PERU: An Introduction to Insurance
Insurance Fraud and Artificial Intelligence
Insurance fraud committed by policyholders remains one of the most persistent and damaging threats to the sustainability of the insurance market. This conduct leads to economic consequences that disrupt the technical balance of the system and harm both insurers and the insured community, particularly in a model governed by the principle of mutuality.
Peruvian law, through Law No 29946 – the Insurance Contract Law (LCS), explicitly recognises that such practices negatively affect the contractual relationship. Accordingly, the law stipulates that fraudulent conduct by the insured results in the forfeiture of their right to indemnity. In the current context of increasing regulatory demands and rapid technological advancements, the legal analysis of such illicit behaviour becomes more critical than ever.
This contribution seeks to provide a legal perspective on how emerging trends, particularly the rise of artificial intelligence (AI), are influencing and, in some cases, exacerbating insurance fraud. Against this backdrop, a key question emerges: what will be the role of AI in detecting, preventing, and even resolving disputes between insurers and insured parties?
Article 73 of the LCS states that “the insured forfeits the right to indemnity if they act fraudulently, exaggerate damages, or use false means to prove the loss.” This provision clearly demonstrates that such fraudulent behaviour constitutes a breach of the principle of good faith that governs insurance contracts, as further reinforced by Article II of the LCS.
Under this framework, different types of conduct – if proven – may result in the loss of indemnity rights: direct fraud, damage exaggeration, or use of false evidence. For instance, during a claims process, an insured party may fabricate a non-existent loss, falsify facts, alter documentation submitted to the insurer or claims adjuster, or even co-ordinate false testimonies. These actions not only justify denial of coverage but may also lead to legal action for recovery of funds or criminal charges such as fraud or forgery.
Moreover, Article 77 of the LCS establishes that “the burden of proving the occurrence of the loss and the extent of damages rests with the insured, whereas the insurer must prove any exonerating circumstances.” This creates one of the main challenges for insurers: the evidentiary difficulty in proving fraud.
Insurance fraud has become increasingly complex and sophisticated, complicating timely detection. Documented cases include billing for medical treatments that were never performed, fabricated accidents, and staged losses using AI-generated images to depict non-existent material damage. There are also instances where insured parties intentionally damage their own vehicles, perform partial repairs, and later file fraudulent claims. Other schemes involve falsified death certificates or diagnoses, inflated service costs, and collusion with third parties to present false narratives during the claims adjustment process.
In this context, the early identification of fraud indicators is essential. This requires co-ordinated efforts from internal claims teams, specialised experts (depending on the type of damage), and thorough fact-gathering through interviews with insured parties, third parties, witnesses, and other sources. The objective is to cross-reference data and evidence to identify inconsistencies or anomalous patterns that may reveal fraudulent behaviour. It is important to remember that, under Article 91 of the LCS, when a loss is intentionally caused by the insured, the insurer is fully released from its payment obligation.
While AI can be misused to facilitate fraudulent claims, its responsible and strategic application in the insurance sector should aim precisely at the opposite: becoming an effective tool for fraud detection. Properly implemented, AI enables the identification of behavioural patterns by analysing large volumes of historical claims data, medical records, networks of service providers, and prior conduct of the insured, among other sources. This approach not only complements traditional verification mechanisms but often surpasses them in efficiency and accuracy. Its effective use can help insurers generate strong supporting evidence for denying coverage and maintaining a solid position in any potential legal dispute.
In this new landscape, the role of the lawyer becomes increasingly demanding. Legal professionals must be fully equipped to understand and utilise such technical information as part of their legal strategy. AI will not replace lawyers, but it will undoubtedly transform how cases are structured, evidence is assessed, and defences are prepared. Article 73 of the Insurance Contract Law will remain the core provision for addressing fraudulent conduct; however, its interpretation and enforcement must evolve to reflect a reality in which AI plays an integral role in claims assessment and legal analysis.
Ultimately, fraud committed by insured parties will continue to present a structural challenge for the insurance sector. Nonetheless, artificial intelligence can become a key ally in addressing it – provided, of course, that its use complies with existing legal frameworks and respects fundamental rights. As lawyers, mastering insurance law is no longer enough; it is now essential to understand and effectively manage the technological tools that influence both evidence and legal strategy in this field.
Although current regulations have not evolved at the same pace as advances in AI, it is imperative to develop contractual frameworks and specific clauses governing its use in insurance activities. This is especially relevant to prevent misuse of these tools by individuals seeking to manipulate evidence or fabricate fraudulent claims through automated systems. Addressing these risks through proactive regulation will be vital to balancing innovation with legal integrity.