Forter raises $300M on a $3B valuation to combat e-commerce fraud

E-commerce is on the rise, but that also means the risk, and occurrence, of e-commerce fraud is, too. Now, Forter, one of the startups building a business to tackle that malicious activity, has closed $300 million in funding — a sign both of the size of the issue, and its success in tackling it to date.

The new funding, a Series F, values Forter at $3 billion — notable not least because the funding is coming only about six months since Forter’s previous round, a $125 million Series E that valued it at over $1.3 billion.

Tiger Global Management is leading this latest equity infusion, with new backers Third Point Ventures and Adage Capital Management, and existing investors Bessemer Venture Partners, Sequoia Capital, March Capital, NewView Capital, Salesforce Ventures and Scale Venture Partners, also involved.

The plan will be to use to the money to expand Forter — founded in Tel Aviv and now based in New York — geographically, to bring more functionality into its product, and to look at adjacent areas where Forter might expand its capabilities, either organically or by way of acquisition.

Forter today focuses mainly on identifying fraud at the point of transaction and building an AI-based platform that “learns” more behaviors to improve its accuracy; it also builds models that keep more people transacting and helps bring down the number of “false positives” where activity that appears suspicious actually is not.

One area on its roadmap for expansion is remediation after the fraud occurs, said Liron Damri, Forter’s co-founder and president.

“Our vision is to serve the merchant as the go-to trusted partner for everything, so remediation is definitely on our roadmap,” he said of potential acquisition targets.

Damri, who co-founded the company with Michael Reitblat, CEO, and Alon Shemesh, chief analyst, said in an interview that the startup — which works with some 350 large customers like Priceline and Instacart and a growing number of service providers like FreedomPay and Flutterwave, altogether seeing some $250 billion dollars worth of transactions globally last year — wasn’t proactively looking for more money.

“All we wanted to do was go back to run the company,” he said. “But in the past six months we’ve seen such a great momentum, doubling revenue and ARR, and seeing our customer volumes grow.”

That led to a lot of investors proactively reaching out and ask questions, he continued. He described Tiger as a “kingmaker” in the category of e-commerce, so it was an easy decision to make, and gave it the “gas” it needed to take its next growth steps.

E-commerce has been one of the major technology growth stories of the last year, fueled by a rush of consumers and businesses playing out their lives online at a time when it has been harder, and in some cases impossible, to transact in person.

While we have definitely seen a lot of growth, and growing sophistication, in the number of tools on the market to combat cybercrime, it’s in some ways an ouroboros of a problem: the more transactions that are made, the more there are that need to be monitored for suspicious activity. And in any case fraud in e-commerce is not exactly going away. It’s estimated that it will cost retailers some $20 billion in 2021 and is always on the rise.

Forter got its start in 2013 focusing first on monitoring activity on sites wherever customers happened to be to identify suspicious behavior — a sign that it might be a bot or someone on an illicit spending spree racking up a lot of items in quick succession — with the bigger concept being to build a network of activity from which to learn and help make more informed decisions over time.

In more recent years, the essence of the issue has expanded somewhat, and also grown more sophisticated. As companies have grown their businesses to reach beyond early adopters and core audiences, and into a more “omnichannel” environment beyond basic check-outs on their own sites, so too have the kinds of consumers coming to shop.

This has meant that traditional “signals” of legitimate buyers no longer were the same as before — a predicament that really rose in profile in the last year, as many newcomers came to e-commerce for the first time during the pandemic. In fact, Damri told me that in 2020 there were seven times more “newcomers” to sites than in 2019, huge growth of that segment.

So with most of the flagging of suspicious activity coming up at the point of transaction, Forter expanded to analyzing activity there.

As with a recent acquisition of Stripe’s, Bouncer, to build out its own anti-fraud product, a large part of Forter’s attention these days is on providing tools to companies to identify suspicious purchasing, but even more than that, to make sure that the many occasions that might look suspicious are not, to help reduce the amount of “cart abandonment” and increase conversions.

The old way of doing things, Damri said, involved “thousands of rules and applying suspicion on everyone. You were guilty unless proved otherwise.”

Using its AI engine and a some risk analysis (not unlike the kind that, say, an insurance or loan provider might apply in their businesses), Forter turned the proposition on its head.

“We wanted to approve as much as possible. We wanted to gradually increase the trust you have of your own customers. We changed the sentiment and approach… especially in areas that were neglected, such as those who saw significant changes in life,” Damri said. “This was extremely important as Covid-19 hit.”

Forter’s risk tolerance model, it seems, has so far proven out. Damri said that its algorithms applied reduce the total number of declines by 80%, but also reduce the number of chargebacks — one indicator of a mistake — by 60%.

This implies that it’s blocking more of the “wrong” kind of purchases, and letting through more of the legitimate ones.  (That is, he pointed out, in addition to a few bad actors Forter intentionally lets buy things, just to learn how they operate. Damri referred to this as “paid-tuition.”)

Risk-based approvals, coupled with algorithms to learn what is truly bad, has resonated with customers, and investors.

“With the unprecedented rate of digital transformation and the fierce competition in creating the slickest user experience, superior fraud prevention plays an ever more critical role in e-commerce revenue growth” said John Curtius, a partner at Tiger Global Management, in a statement. “After we talked with dozens of customers of every relevant solution in this space, it was very clear to us that Forter is the clear leader in performance and scale.”

“As a longtime investor, it’s been incredible to see Forter’s ascent,” added Ravi Viswanathan, NewView Capital. “It’s a testament to the leadership team’s vision and execution in allowing merchants to provide the seamless experiences customers expect and to be able to accept as many transactions as possible, while still accurately identifying and blocking fraud.”


By Ingrid Lunden

Shift Technology raises $220M at a $1B+ valuation to fight insurance fraud with AI

While insurance providers continue to get disrupted by startups like Lemonade, Alan, Clearcover, Pie and many others applying tech to rethink how to build a business around helping people and companies mitigate against risks with some financial security, one issue that has not disappeared is fraud. Today, a startup out of France is announcing some funding for AI technology that it has built for all insurance providers, old and new, to help them detect and prevent it.

Shift Technology, which provides a set of AI-based SaaS tools to insurance companies to scan and automatically flag fraud scenarios across a range of use cases — they include claims fraud, claims automation, underwriting, subrogation detection and financial crime detection — has raised $220 million, money that it will be using both to expand in the property and casualty insurance market, the area where it is already strong, as well as to expand into health, and to double down on growing its business in the U.S. It also provides fraud detection for the travel insurance sector.

This Series D is being led Advent International, via Advent Tech, with participation from Avenir and others. Accel, Bessemer Venture Partners, General Catalyst, and Iris Capital — who were all part of Shift’s Series C led by Bessemer in 2019 — also participated. With this round, Paris and Boston-based Shift Technology has now raised some $320 million and has confirmed that it is now valued at over $1 billion.

The company currently has around 100 customers across 25 different countries — with customers including Generali France and Mitsui Sumitomo — and says that it has already analyzed nearly two billion claims, data that’s feeding its machine learning algorithms to improve how they work.

The challenge (or I suppose, opportunity) that Shift is tackling, however, is much bigger. The Coalition Against Insurance Fraud, a non-profit in the U.S., estimates that at least $80 billion of fraudulent claims are made annually in the U.S. alone, but the figure is likely significantly higher. One problem has, ironically, been the move to more virtualized processes, which open the door to malicious actors exploiting loopholes in claims filing and fudging information.

Shift is also not alone in tackling this issue: the market for insurance fraud detection globally was estimated to be worth $2.5 billion in 2019 and projected to be worth as much as $8 billion by 2024.

In addition to others in claims management tech such as Brightcore and Guidewire, many of the wave of insuretech startups are building in their own in-house AI-based fraud protection, and it’s very likely that we’ll see a rise of other fraud protection services, built out of fintech to guard against financial crime, making their way to insurance, as the mechanics of how the two work and the compliance issues both face are very closely aligned.

“The entire Shift team has worked tirelessly to build this company and provide insurers with the technology solutions they need to empower employees to best be there for their policyholders. We are thrilled to partner with Advent International, given their considerable sector expertise and global reach and are taking another giant step forward with this latest investment,” stated Jeremy Jawish, CEO and co-founder, Shift Technology, in a statement. “We have only just scratched the surface of what is possible when AI-based decision automation and optimization is applied to the critical processes that drive the insurance policy lifecycle.”

For its backers, one key point with Shift is that it’s helping older providers bring on more tools and services that can help them improve their margins as well as better compete against the technology built by newer players.

“Since its founding in 2014, Shift has made a name for itself in the complex world of insurance,” said Thomas Weisman, an Advent director, in a statement. “Shift’s advanced suite of SaaS products is helping insurers to reshape manual and often time-consuming claims processes in a safer and more automated way. We are proud to be part of this exciting company’s next wave of growth.”


By Ingrid Lunden

Feedzai raises $200M at a $1B+ valuation for AI tools to fight financial fraud

On the heels of Jumio announcing a $150 million injection this week to continue building out its AI-based ID verification and anti-money laundering platform, another startup in the space is levelling up. Feedzai, which provides banks, others in the financial sector, and any company managing payments online with AI tools to spot and fight fraud — its cornerstone service involves super quick (3 millisecond) checks happening in the background while transactions are being made — has announced a Series D of $200 million. It said that the new financing is being made at a valuation of over $1 billion.

The round is being led by KKR, with Sapphire Ventures and strategic backer Citi Ventures — both past investors — also participating. Feedzai said it will be using the funds for further R&D and product development, to expand into more markets outside the U.S. — it was originally founded in Portugal but now is based out of San Mateo — and towards business development, specifically via partnerships to integrate and sell its tools.

One of those partners looks to be Citi itself:

“Citi is committed to advancing global payments anchored on transparency, efficiency, and control, and our partnership with Feedzai is allowing us to provide customers with technology that seamlessly balances agility and security,” said Manish Kohli, Global Head of Payments and Receivables, with Citi’s Treasury and Trade Solutions, in a statement.

The funding is coming at a time when the need for fraud protection for those managing transactions online has reached a high watermark, leading to a rush of customers for companies in the field.

Feezai says that its customers include 4 of the 5 largest banks in North America, 80% of the world’s Fortune 500 companies, 154 million individual and business taxpayers in the U.S., and has processed $9 billion in online transactions for 2 of the world’s most valuable athletic brands. In total its reach covers some 800 million customers of businesses that use its services.

In addition to Citibank, its customers include Fiserv, Santander, SoFi, and Standard Chartered’s Mox.

The round comes nearly four years after Feedzai raised its Series C, a $50 million round led by an unnamed investor and with an undisclosed valuation. Sapphire also participated in that round.

While money laundering, fraud and other kinds of illicit financial activity were already problems then, in the interim, the problem has only compounded, not least because of how much activity has shifted online, accelerating especially in the last year of pandemic-driven lockdowns. That’s been exacerbated also by a general rise in cybercrime — of which financial fraud remains the biggest component and motivator.

Within that bigger trend, solutions based on artificial intelligence have really emerged as critical to the task of identifying and fighting those illicit activities. Not only is that because AI solutions are able to make calculations and take actions and simply process more than non-AI based tools, or humans for that matter, but they are then able to go head to head with much of the fraud taking place, which itself is being built out on AI-based platforms and requires more sophistication to identify and combat.

For banking customers, Feedzai’s approach has been disruptive in part because of how it has conceived of the problem: it has built solutions that can be used across different scenarios, making them more powerful since the AI system is subsequently “learning” from more data. This is in contrast to how many financial service providers had conceived and tackled the issue in the past.

“Until now banks have used solutions based on verticals,” Nuno Sebastiao, co-founder and CEO of Feedzai, said in the past to TechCrunc. “The fraud solution you have for an ATM wouldn’t be the same fraud solution you would use for online banking which wouldn’t be the same fraud solution would have for a voice call center.” As these companies have refreshed their systems, many have taken a more agnostic approach like the kind the Feedzai has built.

The scale of the issue is clear, and unfortunately also something many of us have experienced first-hand. Feedzai says its data indicates that the last quarter of 2020 that show consumers saw a 650% increase in account takeover scams, a 600% in impersonation scams, and a 250% increase in online banking fraud attacks versus the first quarter of 2020.  (Those periods are, essentially, before pandemic and during pandemic comparisons.)

“The past 12 months have accelerated the world’s dependency on electronic financial services – from online banking to mobile payments, and in turn have increased fraud and money laundering activity. Our services are in more demand than ever,” said Sebastiao in a statement today.

Indeed, yesterday, when I covered Jumio’s $150 million round, I said I wouldn’t consider its funding to be an outlier (even though Jumio made clear it was the largest funding to date in its space): the fast follow from Feedzai, with an even higher amount of financing, really does underscore the trend at the moment.

In addition to these two, one of Feedzai’s biggest competitors, Kount, was acquired by credit ratings giant Equifax earlier this year for $640 million to move deeper into the space. (And related to that field, in the area of identity management, which goes hand-in-hand with tools for laundering and fraud, Okta acquired Auth0 for $6.5 billion.)

Other big rounds for startups in the wider space have included included ForgeRock ($96 million round), Onfido ($100 million), Payfone ($100 million), ComplyAdvantage ($50 million), Ripjar ($36.8 million) Truework ($30 million), Zeotap ($18 million) and Persona ($17.5 million).

KKR’s involvement in this round is notable as another example of a private equity firm getting in earlier with venture rounds with fast-scaling startups, similar to Great Hill’s investment in Jumio yesterday and a number of other examples. The firm says it’s making this investment out of its Next Generation Technology Growth Fund II, which is focused on making growth equity investment opportunities in the technology space.

“Feedzai offers a powerful solution to one of the biggest challenges we are facing today: financial crime in the digital age. Global commerce depends on future-proof technologies capable of dealing with a rapidly evolving threat landscape. At the same time, consumers rightfully demand a great customer experience, in addition to strong security layers when using banking or payments services,” said Stephen Shanley, Managing Director at KKR, in a statement

“We believe Feedzai’s platform uniquely meets these expectations and more, and we are looking forward to working with Nuno and the rest of the team to expand their offering even further,” added Spencer Chavez, Principal at KKR.


By Ingrid Lunden

Quantexa raises $64.7M to bring big data intelligence to risk analysis and investigations

The wider field of cyber security — not just defending networks, but identifying fraudulent activity — has seen a big boost in activity in the last few months, and that’s no surprise. The global health pandemic has led to more interactions and transactions moving online, and the contractions we’re feeling across the economy and society have led some to take more desperate and illegal actions, using digital challenges to do it.

Today, a UK company called Quantexa — which has built a machine learning platform branded “Contextual Decision Intelligence” (CDI) that analyses disparate data points to get better insight into nefarious activity, as well as to (more productively) build better profiles of a company’s entire customer base — is raising a growth round of funding to address that opportunity.

The London-based startup has picked up $64.7 million, a Series C the it will be using to continue building out both its tools and the use cases for applying them, as well as expanding geographically, specifically in North America, Asia-Pacific and more European territories.

The mission, said Vishal Marria, Quantexa’s founder and CEO, is to “connect the dots to make better business decisions.”

The startup built its business on the back of doing work for major banks and others in the financial services sector, and Marria added that the plan will be to continue enhancing tools for that vertical while also expanding into two growing opportunities: working with insurance and government/public sector organizations.

The backers in this round speak to how Quantexa positions itself in the market, and the traction it’s seen to date for its business. It’s being led by Evolution Equity Partners — a VC that specialises in innovative cybersecurity startups — with participation also from previous backers Dawn Capital, AlbionVC, HSBC and Accenture, as well as new backers ABN AMRO Ventures. HSBC, Accenture and ABN AMRO are all strategic investors working directly with the startup in their businesses.

Altogether, Quantexa has “thousands of users” across 70+ countries, it said, with additional large enterprises including Standard Chartered, OFX and Dunn & Bradstreet.

The company has now raised some $90 million to date, and reliable sources close to the company tell us that the valuation is “well north” of $250 million — which to me sounds like it’s between $250 million and $300 million.

Marria said in an interview that he initially got the idea for Quantexa — which I believe may be a creative portmanteau of “quantum” and “context” — when he was working as an executive director at Ernst & Young and saw “many challenges with investigations” in the financial services industry.

“Is this a money launderer?” is the basic question that investigators aim to answer, but they were going about it, “using just a sliver of information,” he said. “I thought to myself, this is bonkers. There must be a better way.”

That better way, as built by Quantexa, is to solve it in the classic approach of tapping big data and building AI algorithms that help, in Marria’s words, connect the dots.

As an example, typically, an investigation needs to do significantly more than just track the activity of one individual or one shell company, and you need to seek out the most unlikely connections between a number of actions in order to build up an accurate picture. When you think about it, trying to identify, track, shut down and catch a large money launderer (a typical use case for Quantexa’s software) is a classic big data problem.

While there is a lot of attention these days on data protection and security breaches that leak sensitive customer information, Quantexa’s approach, Marria said, is to sell software, not ingest proprietary data into its engine to provide insights. He said that these days deployments typically either are done on premises or within private clouds, rather than using public cloud infrastructure, and that when Quantexa provides data to complement its customers’ data, it comes from publicly available sources (for example Companies House filings in the UK).

There are a number of companies offering services in the same general area as Quantexa. They include those that present themselves more as business intelligence platforms that help detect fraud (such as Looker) through to those that are secretive and present themselves as AI businesses working behind the scenes for enterprises and governments to solve tough challenges, such as Palantir, through to others focusing specifically on some of the use cases for the technology, such as ComplyAdvantage and its focus on financial fraud detection.

Marria says that it has a few key differentiators from these. First is how its software works at scale: “It comes back to entity resolution that [calculations] can be done in real time and at batch,” he said. “And this is a platform, software that is easily deployed and configured at a much lower total cost of ownership. It is tech and that’s quite important in the current climate.”

And that is what has resonated with investors.

“Quantexa’s proprietary platform heralds a new generation of decision intelligence technology that uses a single contextual view of customers to profoundly improve operational decision making and overcome big data challenges,” said Richard Seewald, founding and managing partner of Evolution, in a statement. “Its impressive rapid growth, renowned client base and potential to build further value across so many sectors make Quantexa a fantastic partner whose team I look forward to working with.” Seewald is joining the board with this round.


By Ingrid Lunden

DoJ charges Autonomy founder with fraud over $11BN sale to HP

UK entrepreneur turned billionaire investor, Mike Lynch, has been charged with fraud in US over the 2011 sale of his enterprise software company.

Lynch sold Autonomy, the big data company he founded back in 1996, to computer giant HP for around $11BN some seven years ago.

But within a year around three-quarters of the value of the business had been written off, with HP accusing Autonomy’s management of accounting misrepresentations and disclosure failures.

Lynch has always rejected the allegations, and after HP sought to sue him in UK courts he countersued in 2015.

Meanwhile the UK’s own Serious Fraud Office dropped an investigation into the Autonomy sale in 2015 — finding “insufficient evidence for a realistic prospect of conviction”.

But now the DoJ has filed charges in a San Francisco court, accusing Lynch and other senior Autonomy executives of making false statement that inflated the value of the company.

They face 14 counts of conspiracy and fraud, according to Reuters — a charge which carries a maximum penalty of 20 years in prison.

We’ve reached out to Lynch’s fund, Invoke Capital, for comment on the latest development.

The BBC has obtained a statement from his lawyers, Chris Morvillo of Clifford Chance and Reid Weingarten of Steptoe & Johnson, which describes the indictment as “a travesty of justice”.

The statement also claims Lynch is being made a scapegoat for HP’s failures, framing the allegations as a business dispute over the application of UK accounting standards. 

Two years ago we interviewed Lynch on stage at TechCrunch Disrupt London and he mocked the morass of allegations still swirling around the acquisition as “spin and bullshit”.

Following the latest developments, the BBC reports that Lynch has stepped down as a scientific adviser to the UK government.

“Dr. Lynch has decided to resign his membership of the CST [Council for Science and Technology] with immediate effect. We appreciate the valuable contribution he has made to the CST in recent years,” a government spokesperson told it.


By Natasha Lomas

Riskified prevents fraud on your favorite e-commerce site

Meet Riskified, an Israel-based startup that has raised $64 million in total to fight online fraud. The company has built a service that helps you reject transactions from stolen credit cards and approve more transactions from legitimate clients.

If you live in the U.S., chances are you know someone who noticed a fraudulent charge for an expensive purchase with their credit card — it’s still unclear why most restaurants and bars in the U.S. take your card away instead of bringing the card reader to you.

Online purchases, also known as card-not-present transactions, represent the primary source of fraudulent transactions. That’s why e-commerce websites need to optimize their payment system to detect fraudulent transactions and approve all the others.

Riskified uses machine learning to recognize good orders and improve your bottom line. In fact, Riskified is so confident that it guarantees that you’ll never have to pay chargebacks. As long as a transaction is approved by the product, the startup offers chargeback protection. If Riskified made the wrong call, the company reimburses fraudulent chargebacks.

On the other side of the equation, many e-commerce websites leave money on the table by rejecting transactions and false declines. It’s hard to quantify this as some customers end up not ordering anything. Riskified should help you on this front too.

The startup targets big customers — Macy's, Dyson, Prada, Vestiaire Collective and GOAT are all using it. You can integrate Riskified with popular e-commerce payment systems and solutions, such as Shopify, Magento and Stripe. Riskified also has an API for more sophisticated implementations.


By Romain Dillet

Stripe debuts Radar anti-fraud AI tools for big businesses, says it has halted $4B in fraud to date

Cybersecurity continues to be a growing focus and problem in the digital world, and now Stripe is launching a new paid product that it hopes will help its customers better battle one of the bigger side-effects of data breaches: online payment fraud. Today, Stripe is announcing Radar for Fraud Teams, an expansion of its free AI-based Radar service that runs alongside Stripe’s core payments API to help identify and block fraudulent transactions.

And there are further efforts that Stripe is planning in coming months. Michael Manapat, Stripe’s engineering manager for Radar and machine learning, said the company is going to soon launch a private beta of a “dynamic authentication” that will bring in two-factor authentication and start to see Stripe’s first forays into considering how to implement biometric factors in payments. Fingerprints and other physical attributes have become increasingly popular ways to identify mobile and other users.

The initial iteration of Radar launched in October 2016, and since then, Manapat tells me that it has prevented $4 billion in fraud for its “hundreds of thousands” of customers.

Considering the wider scope of how much e-commerce is affected by fraud — one study estimates $57.8 billion in e-commerce fraud across eight major verticals in a one-year period between 2016 and 2017 — this is a decent dent, but there is a lot more work to be done. And Stripe’s position of knowing four out of every five payment card numbers globally (on account of the ubiquity of its payments API) gives it a strong position to be able to tackle it.

The new paid product comes alongside an update to the core, free product that Stripe is dubbing Radar 2.0, which Stripe claims will have more advanced machine learning built into it and can therefore up its fraud detection by some 25 percent over the previous version.

New features for the whole product (free and paid) will include being able to detect when a proxy VPN is being used (which fraudsters might use to appear like they are in one country when they are actually in another) and ingesting billions of data points to train its model, which is now being updated on a daily basis automatically — itself an improvement on the slower and more manual system that Manapat said Stripe has been using for the past couple of years.

Meanwhile, the paid product is an interesting development.

At the time of the original launch, Stripe co-founder John Collison hinted that the company would be considering a paid product down the line. Stripe has said multiple times that it’s in no rush to go public — and statement that a spokesperson reiterated this week — but it’s notable that a paid tier is a sign of how Stripe is slowly building up more monetization and revenue generation.

Stripe is valued at around $9.2 billion as of its last big round in 2016. Most recently, it raised $150 million back in that November 2016 round. A $44 million from March of this year, noted in Pitchbook, was actually related to issuing stock related to its quiet acquisition of point-of-sale payments startup Index in that month — incidentally another interesting move for Stripe to expand its position and placement in the payments ecosystem. Stripe has raised around $450 million in total.

The Teams product, aimed at businesses that are big enough to have dedicated fraud detection staff, will be priced at an additional $0.02 per transaction, on top of Stripe’s basic transaction fees of a 2.9 percent commission plus 30 cents per successful card charge in the U.S. (fees vary in other markets).

The chief advantage of taking the paid product will be that teams will be able to customise how Radar works with their own transactions.

This will include a more complete set of data for teams that review transactions, and a more granular set of tools to determine where and when sales are reviewed, for example based on usage patterns or the size of the transaction. There are already a set of flags the work to note when a card is used in frequent succession across disparate geographies; but Manapat said that newer details such as analysing the speed at which payment details are entered and purchases are made will now also factor into how it flags transactions for review.

Similarly, teams will be able to determine the value at which a transaction needs to be flagged. This is the online equivalent of when certain purchases require or waive you to enter a PIN or provide a signature to seal the deal. (And it’s interesting to see that some e-commerce operations are potentially allowing some dodgy sales to happen simply to keep up the user experience for the majority of legitimate transactions.)

Users of the paid product will also be able to now use Radar to help with their overall management of how it handles fraud. This will include being able to keep lists of attributes, names and numbers that are scrutinised, and to check against them with analytics also created by Stripe to help identify trending issues, and to plan anti-fraud activities going forward.

Updated with further detail about Stripe’s funding.