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The missing link connecting all of your risk signals

60%
More detections than baseline Resistant AI products
-20%
False positives

Leverage the data you already generate across your entire tech stack to detect criminals faster, with more clarity and confidence. 

The missing link connecting all of your risk signals

Leverage the data you already generate across your entire tech stack to detect criminals faster, with more clarity and confidence. 

60%
More detections than baseline Resistant AI products
-20%
False positives
Join the leaders already making their processes Resistant to fraud & fincrime

Crime has changed. Fraud and fincrime prevention needs to

AI-generated documents, fake identities, and pre-verified bank accounts are sold daily on online marketplaces. Full-service fraud and money laundering networks help criminals exploit every gap in your risk and compliance processes. Break your fraud and AML teams out of their siloes and start proactively hunting the criminals down before they scale.

Defense in depth

Get insights from Documents...

Whether submitted at onboarding, or as part of an EDD process, analyze any document and cross-reference against all others.

...Behaviors & devices...

Server timestamps, buttons clicked, keyboard strokes, IP addresses used, device IDs, screen resolutions... All these latent data points tell a story.

...Transactions...

Each account, transaction, counterparty, money flow, alert, and more is a signal that can be enriched with additional context.

...And any other data point.

Find the patterns in passwords, security questions, phone numbers, company names, registration dates, and more...

ryan

Resistant AI has helped us to drastically reduce both the time it takes to catch fraud, and the amount of fraud that makes it past us to lenders.

- Ryan Edmeades MLRO and head of financial crime habito
petr

Resistant AI has significantly helped prevent specific types of credit fraud, and enhances our ability to defend against document fraud attacks.

- Petr Volevecký Head of Credit Fraud Risk raiffeisenbank
katarina

Probably the best tool in our review flow. Resistant are our bionic eyes.

- Katarina Demchuk Identity Verification PM payoneer
sergey

Resistant AI perfectly complement FINOM's AML and Anti-Fraud program with its explainable AI, ensuring transparency to AML analysts as well as the regulator.

- Sergey Petrov Co-founder, COO finom
valentina

With Resistant AI, we can manage our known risks more efficiently while also identifying and adapting to previously unknown risks.

- Valentina Butera Head of AML & AFC Operations holvi
alexandra

Resistant AI prevents the manipulation of invoices submitted to our marketplace. It allows out investors to trade in security and saves my team a huge amount of manual work.

- Alexandra Belková Head of Operations RogerLogo
Defense in depth

Go from layered defenses to a holistic risk-based approach

Layered defenses make life difficult for individual criminals. But to tackle the most advanced forms of fraud and financial crime, those layers need to talk and learn from each other. Turn weak signals into findings, and feed the right insight to the right team, at the right time.

Your rules-based monitoring system

High false positives, low recall, and endless spreadsheets of conflicting rules to update.

Augmented with Resistant Transactions

Catch more with less noise, without retraining your staff or ripping your existing TM systems. 

Supercharged with all your data

Get unheard of fincrime prevention and productivity by feeding in Documents, behaviors, and other signals... 

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Make your transactions Resistant today!

Upgrade your transaction monitoring system by talking to one of our experts. 

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module Defense in Depth FAQ Everything you ever wanted to know about our most advanced crime-fighting capabilities (but were afraid to ask).
What is Defense in depth?
Defense in depth is a decisioning layer that sits on top all of your existing risk detection processes, and analyzes all of their signals together to make new, more insightful calls. Where several layers acting alone may approve a customer onboarding or transaction, weak signals in each might indicate something wrong once combined. 
How does this improve my rules-based transaction monitoring systems?
Defense in depth enriches your transaction monitoring efforts with new signals from other parts of your risk tech stack, meaning significantly higher recall and higher precision, along with greater context and explainability.  
How does this improve my onboarding flows?
Instead of assessing each customer on their own merits, you can compare their submitted documentation, behaviors and other characteristics against those of already onboarded customers for greater prevention and more confident approvals. 
How does the system reduce false positives?
How long does implementation take?
How do I get started?