Workshop
Dec 8
9am pt / 12pm et
Dec 8
9am pt / 12pm et
Live Build
Live Build
Fraud Score, from confirmed fraud to predictions in your app
Register
We're committed to your privacy. You may unsubscribe from our communications at any time.
Register
We're committed to your privacy. You may unsubscribe from our communications at any time.
Register
We're committed to your privacy. You may unsubscribe from our communications at any time.
Turn the fraud you've already caught into predictions for the fraud you haven't
Your team already knows a lot about fraud. Every chargeback, closed-out fake account, and manual review decision is a confirmed example of what fraud looks like on your platform. Most of that knowledge never makes it back into detection, because turning it into a model means building, training, and maintaining ML infrastructure.
Fingerprint's Fraud Score takes those confirmed outcomes, combines them with Fingerprint device intelligence, and trains models specific to your business and the fraud types you care about. Predictions for each fraud type come back in the Fingerprint API response making it easy for you to consume and act on.
In this livestream, Keshia Rose builds the code around Fraud Score from scratch, then shows a trained model scoring new traffic.
You'll see how to:
Store Fingerprint event IDs alongside the signups, logins, and transactions you may need to label later
Turn confirmed outcomes, like chargebacks or manual review decisions, into labeled fraud data
Read Fraud Score predictions for different fraud types from the Fingerprint API response
Use those predictions to decide when to allow, review, or block
Keshia Rose
Sr. Developer Evangelist @ Fingerprintprint
Keshia Rose is a Senior Developer Evangelist at Fingerprint, where she helps developers build more secure and seamless online experiences. Through hands-on demos, technical workshops, and developer-focused content, she breaks down complex fraud prevention concepts into practical solutions teams can put into action. Her work helps developers better understand how device intelligence, risk signals, and modern fraud detection techniques can be used to identify suspicious behavior, stop abuse, and protect legitimate users without adding unnecessary friction.
SPEAKERS
Keshia Rose
Sr. Developer Evangelist @ Fingerprintprint
Keshia Rose is a Senior Developer Evangelist at Fingerprint, where she helps developers build more secure and seamless online experiences. Through hands-on demos, technical workshops, and developer-focused content, she breaks down complex fraud prevention concepts into practical solutions teams can put into action. Her work helps developers better understand how device intelligence, risk signals, and modern fraud detection techniques can be used to identify suspicious behavior, stop abuse, and protect legitimate users without adding unnecessary friction.
Keshia Rose
Sr. Developer Evangelist @ Fingerprintprint
Keshia Rose is a Senior Developer Evangelist at Fingerprint, where she helps developers build more secure and seamless online experiences. Through hands-on demos, technical workshops, and developer-focused content, she breaks down complex fraud prevention concepts into practical solutions teams can put into action. Her work helps developers better understand how device intelligence, risk signals, and modern fraud detection techniques can be used to identify suspicious behavior, stop abuse, and protect legitimate users without adding unnecessary friction.
Keshia Rose
Sr. Developer Evangelist @ Fingerprintprint
Keshia Rose is a Senior Developer Evangelist at Fingerprint, where she helps developers build more secure and seamless online experiences. Through hands-on demos, technical workshops, and developer-focused content, she breaks down complex fraud prevention concepts into practical solutions teams can put into action. Her work helps developers better understand how device intelligence, risk signals, and modern fraud detection techniques can be used to identify suspicious behavior, stop abuse, and protect legitimate users without adding unnecessary friction.
Workshop
Dec 8
9am pt / 12pm et
Dec 8
9am pt / 12pm et
Live Build
Fraud Score, from confirmed fraud to predictions in your app
Register
We're committed to your privacy. You may unsubscribe from our communications at any time.
Register
We're committed to your privacy. You may unsubscribe from our communications at any time.
Register
We're committed to your privacy. You may unsubscribe from our communications at any time.
Turn the fraud you've already caught into predictions for the fraud you haven't
Your team already knows a lot about fraud. Every chargeback, closed-out fake account, and manual review decision is a confirmed example of what fraud looks like on your platform. Most of that knowledge never makes it back into detection, because turning it into a model means building, training, and maintaining ML infrastructure.
Fingerprint's Fraud Score takes those confirmed outcomes, combines them with Fingerprint device intelligence, and trains models specific to your business and the fraud types you care about. Predictions for each fraud type come back in the Fingerprint API response making it easy for you to consume and act on.
In this livestream, Keshia Rose builds the code around Fraud Score from scratch, then shows a trained model scoring new traffic.
You'll see how to:
Store Fingerprint event IDs alongside the signups, logins, and transactions you may need to label later
Turn confirmed outcomes, like chargebacks or manual review decisions, into labeled fraud data
Read Fraud Score predictions for different fraud types from the Fingerprint API response
Use those predictions to decide when to allow, review, or block
Keshia Rose
Sr. Developer Evangelist @ Fingerprintprint
Keshia Rose is a Senior Developer Evangelist at Fingerprint, where she helps developers build more secure and seamless online experiences. Through hands-on demos, technical workshops, and developer-focused content, she breaks down complex fraud prevention concepts into practical solutions teams can put into action. Her work helps developers better understand how device intelligence, risk signals, and modern fraud detection techniques can be used to identify suspicious behavior, stop abuse, and protect legitimate users without adding unnecessary friction.
SPEAKERS
Keshia Rose
Sr. Developer Evangelist @ Fingerprintprint
Keshia Rose is a Senior Developer Evangelist at Fingerprint, where she helps developers build more secure and seamless online experiences. Through hands-on demos, technical workshops, and developer-focused content, she breaks down complex fraud prevention concepts into practical solutions teams can put into action. Her work helps developers better understand how device intelligence, risk signals, and modern fraud detection techniques can be used to identify suspicious behavior, stop abuse, and protect legitimate users without adding unnecessary friction.
Keshia Rose
Sr. Developer Evangelist @ Fingerprintprint
Keshia Rose is a Senior Developer Evangelist at Fingerprint, where she helps developers build more secure and seamless online experiences. Through hands-on demos, technical workshops, and developer-focused content, she breaks down complex fraud prevention concepts into practical solutions teams can put into action. Her work helps developers better understand how device intelligence, risk signals, and modern fraud detection techniques can be used to identify suspicious behavior, stop abuse, and protect legitimate users without adding unnecessary friction.
Workshop
Dec 8
9am pt / 12pm et
Dec 8
9am pt / 12pm et
Live Build
Fraud Score, from confirmed fraud to predictions in your app
Register
We're committed to your privacy. You may unsubscribe from our communications at any time.
Register
We're committed to your privacy. You may unsubscribe from our communications at any time.
Register
We're committed to your privacy. You may unsubscribe from our communications at any time.
Turn the fraud you've already caught into predictions for the fraud you haven't
Your team already knows a lot about fraud. Every chargeback, closed-out fake account, and manual review decision is a confirmed example of what fraud looks like on your platform. Most of that knowledge never makes it back into detection, because turning it into a model means building, training, and maintaining ML infrastructure.
Fingerprint's Fraud Score takes those confirmed outcomes, combines them with Fingerprint device intelligence, and trains models specific to your business and the fraud types you care about. Predictions for each fraud type come back in the Fingerprint API response making it easy for you to consume and act on.
In this livestream, Keshia Rose builds the code around Fraud Score from scratch, then shows a trained model scoring new traffic.
You'll see how to:
Store Fingerprint event IDs alongside the signups, logins, and transactions you may need to label later
Turn confirmed outcomes, like chargebacks or manual review decisions, into labeled fraud data
Read Fraud Score predictions for different fraud types from the Fingerprint API response
Use those predictions to decide when to allow, review, or block
Keshia Rose
Sr. Developer Evangelist @ Fingerprintprint
Keshia Rose is a Senior Developer Evangelist at Fingerprint, where she helps developers build more secure and seamless online experiences. Through hands-on demos, technical workshops, and developer-focused content, she breaks down complex fraud prevention concepts into practical solutions teams can put into action. Her work helps developers better understand how device intelligence, risk signals, and modern fraud detection techniques can be used to identify suspicious behavior, stop abuse, and protect legitimate users without adding unnecessary friction.
SPEAKERS
Keshia Rose
Sr. Developer Evangelist @ Fingerprintprint
Keshia Rose is a Senior Developer Evangelist at Fingerprint, where she helps developers build more secure and seamless online experiences. Through hands-on demos, technical workshops, and developer-focused content, she breaks down complex fraud prevention concepts into practical solutions teams can put into action. Her work helps developers better understand how device intelligence, risk signals, and modern fraud detection techniques can be used to identify suspicious behavior, stop abuse, and protect legitimate users without adding unnecessary friction.
Keshia Rose
Sr. Developer Evangelist @ Fingerprintprint
Keshia Rose is a Senior Developer Evangelist at Fingerprint, where she helps developers build more secure and seamless online experiences. Through hands-on demos, technical workshops, and developer-focused content, she breaks down complex fraud prevention concepts into practical solutions teams can put into action. Her work helps developers better understand how device intelligence, risk signals, and modern fraud detection techniques can be used to identify suspicious behavior, stop abuse, and protect legitimate users without adding unnecessary friction.
Workshop
Dec 8
9am pt / 12pm et
Dec 8
9am pt / 12pm et
Live Build
Fraud Score, from confirmed fraud to predictions in your app
Register
We're committed to your privacy. You may unsubscribe from our communications at any time.
Register
We're committed to your privacy. You may unsubscribe from our communications at any time.
Register
We're committed to your privacy. You may unsubscribe from our communications at any time.
Turn the fraud you've already caught into predictions for the fraud you haven't
Your team already knows a lot about fraud. Every chargeback, closed-out fake account, and manual review decision is a confirmed example of what fraud looks like on your platform. Most of that knowledge never makes it back into detection, because turning it into a model means building, training, and maintaining ML infrastructure.
Fingerprint's Fraud Score takes those confirmed outcomes, combines them with Fingerprint device intelligence, and trains models specific to your business and the fraud types you care about. Predictions for each fraud type come back in the Fingerprint API response making it easy for you to consume and act on.
In this livestream, Keshia Rose builds the code around Fraud Score from scratch, then shows a trained model scoring new traffic.
You'll see how to:
Store Fingerprint event IDs alongside the signups, logins, and transactions you may need to label later
Turn confirmed outcomes, like chargebacks or manual review decisions, into labeled fraud data
Read Fraud Score predictions for different fraud types from the Fingerprint API response
Use those predictions to decide when to allow, review, or block
Keshia Rose
Sr. Developer Evangelist @ Fingerprintprint
Keshia Rose is a Senior Developer Evangelist at Fingerprint, where she helps developers build more secure and seamless online experiences. Through hands-on demos, technical workshops, and developer-focused content, she breaks down complex fraud prevention concepts into practical solutions teams can put into action. Her work helps developers better understand how device intelligence, risk signals, and modern fraud detection techniques can be used to identify suspicious behavior, stop abuse, and protect legitimate users without adding unnecessary friction.
SPEAKERS
Keshia Rose
Sr. Developer Evangelist @ Fingerprintprint
Keshia Rose is a Senior Developer Evangelist at Fingerprint, where she helps developers build more secure and seamless online experiences. Through hands-on demos, technical workshops, and developer-focused content, she breaks down complex fraud prevention concepts into practical solutions teams can put into action. Her work helps developers better understand how device intelligence, risk signals, and modern fraud detection techniques can be used to identify suspicious behavior, stop abuse, and protect legitimate users without adding unnecessary friction.
Keshia Rose
Sr. Developer Evangelist @ Fingerprintprint
Keshia Rose is a Senior Developer Evangelist at Fingerprint, where she helps developers build more secure and seamless online experiences. Through hands-on demos, technical workshops, and developer-focused content, she breaks down complex fraud prevention concepts into practical solutions teams can put into action. Her work helps developers better understand how device intelligence, risk signals, and modern fraud detection techniques can be used to identify suspicious behavior, stop abuse, and protect legitimate users without adding unnecessary friction.
Workshop
Dec 8
9am pt / 12pm et
Dec 8
9am pt / 12pm et
Live Build
Fraud Score, from confirmed fraud to predictions in your app
Register
We're committed to your privacy. You may unsubscribe from our communications at any time.
Register
We're committed to your privacy. You may unsubscribe from our communications at any time.
Register
We're committed to your privacy. You may unsubscribe from our communications at any time.
Turn the fraud you've already caught into predictions for the fraud you haven't
Your team already knows a lot about fraud. Every chargeback, closed-out fake account, and manual review decision is a confirmed example of what fraud looks like on your platform. Most of that knowledge never makes it back into detection, because turning it into a model means building, training, and maintaining ML infrastructure.
Fingerprint's Fraud Score takes those confirmed outcomes, combines them with Fingerprint device intelligence, and trains models specific to your business and the fraud types you care about. Predictions for each fraud type come back in the Fingerprint API response making it easy for you to consume and act on.
In this livestream, Keshia Rose builds the code around Fraud Score from scratch, then shows a trained model scoring new traffic.
You'll see how to:
Store Fingerprint event IDs alongside the signups, logins, and transactions you may need to label later
Turn confirmed outcomes, like chargebacks or manual review decisions, into labeled fraud data
Read Fraud Score predictions for different fraud types from the Fingerprint API response
Use those predictions to decide when to allow, review, or block
Keshia Rose
Sr. Developer Evangelist @ Fingerprintprint
Keshia Rose is a Senior Developer Evangelist at Fingerprint, where she helps developers build more secure and seamless online experiences. Through hands-on demos, technical workshops, and developer-focused content, she breaks down complex fraud prevention concepts into practical solutions teams can put into action. Her work helps developers better understand how device intelligence, risk signals, and modern fraud detection techniques can be used to identify suspicious behavior, stop abuse, and protect legitimate users without adding unnecessary friction.
SPEAKERS
Keshia Rose
Sr. Developer Evangelist @ Fingerprintprint
Keshia Rose is a Senior Developer Evangelist at Fingerprint, where she helps developers build more secure and seamless online experiences. Through hands-on demos, technical workshops, and developer-focused content, she breaks down complex fraud prevention concepts into practical solutions teams can put into action. Her work helps developers better understand how device intelligence, risk signals, and modern fraud detection techniques can be used to identify suspicious behavior, stop abuse, and protect legitimate users without adding unnecessary friction.
Keshia Rose
Sr. Developer Evangelist @ Fingerprintprint
Keshia Rose is a Senior Developer Evangelist at Fingerprint, where she helps developers build more secure and seamless online experiences. Through hands-on demos, technical workshops, and developer-focused content, she breaks down complex fraud prevention concepts into practical solutions teams can put into action. Her work helps developers better understand how device intelligence, risk signals, and modern fraud detection techniques can be used to identify suspicious behavior, stop abuse, and protect legitimate users without adding unnecessary friction.
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Identify your web and mobile traffic in minutes
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Start to collect visitor IDs and signals instantly for free.
