Signifyd

Signifyd

Artificial Intelligence/Machine Learning | RegTech | Authentication and Fraud Prevention
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  • FOUNDING YEAR: 2011
    LOCATION:
    United States
    REGION:
    California
    Last Update:
    9 Sep, 2019

Description

Signifyd (formerly known as Trusted Patterns) is an e-commerce fraud-prevention startup that analyzes online transactions to detect fraudulent activities. The platform leverages machine learning algorithms along with behavioral technology and data science research to safeguard online retailers against the risks of fraud. It also helps the merchant to prevent credit card chargebacks, thereby reducing loss associated with fraud. Signifyd was listed in Forbes' FinTech 50 and Bloomberg’s 50 Most Promising Startups. The company was also listed as one of Inc. Magazine’s Best Workplaces for 2018.

FOCUS

PRODUCTS & SERVICES:
Signifyd offers a guaranteed fraud protection solution for online merchants which helps them to reduce fraud and chargebacks. In case of a fraudulent chargeback, it pays back the chargeback fees to the merchant within 48 hours. It leverages machine learning to automate fraud prevention that enables businesses to reduce risk and increase sales at the same time. Signifyd can be integrated on the existing e-commerce platform through an API.
REVENUE MODEL:
Viewable by subscribers only
MAIN CLIENTS:
The company has major customers from e-commerce industry. Some of its customers: Makerbot, chegg, Zentail, Misfit, Zepp, build.com, HSN, Lumo Lift, Isaora, Wayfair, Petflow, Areatrend, Haven, Netaya, GearX.com, Lensrentals, UBIQ, Lumoid, Roden Gray, Campsaver, Pure Fix, Greats, Progenex, iRobot, Stance, Wayfair, etc.
EXISTING PARTNERSHIPS:
Some of its partners; Salesforce Commerce Cloud, Magento, Accertify, ThreatMetrix, Shopify, Collins Harper, Praxis, etc.
COMMENT
Signifyd uses machine learning algorithms to analyze thousands of data points from various data providers to verify orders in real-time. Signifyd automates back-office operations including auto-fulfill and auto-cancel on orders and makes it easy for merchants to get paid for chargebacks.

PEOPLE

FOUNDERS:
Michael Liberty, Rajesh Ramanand
Board Members and Advisors:
Jai Choi, Maria Thomas, Ohad Samet, Safa Rashtchy, Tim Eades
NUMBER OF EMPLOYEES:
51-200
INVESTORS:
Bain Capital Ventures, Menlo Ventures, American Express, IA Ventures, AllegisCyber, Resolute Ventures, TriplePoint Capital, QED Investors, Tekton Ventures, Lucas Venture Group, Andreessen Horowitz, Data Collective, OVO Fund, Streamlined Ventures, BYU Cougar Capital, Thinkplus, FEP Fund, Bill McKiernan, Timothy Eades, Premji Invest
KEY EMPLOYEES:
Rajesh Ramanand (CEO); Chendong Zou (Head of Engineering); Johannes Fischli (Head of Business Operations); Vahe Amirbekian (Head of Risk Product); Skye Spear (Head of Sales & Partnerships)

TRACTION

VALUATION / MARKET CAP:
$400 MM in 2018
VALUATION COMMENT
Third-party sources claims that the company's valuation is around $400M to $600M.
REVENUE:
$10M+ in 2017
REVENUE COMMENT:
Viewable by subscribers only
TRANSACTIONS COMMENT:
The company claims that it has protected sales orders worth $9.1B till date. (May 2018)
ACTIVE USERS COMMENT:
The platform is being used by more than 10,000 e-commerce merchants. (May 2018)
OTHER INFO:
The fraud detection and prevention market is estimated to reach nearly $42B by 2022.
COMMENT:
The company is planning to open an office in Barcelona in next year to expand its operations in Europe.

MOMENTUM

FUNDED:
YES
TOTAL FUNDING:
$187 Million
DATE OF LAST FUNDING:
May 2018
TOTAL FUNDING COMMENT:
The company is planning to use its funds to further accelerate its growth with global enterprise and omnichannel retailers to provide friction-free e-commerce to their customers. (May 2018)
SEED & PRIOR FUNDING:
$4.2 Millions
SEED CLOSED ON:
Jan 2013
SEED PRIOR FUNDING COMMENT:
Cumulative funding indicated
FUNDING SERIES:
SERIES A
$7 Millions Closed: Apr 2014
SERIES B
$39 Millions Closed: Sep 2016
SERIES C
$56 Millions Closed: May 2017
SERIES D
$100 Million Closed: May 2018
LISTED:
NO
COMMENT:
There is a mismatch between total funding amount and overall funding rounds raised by the company.

LATEST RESEARCH


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