (AI) provides a robust, multi-layered defense against increasingly sophisticated deepfake attacks.
• The integration of blockchain with AI-based anomaly detection enables scalable, transparent, and fraud-resistant insurance claims processing for autonomous vehicles.
• The system supports a shift from personal liability to product and cyber liability models, aligning with the evolving AV ecosystem and the changing roles of OEMs, software providers, and insurers.