This document summarizes 5 startups that are using machine learning and behavioral biometrics to fight fraud. It provides details on each startup such as their funding amounts, investors, and business models. Some key points are that behavioral biometrics can identify users through unconscious behaviors, machine learning is used to analyze vast amounts of user data to detect fraud patterns, and the featured startups provide fraud detection and prevention platforms to help protect companies from online fraud.

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RESEARCH BRIEF
March 28, 2017
5 Startups Using Machine Learning And Behavioral
Biometrics To Fight Fraud
Arti cial Intelligence Cybersecurity Trends
   
Accel Partners backed two of the fraud- ghting companies
featured below.
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Machine learning and behavioral biometrics are emerging as powerful tools to ght fraud in online
transactions. These tools are helping organizations from banks to e-commerce platforms get
better at hunting down those who seek to bene t from faking their identity online, allowing them to
move beyond pre-set triggers for identifying fraud and incorporating machine learning software
that can sift through vast amounts of data faster than human analysts.
Behavioral biometrics use unconscious behaviors such as typing rhythm or browsing speed to
verify a user’s identity. These types of behaviors are very di cult for fraudsters to fake, but to
identify fraud using these parameters, computers must sift through a vast amount of data.
Applying machine learning software to biometric behavioral data means systems can learn which
data points from a collection of millions are signi cant when detecting fraud and which are
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benign. Combining behavioral biometrics and machine learning alongside rules-based systems
results in the most advanced forms of online fraud detection available today.
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Using CB Insights database we identi ed 5 cybersecurity startups to watch that are working on
ghting fraud with a mix of behavioral biometrics and machine learning. We selected these private
companies based primarily on CB Insights’ Mosaic scoring algorithm, which uses nancial and
non- nancial signals to assess the health of private companies.
1. RAVELIN
Ravelin offers a fraud detection and prevention platform that allows organizations that rely on
online payments to automatically examine customer behavior in real-time and identify fraudsters
before they do damage.
Total Disclosed Funding: $5.72M
Latest Funding Round: $3.6M Series A
Select Investors: Passion Capital, Amadeus Capital, Playfair Capital
2. SIMILITY
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TRACK ALL THE CYBERSECURITY STARTUPS IN THIS BRIEF AND MANY MORE ON
OUR PLATFORM.
Startups working on cybersecurity are gaining much more visibility as cyber threats increase. Sign
up for a free trial and look for Cybersecurity in the Collections tab.
Simility offers a fraud prevention platform which combines machine learning and data
visualization technology with a rules engine to help protect enterprises from fraud.
Total Disclosed Funding: $7.2M
Latest Funding Round: $1.5M Seed
Select Investors: Accel Partners, Array Ventures, Trinity Ventures, The Valley Fund
3. SHIFT TECHNOLOGY
Shift Technology is a SaaS company designed to detect potential insurance fraud. The company’s
software uses mathematical modelling and machine learning algorithms to help detect fraudulent
claims.
Total Disclosed Funding: $11.7M
Latest Funding Round: $10M Series A
Select Investors: Iris Capital, Accel Partners, LumenLab

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Track cybersecurity startups
4. SOCURE
Socure’s social biometrics solution helps organizations detect fraudulent users on websites and in
mobile applications using machine learning algorithms.
Total Disclosed Funding: $19.4M
Latest Funding Round: $13M Series A
Select Investors: Two Sigma Ventures, Santander InnoVentures, ff Venture Capital
5. SIFT SCIENCE
Sift Science provides machine learning software that automatically learns and detects fraudulent
behavioral patterns, alerting businesses before they or their customers are defrauded. Beyond this,
CYBERSECURITY STARTUPS
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the company has also launched a new set of products designed to detect and mitigate additional
types of fraud and abuse.
Total Disclosed Funding: $53.5M
Latest Funding Round: $30M Series C
Select Investors: Y Combinator, Union Square Ventures, Spark Capital
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