Machine learning and behavioral biometrics are emerging as powerful tools to fight fraud in online transactions. These tools are helping organizations from banks to e-commerce platforms get better at hunting down those who seek to benefit 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 difficult 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 significant when detecting fraud and which are benign. Combining behavioral biometrics and machine learning alongside rules-based systems results in the most advanced forms of online fraud detection available today.
Using CB Insights database we identified 5 cybersecurity startups to watch that are working on fighting 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 financial and non-financial 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
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 startups4. 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, 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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