Machine Learning for Fraud Prevention: What’s Next
Get a deep dive into how ML works and why it outperforms other approaches.
Fraud on listings sites comes in many forms – and it also comes with a hefty price: overloaded customer support teams, financial loss, community degradation and customer churn, legal and compliance issues, brand damage, and more.
When fraudsters post listings aimed at harming buyers, renters, and job seekers it can lead to phishing scams, stolen goods and fake jobs, or an ad farm.
If your platform enables comments or messaging, you’ve likely seen fraudsters try to take transactions offline – either to scam your users, or avoid fees.
Account takeover (ATO) – when a bad actor uses stolen credentials to access a good user's account – is among the fastest growing types of fraud. Fraudsters are eager to extract personally identifiable information and stored value from accounts, or post spam listings and comments. In 2017, ATO losses reached $5.1 billion worldwide.
Bad actors can use stolen credit cards to buy real goods – just one of the many ways you can be hit with a chargeback.
The Sift Science Digital Trust Platform uses Live Machine Learning to accurately predict which buyers, sellers, and online businesses your business can trust, and which ones it can’t, and adjust the user's payment process accordingly.
The world’s largest Digital Trust Network anonymously shares fraud patterns and analysis to generate the most accurate fraud predictions.
The Sift Science Digital Trust Platform creates a behavioral footprint for all users, separating real logins from ATO attempts in real time.
Our purpose-built ATO solution provides webhooks and APIs so that you can incorporate the Sift Score into your login flow with the flexibility you need — without adding unnecessary friction for your trusted users.
Our technology includes Live Machine Learning, natural language processing, and deep learning.
Sift Science customers can see bad content drop by 95% and spend 80% less time on manual review, letting them focus on running their business, not fraud.
Our Live Machine Learning models have examined over 16,000 signals across 12,000+ websites and mobile apps to fight fraud.
With the Sift Science Digital Trust Platform, our customers:
Maintain a high-quality experience for their buyers, sellers, employers, and job seekers by preventing spam and scam listings, comments, and other types of content abuse without having to build an army of reviewers.
Secure their user accounts, increasing customer retention and maintaining trust.
Safeguard their reputation by keeping transactions in view, rather than letting bad actors take them offline.
Integrate effortlessly with open, accessible, and easy-to-understand API documentation.
The Sift Science Digital Trust Platform helps websites and apps like Glassdoor, KSL, and Everything but the House effectively mitigate fraud and elevate the user experience, allowing good listings to flow on their platforms. Your business will thrive once users know they’ll have a trustworthy and abuse-free experience.
Wanelo is a unique shopping experience targeting Generation Z that is about community and conversation as much as it is about buying.
Mercari is a mobile peer-to-peer e-commerce marketplace that has been experiencing massive growth in the United States.
KSL.com is a Utah-focused website that offers local news reporting, classified ads, job postings, a local business directory, a deals platform, and other online services.