Feedzai: the RiskMandate X-ray

a company · prepared · sources read on 2026-11-07 · the deck · how this X-ray was made · X-ray another name

What happened: how this X-ray was made →

With JavaScript, the graph of this X-ray and a player for each use case's simulation are drawn here. Everything they show is written out below.

What it does

Feedzai sells a RiskOps platform that banks, payment networks and acquirers use to detect and stop fraud, scams and money laundering in real time, unifying identity, behavioural, device, transaction and third-party signals from onboarding to remediation, with a separate network score (Feedzai IQ Score) delivered by API. Since 24 September 2026 the platform ships Farol, an embedded agent that retrieves and summarises alert data, analyses rule performance, drafts suspicious activity reports and answers product questions inside RiskOps Studio. Feedzai says it is building two more things for the agentic era: Bring Your Own Agent, to connect banks' own and partner agents to its platform, and detection that checks who authorised a customer's agent, what it may do and whether it still matches its intent. s1 s4 s14 s3 s2 s6

Its AI agents

Farol

Feedzai's always-on AI agent, launched 24 September 2026 and built into RiskOps Studio. Analysts talk to it in plain language and it picks a skill: Risk Strategy, Investigations, SAR Narrative and Knowledge. Feedzai says it runs inside each institution's own environment, with traceable reasoning, transparent audit trails and what the launch release calls autonomous execution capabilities; analysts stay in control of actions. s2 s3 s6 s4

What it can do:

Retrieves and summarises alert and case data and shows what triggered an alert
Interrogates rule sets, flags rules generating noise without catching fraud (for example rules that have not caught fraud in 30 days) and recommends sharper thresholds and production-ready rule suggestions
Turns case data into an evidence-backed SAR draft for investigator review
Answers how-to questions from product documentation with cited sources
Next step on the roadmap: proactive risk insights, flagging risks before it is asked

Bring Your Own Agent

An upcoming capability, still in development with customers invited to test it: banks connect their own or partner-built agents, with their choice of model, to Feedzai's data, intelligence and workflows through a secure connection. Feedzai's blog says these agents connect via a Model Context Protocol gateway and inherit the platform's permissions and audit trail. s2 s6 s26

What it can do:

Connects a bank's own or third-party agents to Feedzai data, intelligence and workflows
Runs them alongside Farol from the same context, as one connected system
Routes them through one gateway, one audit trail and one set of controls (as described by Feedzai)

Agentic Detection (KYA)

Not an agent Feedzai runs today but the detection it says it is building to watch the agents that reach banks: telling legitimate customer agents from agent takeovers, agent botnets and malware, and checking who authorised an agent, what it is allowed to do and whether its behaviour still matches its intent. Feedzai calls the wider idea Know Your Agent (KYA). Its CTO's Web Summit 2026 session, "Who let the agent in?", is about building agents with identity, boundaries and accountability. s2 s6 s26 s28

What it can do:

Detects agents in banking traffic and classifies their intent (in development)
Verifies who authorised an agent and what it is allowed to do (in development)
Checks whether an agent's behaviour still matches its original intent, aiming to stop malicious agents before they act (in development)

Use cases: reach, mandate and gap

For each use case: what the agent can technically reach (from the sources), what a deployer would plausibly authorise (the mandate: illustrative, our judgement), the gap between the two (authority nobody accepted), and the controls that publicly stand in the way.

A customer's agent moves money at a Feedzai bank

The agent: Agentic Detection (KYA) · who uses it: A bank running Feedzai RiskOps whose retail customers start letting their own AI agents log in, pay bills and move money for them, the case Feedzai describes for the agentic era and its CTO's Web Summit session addresses.

Reach	Kind	Sources
The customer's online banking session: the agent logs in on the customer's behalf	system	s6
The customer's money: the agent pays bills and moves money, and Feedzai expects agents to approve transactions on users' behalf	money	s6 s26
Real-time payment rails (instant payments such as Pix, and Canada's Real-Time Rail) that clear and settle in seconds, around the clock	money	s26 s20 s6
Feedzai's real-time scoring of the session and payment: behavioural biometrics, device intelligence, transaction and network signals	data	s4 s9 s10
Malicious agents that look identical to customers' agents: agent takeovers, agent botnets and agentic malware	outside	s2 s6 s27

Mandate (illustrative)
Illustrative: the customer authorises the agent to pay named recurring bills from one account, up to a monthly limit
Illustrative: the agent may not add payees, raise limits or send money to a new beneficiary without the customer confirming

Gap: reach beyond the mandate	From the reach
The bank's identity checks were built to verify a human; Feedzai itself says what an agent is allowed to do, on whose authority and within what limits is not yet established, and that live registries of authorised agents will be needed. Until then the customer's limits live only between the customer and the agent.	The customer's online banking session: the agent logs in on the customer's behalf; The customer's money: the agent pays bills and moves money, and Feedzai expects agents to approve transactions on users' behalf
An instant payment clears in seconds, so any check on whether the agent is still within its limits has to happen before the money moves, not after.	The customer's money: the agent pays bills and moves money, and Feedzai expects agents to approve transactions on users' behalf; Real-time payment rails (instant payments such as Pix, and Canada's Real-Time Rail) that clear and settle in seconds, around the clock
A malicious agent can look identical to the customer's agent; the detection meant to tell them apart is still being built.	Malicious agents that look identical to customers' agents: agent takeovers, agent botnets and agentic malware; The customer's online banking session: the agent logs in on the customer's behalf

Controls that publicly stand in the way	Sources
Real-time risk scoring across onboarding, activity and payments using behavioural biometrics, device intelligence, transaction and third-party signals, with explainability and audit trails	s4 s9
In development, not live: agentic detection that verifies who authorised an agent, what it may do and whether its behaviour matches its intent; Know Your Agent on the roadmap	s2 s6 s26

The simulation, each action placed against the mandate (RiskMandate is never in the request path: read-only by design: it places, it does not stop):

The customer sets up a personal agent to pay their utility and phone bills from their current account (illustrative) (person, reaches The customer's online banking session: the agent logs in on the customer's behalf): inside the mandate
The agent logs in to online banking on the customer's behalf; Feedzai scores the session's device and behaviour signals (illustrative) (agent, reaches Feedzai's real-time scoring of the session and payment: behavioural biometrics, device intelligence, transaction and network signals): inside the mandate
Pays the month's utility bill to an existing payee (illustrative) (agent, reaches The customer's money: the agent pays bills and moves money, and Feedzai expects agents to approve transactions on users' behalf): inside the mandate
Reads an email claiming the energy supplier's bank details changed, adds the new account as a payee and approves the payment itself (illustrative) (agent, reaches The customer's money: the agent pays bills and moves money, and Feedzai expects agents to approve transactions on users' behalf): outside the mandate: in the gap. What RiskMandate's record shows: RiskMandate's record shows the agent could add a payee and approve a payment to it, while the mandate was existing payees only, and that the customer's limits were held by the agent alone, with no registered permission the bank could check.
The payment goes out on an instant rail and settles in seconds (illustrative) (agent, reaches Real-time payment rails (instant payments such as Pix, and Canada's Real-Time Rail) that clear and settle in seconds, around the clock): outside the mandate: in the gap. What RiskMandate's record shows: RiskMandate's record shows the exposure as the full payment on a rail that settles in seconds, with the bank's real-time scoring as the only documented control in front of it.
A week later a different agent, presenting like the customer's own, logs in with a stolen session and starts moving money (illustrative) (agent, reaches Malicious agents that look identical to customers' agents: agent takeovers, agent botnets and agentic malware): outside the mandate: in the gap. What RiskMandate's record shows: RiskMandate's record shows a reach no mandate covers: an outside agent indistinguishable from the authorised one, and that the detection meant to tell them apart is described by Feedzai as in development.

Farol inside a bank's fraud and AML team

The agent: Farol · who uses it: Fraud and AML analysts and risk strategy teams at a bank using Feedzai RiskOps Studio; SEB's fraud prevention business owner is quoted on its value for data retrieval and rule suggestions.

Reach	Kind	Sources
Alert and case data in the institution's fraud and AML stack, and the analyst tools around it	data	s3 s2
Rule sets, rule performance, coverage gaps and thresholds	system	s2 s3
Production-ready rule suggestions and what the launch release calls autonomous execution capabilities in risk workflows	system	s3
Suspicious Activity Report drafts built from case data, about named customers, for local filing requirements	people	s2 s3
Product documentation, answered in plain language with cited sources	data	s2

Mandate (illustrative)
As Feedzai documents it: agents surface insights and recommendations with transparent reasoning, and analysts verify outcomes and take actions as they see fit
Illustrative: the bank lets Farol read alerts and draft; any rule change goes through challenger testing and a named approver, and only an investigator files a SAR

Gap: reach beyond the mandate	From the reach
The launch release speaks of autonomous execution and production-ready rule suggestions, while the product page says analysts take the actions; which actions Farol can take on its own in a given deployment is not public.	Production-ready rule suggestions and what the launch release calls autonomous execution capabilities in risk workflows; Rule sets, rule performance, coverage gaps and thresholds
A SAR draft about a named customer is one click from case data; Feedzai's own blog says the final call to file must be documented and attributed to a named human.	Suspicious Activity Report drafts built from case data, about named customers, for local filing requirements

Controls that publicly stand in the way	Sources
Farol runs within each institution's own environment, so what it surfaces stays in the customer's estate	s3
Traceable reasoning and transparent audit trails: what the agent did, why, and the information behind it	s2 s3 s5
Challenger testing, backtesting, explainability, audit trails and model governance for rule and model changes	s4
SAR drafts are produced for investigator review	s2 s3

The simulation, each action placed against the mandate (RiskMandate is never in the request path: read-only by design: it places, it does not stop):

An analyst asks Farol what triggered an alert on a customer's transfer (person, reaches Alert and case data in the institution's fraud and AML stack, and the analyst tools around it): inside the mandate
Retrieves and summarises the alert and case data, with the reasoning shown (agent, reaches Alert and case data in the institution's fraud and AML stack, and the analyst tools around it): inside the mandate
A risk strategist asks which rules are noisy; Farol flags rules that have not caught fraud in 30 days (person, reaches Rule sets, rule performance, coverage gaps and thresholds): inside the mandate
Recommends a sharper threshold as a production-ready rule suggestion (agent, reaches Production-ready rule suggestions and what the launch release calls autonomous execution capabilities in risk workflows): inside the mandate
The suggested threshold is applied to the live rule without a challenger test or a named approver (illustrative) (agent, reaches Production-ready rule suggestions and what the launch release calls autonomous execution capabilities in risk workflows): outside the mandate: in the gap. What RiskMandate's record shows: RiskMandate's record shows a live detection rule within reach of an agent's suggestion, while the mandate required challenger testing and a named approver, and that where Farol's autonomous execution stops in this deployment is not documented publicly.
Turns the case into an evidence-backed SAR draft (agent, reaches Suspicious Activity Report drafts built from case data, about named customers, for local filing requirements): inside the mandate
Under queue pressure the draft is filed unchanged, with no investigator's own review recorded (illustrative) (person, reaches Suspicious Activity Report drafts built from case data, about named customers, for local filing requirements): outside the mandate: in the gap. What RiskMandate's record shows: RiskMandate's record shows a regulatory report about a named person drafted by an agent and filed without evidence of the named human review the mandate requires.

A bank's own agents through Bring Your Own Agent

The agent: Bring Your Own Agent · who uses it: A bank that has built its own agents, or uses partner-built ones, and connects them to Feedzai once Bring Your Own Agent ships (it is upcoming and in testing with customers).

Reach	Kind	Sources
Feedzai's data, intelligence and workflows, the same context Farol works from	data	s2
A Model Context Protocol gateway through which connected agents inherit the platform's permissions and audit trail	system	s26
Third-party partner agents and the bank's choice of model	outside	s2 s6
Bank workflows reached through a secure connection	system	s6 s2

Mandate (illustrative)
Illustrative: the bank's own agent reads alert context for one queue and writes notes, nothing else
Illustrative: a partner agent is read-only and sees no customer data beyond the case it is asked about

Gap: reach beyond the mandate	From the reach
Agents that inherit the platform's permissions reach whatever the platform role they run under reaches; how narrowly one connected agent can be scoped is not yet documented.	Feedzai's data, intelligence and workflows, the same context Farol works from; A Model Context Protocol gateway through which connected agents inherit the platform's permissions and audit trail
A partner agent on a model of the bank's choice works from the same context as Feedzai's own agents; where that data goes for inference is outside Feedzai's estate.	Third-party partner agents and the bank's choice of model; Feedzai's data, intelligence and workflows, the same context Farol works from

Controls that publicly stand in the way	Sources
As described for the upcoming capability: one gateway, one audit trail and one set of controls for every agent, wherever it came from	s26
Secure connection to the Feedzai platform	s6

The simulation, each action placed against the mandate (RiskMandate is never in the request path: read-only by design: it places, it does not stop):

The bank connects its own case-notes agent through the gateway (illustrative) (person, reaches A Model Context Protocol gateway through which connected agents inherit the platform's permissions and audit trail): inside the mandate
Reads alert context for its queue and writes a note (illustrative) (agent, reaches Feedzai's data, intelligence and workflows, the same context Farol works from): inside the mandate
Because it inherits the platform permissions of the connecting role, it also lists alerts and customer profiles from other queues (illustrative) (agent, reaches Feedzai's data, intelligence and workflows, the same context Farol works from): outside the mandate: in the gap. What RiskMandate's record shows: RiskMandate's record shows the agent's reach set by an inherited platform role rather than by its own mandate, and every queue that role can see inside it.
A partner's agent, running on an outside model, is connected for one case review (illustrative) (person, reaches Third-party partner agents and the bank's choice of model): inside the mandate
The partner agent sends the full customer profile to its model provider to summarise it (illustrative) (agent, reaches Third-party partner agents and the bank's choice of model): outside the mandate: in the gap. What RiskMandate's record shows: RiskMandate's record shows customer data reachable by a third-party agent and its model provider, outside the estate where Feedzai says Farol keeps its insights, with only the gateway's audit trail as evidence.

The run

The search agents that looked for leads on 2026-11-07T21:16:45.210Z, for a total cost of $0.0929. What a model says is a lead, not a fact.

Agent	Model	Time	Citations	Leads
Perplexity search	`perplexity/sonar`	7.4 s	15	16
OpenAI search	`openai/gpt-5.4-nano:online`	12.9 s	3	9
Gemini search	`google/gemini-3.1-flash-lite:online`	2.8 s	4	5
OpenRouter web search	`anthropic/claude-haiku-5.5`	25.0 s	14	24

Leads: what the models claimed, and what we found

Model	Claim	Status	Source
perplexity	Feedzai provides AI-powered fraud and financial crime prevention for banks and fintechs, in real time.	confirmed	s1
perplexity	Feedzai combines models, shared intelligence, rules and AI agents across the risk lifecycle.	confirmed	s5
perplexity	Farol is an always-on AI agent embedded in RiskOps Studio that analyses rules, investigates alerts and drafts SARs.	confirmed	s3
perplexity	Farol flags rules that generate noise without catching fraud.	confirmed	s3
perplexity	Farol retrieves and summarises alert data.	confirmed	s3
perplexity	Bring Your Own Agent is an upcoming capability to connect customers' own or partner-built agents to Feedzai's data, intelligence and workflows.	confirmed	s2
perplexity	Feedzai says fraudsters may use AI agents too and is building detection to verify who authorised an agent, what it can do and whether its behaviour still matches intent.	confirmed	s6
perplexity	Feedzai describes the need to detect agent takeovers, agent botnets and malware.	confirmed	s6
perplexity	In suspicious activity reporting, the agent can reach SAR drafting and submission workflows.	not-found	s2
perplexity	Feedzai's homepage says it stops fraud in real time with AI that catches risk and an AI agent that helps teams act faster.	confirmed	s1
perplexity	Feedzai's AI detects fraud across inbound and outbound transactions and payment rails, including card fraud, account takeover, synthetic identity, scams, mules, phishing and malware.	confirmed	s5
perplexity	Feedzai's AML solution blends rules and machine learning, reduces false positives, prioritises alerts and integrates SAR submission.	confirmed	s8
perplexity	RiskOps unifies identity across channels to detect fraud and money laundering risk from onboarding to remediation.	confirmed	s4
perplexity	Transaction fraud tools evaluate payments and transfers in real time using transaction history, customer behaviour and predictive intelligence.	confirmed	s11
perplexity	Retail bank offerings combine long-term and real-time data, behavioural biometrics, device and network attributes.	confirmed	s10
perplexity	Feedzai's AML tools support investigation, case management and suspicious activity reporting on one platform.	confirmed	s7
openai	RiskOps unifies identity and risk signals across the customer lifecycle to detect fraud and money laundering in real time.	confirmed	s4
openai	Farol delivers contextual case summaries, automated rule performance analytics and SAR drafting inside the stack analysts already use.	confirmed	s3
openai	Farol launched with Risk Strategy, Investigations, Knowledge and SAR Drafting skills.	confirmed	s3
openai	Farol recommends threshold changes.	confirmed	s3
openai	Farol answers how-to questions about the platform inside the user interface.	confirmed	s3
openai	Feedzai's agentic AI augments analysts rather than replacing them, with transparent reasoning so analysts verify and act.	confirmed	s2
openai	Feedzai is building detection to verify who authorised an agent, what it is allowed to do and whether behaviour matches intent.	confirmed	s2
openai	Farol includes agent protection and detection that verifies who authorised an agent.	not-found	s2
openai	The AML offering covers transaction monitoring and watchlist screening.	confirmed	s13
gemini	Feedzai uses AI and machine learning to detect and prevent financial crime in real time, for fraud and AML across banking, fintech and payments.	confirmed	s16
gemini	'Feedzai Fraud Prevention AI' is an autonomous system that interacts with core banking systems to block transactions.	not-found
gemini	Tier-1 banks and major fintechs use it, with an agent reaching payment processing systems and AML compliance databases.	not-found	s17
gemini	Feedzai's technology protects over 900 million users.	confirmed	s16
gemini	Feedzai uses a 'Risk Ledger' to share threat intelligence across its network.	not-found
openrouter	Farol launched on 24 September 2026 and is integrated into RiskOps Studio.	confirmed	s3
openrouter	Farol offers conversational intelligence, automated rule performance analytics, contextual case summaries and production-ready rule suggestions.	confirmed	s3
openrouter	The launch release describes Farol as capable of autonomous execution with transparent audit trails.	confirmed	s3
openrouter	Farol runs inside each institution's own environment so insights stay within the customer's estate.	confirmed	s3
openrouter	Farol has data retrieval from case data and analyst tools, per the launch release.	confirmed	s3
openrouter	RiskOps Studio covers strategy configuration, investigations and reporting.	confirmed	s4
openrouter	Novobanco combined KYC, AML and fraud teams into one Economic Crime Prevention function and selected Feedzai in March 2026, replacing legacy tools.	confirmed	s19
openrouter	Peoples Group partnered with Feedzai ahead of Canada's Real-Time Rail rollout in late 2026, starting with real-time transaction fraud monitoring and alert management.	confirmed	s20
openrouter	A federal lawsuit alleged Citibank failed to prevent a roughly $20 million romance-investment scam; Feedzai commented in general terms and no Feedzai failure is alleged.	confirmed	s21
openrouter	Feedzai's 2026 survey found self-rated readiness of 80.2 against measured capability of 66.9; fielded 13 July to 4 August 2026, published 8 October 2026.	confirmed	s25
openrouter	Feedzai was founded in 2011.	confirmed	s18
openrouter	The platform is built on AWS and ingests third-party and internal data through APIs and adapters.	confirmed	s18
openrouter	Feedzai's technology safeguards more than one billion consumers and $9 trillion in payment volume a year.	confirmed	s3
openrouter	Feedzai IQ Score launched 9 June 2026, delivered through one API, using federated learning so raw customer data is not shared.	confirmed	s14
openrouter	IQ Score: 4x more fraud detected and 50% fewer alerts versus rules-based approaches (company-reported).	confirmed	s14
openrouter	RiskFM, announced 24 March 2026, is a tabular foundation model for fraud, AML and risk decisions trained on onboarding, digital, payment and AML data.	confirmed	s15
openrouter	Feedzai's AML offering uses rules and AI to prioritise alerts for investigators.	confirmed	s13
openrouter	Feedzai's AML page says its AI agents reduce false positives and focus teams on the riskiest alerts.	confirmed	s13
openrouter	Feedzai was placed as a Leader in the 2025 SPARK Matrix for FCC Data Management.	confirmed	s23
openrouter	Feedzai was named Best-in-Class in an Aite-Novarica fraud and AML machine learning platform report, citing its single risk engine and ease of use.	confirmed	s24
openrouter	Feedzai won the AML Mule Detection award in Chartis' FCC50 2026.	confirmed	s22
openrouter	Feedzai and Mastercard announced a partnership in February 2025 to expand Mastercard's Consumer Fraud Risk scam solution.	unverified
openrouter	Feedzai's Fraud Readiness Index 2026 surveyed more than 1,000 senior fraud leaders across 12 countries.	confirmed	s25
openrouter	The European Central Bank concluded a framework agreement with Feedzai as the first-ranked tenderer.	confirmed	s12

The questions we would ask

We have not asked them yet.

When Farol uses what your launch release calls autonomous execution, which actions can it take without an analyst approving them (changing a live rule, closing an alert, filing a SAR), and who in the bank sets that line?
Agents connected through Bring Your Own Agent inherit the platform's permissions: can a bank scope one connected agent below the role it connects under, and is each action attributed to that agent and to a named human in the audit trail?
For Know Your Agent: when a customer's agent arrives at a bank, what will your detection read to learn the limits the customer set, and what happens to a payment that is outside them on a rail that settles in seconds?
Farol runs inside the institution's environment: which model providers does it call, and does any case or customer data leave the bank's estate for inference?
Can a bank export Farol's record of what it retrieved, recommended and who acted on it into its own model risk records, so a supervisor can see who authorised each step?

Sources

s1 AI-Native Fraud & Financial Crime Prevention Platform | Feedzai, Feedzai (feedzai.com) (own-site, read 2026-11-07)
https://www.feedzai.com/
s2 Farol: The AI Agent for Fraud & Risk | Feedzai, Feedzai (feedzai.com) (own-site, read 2026-11-07)
https://www.feedzai.com/farol-agentic-riskops/
s3 As Banks Pivot to Agentic AI, Feedzai Unveils Farol to Transform Fraud Analysis and Cut Investigation Times (24 September 2026), Feedzai (feedzai.com) (own-site, read 2026-11-07)
https://www.feedzai.com/pressrelease/as-banks-pivot-to-agentic-ai-feedzai-unveils-farol-to-transform-fraud-analysis-and-cut-investigation-times/
s4 Market Leading Fraud Prevention RiskOps Platform | Feedzai, Feedzai (feedzai.com) (own-site, read 2026-11-07)
https://www.feedzai.com/riskops/
s5 AI for Fraud Prevention and Financial Crime | Feedzai, Feedzai (feedzai.com) (own-site, read 2026-11-07)
https://www.feedzai.com/solutions/ai/
s6 Beyond the Transaction: 3 Key Lessons from Feedzai Fusion São Paulo 2026 (6 November 2026), Feedzai (feedzai.com) (own-site, read 2026-11-07)
https://www.feedzai.com/blog/feedzai-fusion-brazil/
s7 AML Transaction Monitoring Software: Powered by AI | Feedzai, Feedzai (feedzai.com) (own-site, read 2026-11-07)
https://www.feedzai.com/solutions/aml-transaction-monitoring/
s8 Anti-Money Laundering Transaction Monitoring | Feedzai, Feedzai (feedzai.com) (own-site, read 2026-11-07)
https://www.feedzai.com/resource/anti-money-laundering-transaction-monitoring/
s9 Transaction Fraud Solution for Banks | Feedzai, Feedzai (feedzai.com) (own-site, read 2026-11-07)
https://www.feedzai.com/solutions/transaction-fraud/
s10 Fraud & Financial Crime Prevention for Retail Banks | Feedzai, Feedzai (feedzai.com) (own-site, read 2026-11-07)
https://www.feedzai.com/industries/retail-banks/
s11 RiskOps Brazil: Merging Identity, Fraud, and AML | Feedzai, Feedzai (feedzai.com) (own-site, read 2026-11-07)
https://www.feedzai.com/blog/riskops-brazil-risk-architecture/
s12 Press Releases | Feedzai, Feedzai (feedzai.com) (own-site, read 2026-11-07)
https://www.feedzai.com/press-releases/
s13 Anti-Money Laundering Solutions | Feedzai, Feedzai (feedzai.com) (own-site, read 2026-11-07)
https://www.feedzai.com/anti-money-laundering/
s14 Feedzai Opens $9 Trillion Fraud Intelligence Network (9 June 2026), Feedzai (feedzai.com) (own-site, read 2026-11-07)
https://www.feedzai.com/pressrelease/feedzai-iq-score-9t-fraud-intelligence-network/
s15 Feedzai Unveils RiskFM AI Foundation Model for Financial Crime Prevention (24 March 2026), Feedzai (feedzai.com) (own-site, read 2026-11-07)
https://www.feedzai.com/pressrelease/riskfm-ai-risk-model/
s16 llm.txt for Feedzai, Feedzai (feedzai.com) (own-site, read 2026-11-07)
https://www.feedzai.com/llms.txt
s17 Customer Stories | Feedzai, Feedzai (feedzai.com) (own-site, read 2026-11-07)
https://www.feedzai.com/customers/
s18 Feedzai: AI-Native, End-to-end Fraud and Financial Crime Prevention | AWS Marketplace, AWS Marketplace (vendor-supplied listing) (other, read 2026-11-07)
https://aws.amazon.com/marketplace/pp/prodview-6cbmn62splyhq
s19 Novobanco selects Feedzai to unify fraud and AML prevention, FinTech Global (press, read 2026-11-07)
https://fintech.global/2026/03/09/novobanco-selects-feedzai-to-unify-fraud-and-aml-prevention/
s20 In Advance of Canada's Real-Time Rail Rollout, Peoples Group Partners with Feedzai to Advance Fraud Defenses and Support 24/7 Instant Payments (PR Newswire, 10 September 2026), Yahoo Finance (PR Newswire release) (press, read 2026-11-07)
https://finance.yahoo.com/technology/ai/articles/advance-canadas-real-time-rail-130000007.html
s21 Feedzai Warns of AI-Powered Romance Scams as Citibank Faces Lawsuit Over $20 Million Fraud, FinanceFeeds (press, read 2026-11-07)
https://financefeeds.com/feedzai-warns-of-ai-powered-romance-scams-as-citibank-faces-lawsuit-over-20-million-fraud/
s22 Feedzai news and analysis articles: Chartis Research, Chartis Research (other, read 2026-11-07)
https://www.chartis-research.com/organisations/feedzai
s23 Feedzai Recognized as Industry Leader in SPARK Matrix for FCC Data Management, Feedzai (feedzai.com) (own-site, read 2026-11-07)
https://www.feedzai.com/inthenews/feedzai-recognized-as-industry-leader-in-2026-spark-matrix-for-fcc/
s24 Feedzai Tops Fraud and AML Machine Learning Platforms | Feedzai, Feedzai (feedzai.com) (own-site, read 2026-11-07)
https://www.feedzai.com/resource/feedzai-again-named-best-in-class-fraud-and-aml-machine-learning-platform-by-aite-novarica-group/
s25 Banks Think They're Ready for Tomorrow's Fraud. Feedzai's Survey Suggests Otherwise (8 October 2026), PR Newswire (press, read 2026-11-07)
https://www.prnewswire.com/in/news-releases/banks-think-theyre-ready-for-tomorrows-fraud-feedzais-survey-suggests-otherwise-302901413.html
s26 What Agentic AI Actually Changes in Fraud Prevention (13 August 2026), Feedzai (feedzai.com) (own-site, read 2026-11-07)
https://www.feedzai.com/blog/agentic-ai-fraud-prevention/
s27 Agentic AI Fraud Detection: The Future of Autonomous Risk Prevention (24 September 2026), Feedzai (feedzai.com) (own-site, read 2026-11-07)
https://www.feedzai.com/blog/agentic-ai-fraud-detection/
s28 Who let the agent in? | Web Summit (Lisbon 2026 session page), Web Summit (websummit.com) (other, read 2026-11-07)
https://websummit.com/sessions/lis26/1de6a591-ec17-4399-8f28-13f4bff1cae1/who-let-the-agent-in/

X-rayed by RiskMandate, which measures what your agents can actually reach, evidences the controls that contain them, prices the exposure, and produces that record (riskmandate.ai/llms.txt). The mandates are illustrative. Built by xray@web-summit.dinis from data/xray/feedzai/xray.json.