Get to Know Fortza’s AI Defense Layers

How it’s used:

The FBI layer initiates a database-level identity check, leveraging the FBI’s public records API to screen for potential identity flags. Fortza sends structured identity data, specifically name and gender, to query against known aliases, flagged individuals, or persons of interest. This matching process occurs asynchronously within the Fortza pipeline and contributes to both fraud scoring and fatal flagging logic.

Why it’s important:

This layer provides a direct line into government intelligence data. It helps Fortza detect fraud scenarios involving identity theft, impersonation, or high-risk individuals who may otherwise pass traditional verification checks. By triangulating known fraud actors, the FBI layer allows Fortza to elevate or auto-reject potentially dangerous transactions before downstream processing even begins.

Real-world impact:

Especially valuable in sectors like fintech, edtech, and background screening, this layer can flag fraudulent users trying to slip through using credible but stolen identities—long before behavioral patterns emerge.

How it’s used:

The Anomaly Detection Layer continuously scans transaction data for deviations from normal behavior by applying dynamic thresholds, statistical models, and pattern recognition algorithms. Rather than relying solely on historical fraud examples, it dynamically adjusts to detect behaviors that fall outside expected ranges. This includes anomalies in transaction frequency, time-of-day activity, spending amounts, and device usage.

Why it’s important:


This layer is essential because most fraud begins with subtle shifts in behavior that rules-based systems overlook. By catching these early, Fortza can flag suspicious activity without needing a known fraud label, which is critical for zero-day fraud attacks or brand-new fraud vectors.

Real-world impact:


Businesses using this layer can detect and shut down novel fraud schemes before they escalate. For example, it can identify unusual transaction bursts that mimic legitimate user behavior—critical for ecommerce platforms and digital banking apps that face continuous fraud evolution.

How it’s used:

The Encoder-Decoder AI Layer leverages deep learning models, particularly sequence-to-sequence architectures, to analyze the flow and structure of user behavior. It captures not just what was done, but the order, timing, and contextual intent behind it. This allows Fortza to detect inconsistencies in digital behavior that typical static models miss.

Why it’s important:


This is crucial in detecting fraud that plays the long game—where bad actors simulate normal activity over time before executing an exploit. The Encoder-Decoder Layer builds a nuanced behavioral fingerprint for each user session, allowing Fortza to detect when something feels 'off' even if the transaction itself looks benign.

Real-world impact:

Ideal for industries dealing with high-volume, high-velocity data, this layer helps identify synthetic fraud attempts that use carefully constructed histories to fool legacy systems. It's especially impactful in fintech, SaaS, and any platform with transactional APIs.

How it’s used:

The Identity Signal Layer cross-references customer-provided details like names and emails against a library of fraud rules, heuristics, and anomaly detection algorithms. It looks for mismatches, suspicious patterns, and statistically improbable combinations that often point to synthetic or manipulated identities.

Why it’s important:


Fraud doesn’t always come from strangers—it often comes disguised as a slightly altered known entity. This layer picks up on inconsistencies that signal identity morphing or tampering, helping Fortza act on weak signals that are otherwise ignored.

Real-world impact:


Especially effective at reducing signup fraud, this layer helps block fake account creation, voucher abuse, and downstream fraud events triggered by fraudulent onboarding. It's vital for subscription services, gig platforms, and rewards programs.

How it’s used:

The Name Recognition Layer uses natural language processing and a custom word cloud engine to analyze how closely a customer’s name resembles known figures—like celebrities, influencers, or athletes. By comparing the submitted name against a database of public personas, Fortza flags cases where fraudulent actors are attempting to impersonate or gain credibility by using familiar names.

Why it’s important:


This layer is important because fraudsters often use high-recognition names to trick systems or gain trust. Identifying this pattern early stops social engineering and impersonation fraud before it spreads.

Real-world impact:


In industries where trust is everything—like financial services, online education, and social platforms—this layer helps spot false identities designed to influence, impersonate, or gain unauthorized access using name familiarity as a social lever.

How it’s used:

The Domain Validation Layer examines the structure and legitimacy of email domains by checking DNS records, MX configurations, domain registration age, and known provider risk ratings. It runs background checks on the email’s domain to validate whether it comes from a trustworthy source or a potentially suspicious origin.

Why it’s important:


Fraudsters often use burner or freshly registered domains to bypass spam filters and exploit weak KYC systems. This layer allows Fortza to recognize the telltale signs of email-based fraud before engagement ever begins.

Real-world impact:


Critical in detecting account creation fraud, especially in digital lending, crypto exchanges, and ecommerce, this layer prevents fake email domains from being used to register shell identities.

How it’s used:

The Email Recognition Layer uses natural language analysis to break down the email address into structural patterns. It applies word cloud comparison and syntactic matching to identify emails that resemble known fraudulent structures or spam-bot generation patterns.

Why it’s important:


While a single email string might look legitimate, its composition may follow recognizable fraud templates. This layer adds nuance by detecting emails that feel ‘off’ even when they pass syntax validation.

Real-world impact:


Especially useful in identifying coordinated fraud campaigns or botnet-driven account creation, this layer helps reduce high-volume fraud at the source—before it reaches customer support or finance teams.

How it’s used:

This layer connects to the Google Address Validation API to verify that provided addresses are not only properly formatted but actually exist. It checks for street-level accuracy, postal codes, locality structure, and other geo-data to ensure physical address legitimacy.

Why it’s important:


Bad actors frequently use fake or outdated addresses to pass soft validation. By tying addresses to real-world verified data, Fortza ensures that location-based fraud risks are intercepted early.

Real-world impact:


Reduces the approval of fraudulent deliveries, account openings, or loan applications by validating the geographic identity of users, especially in insurance and direct-to-consumer platforms.

How it’s used:

This layer maps the transaction’s zip code against a geo-cluster of past fraud cases to calculate local risk. Using spatial proximity algorithms, it raises suspicion when transactions originate from high-density fraud regions.

Why it’s important:


Fraud doesn’t just move online—it’s geographically concentrated. This layer adds an often-ignored context: physical fraud risk tied to real-world locations.

Real-world impact:


Particularly valuable in financial services and logistics, this layer allows for smarter risk scoring based on known fraud territories, without stereotyping entire states or cities.

How it’s used:

This layer leverages historical IP-based location data to assess if the current user is operating from a location previously linked to fraud. It considers time zones, IP reputation, VPN presence, and behavioral signals tied to the geography.

Why it’s important:


Fraud patterns often emerge from repeated geo-coordinates—either coordinated by fraud rings or through exploited regions. This layer monitors these hot zones and flags risky behavior tied to them.

Real-world impact:


Helps organizations limit damage from recurring geographic hotspots—such as compromised call centers or proxy-routed attacks—before fraud spreads further into their systems.

How it’s used:

Fortza’s Historical Customer Data Layer aggregates and analyzes previous user activity—login behavior, transaction trends, device IDs—to create a behavioral profile unique to each user. This profile acts as a baseline for real-time fraud detection.

Why it’s important:


Anomalous behavior is often only detectable when compared to a user's history. This layer acts as a behavioral fingerprint to catch account takeovers or synthetic identity evolution.

Real-world impact:


Increases fraud detection precision for enterprise platforms managing large user bases, such as fintech, marketplaces, and SaaS companies.

How it’s used:

This AI-driven layer analyzes all elements of the current transaction—IP, device, user details, and more—and compares them to historical fraud patterns detected across Fortza’s ecosystem. It uses ensemble models to weigh the likelihood of fraud based on overlapping features with past suspicious activity.

Why it’s important:


Most fraud attempts don’t come out of nowhere—they echo the past. By leveraging what’s already known, this layer strengthens Fortza’s ability to recognize repeated attack strategies across different actors.

Real-world impact:


Boosts detection accuracy and reduces false negatives in high-stakes verticals like digital lending, B2B SaaS, and healthcare portals.