Digital identity fraud in Peru: over 8,000 complaints and new AI to stop crime
Between January and August 2026, over 8,000 complaints were filed for digital identity fraud in Peru. New AI-based technology seeks to detect fraud across different financial platforms.
Over 8,000 complaints of digital impersonation in Peru as fraud aided by AI grows
According to elperuano.pe, between January and August 2026, over 8,000 complaints were filed for digital identity fraud in the country, according to an analysis of open data from the Public Ministry. The records correspond to cases classified under article 9 of Law No. 30096, the Law on Computer Crimes.
The problem takes on greater dimensions in a context of rapid expansion of digital financial services. The Superintendence of Banking, Insurance and Private Pension Funds (SBS) reported that 67% of Peruvian adults had a digital account or wallet in 2025, compared to 42% recorded in 2020. A study presented by the SBS in March 2026 also revealed that 44% of those surveyed kept their passwords on their own devices, and that 27% conducted financial operations from public Wi-Fi networks.
Artificial intelligence as a fraud tool
One of the factors complicating fraud detection is the growing use of so-called artificial intelligence agents. These tools allow the creation of synthetic identities, document modification and repeated fraud attempts across different platforms in very short timeframes.
The structural problem lies in the fact that security systems at banks and other organisations operate independently. An attacker rejected by one institution can attempt fraud at another without the new attempt being identified as part of the same criminal pattern. According to projections from Incode Technologies, more than 60% of fraud attempts will be driven by artificial intelligence by the end of 2026.
Ricardo Amper, founder and CEO of Incode, described the problem with precision: "Fraud operates in networks, but defences remain in silos. Attackers benefit from acting between different organisations because each institution typically only sees its own interactions. A trust network changes that equation by turning isolated attempts into signals that others can use."
TrustGraph: an alert network between financial institutions
Facing this scenario, Incode presented TrustGraph, a trust network designed for banks, fintechs and other organisations to share fraud signals detected across different institutions. During the early adoption programme, the technology made it possible to identify 53% more fraud compared to existing models in participating entities, equivalent to 53 additional cases for every 100 previously detected.
The system is capable of recognising suspicious patterns even when attackers change their name, document or device. It generates alerts during processes such as account opening, login and transactions classified as high risk.
Data protection: no centralised database or blacklist
One of the challenges in sharing fraud information between companies is the protection of users' personal data. TrustGraph uses patented cryptography, peer-to-peer technology and secure multiparty computation (SMPC) to identify matches between institutions without exchanging their customers' personal records.
In practice, an entity can receive an alert indicating that possible fraud has already been identified at another organisation, without accessing the personal data that institution stores. The records remain at each entity and are not concentrated in a centralised database. Furthermore, each organisation autonomously determines what actions to take in response to received alerts, so the system does not function as a shared blacklist.
The technology incorporates more than a decade of research developed by Identiq, a company specialised in cryptographic peer-to-peer identity validation, acquired by Incode in June 2026. TrustGraph is already available on Incode's platform and can be integrated with existing identity verification and authentication systems.
Incode is a global company specialising in identity and fraud prevention through artificial intelligence. Its platform uses proprietary models trained with over 7 billion trust verifications, and among its clients are eight of the ten largest banks in the United States, four of the five largest marketplaces in the world, and the three leading global neobanks.
Source: Google News PE — Crime