As financial crime has become significantly more sophisticated, so too have the tools that are used to combat it. Now, Quantexa — one of the more interesting startups that has been building AI-based solutions to help detect and stop money laundering, fraud, and other illicit activity — has raised a growth round of $153 million, both to continue expanding that business in financial services and to bring its tools into a wider context, so to speak: linking up the dots around all customer and other data.

“We’ve diversified outside of financial services and working with government, healthcare, telcos and insurance,” Vishal Marria, its founder and CEO, said in an interview. “That has been substantial. Given the whole journey that the market’s gone through in contextual decision intelligence as part of bigger digital transformation, was inevitable.”

The Series D values the London-based startup between $800 million and $900 million on the heels of Quantexa growing its subscriptions revenues 108% in the last year.

Warburg Pincus led the round, with existing backers Dawn Capital, AlbionVC, Evolution Equity Partners (a specialist cybersecurity VC), HSBC, ABN AMRO Ventures and British Patient Capital also participating. The valuation is a significant hike up for Quantexa, which was valued between $200 million and $300 million in its Series C last July. It has now raised over $240 million to date.

Quantexa got its start out of a gap in the market that Marria identified when he was working as a director at Ernst & Young tasked with helping its clients with money laundering and other fraudulent activity. As he saw it, there were no truly useful systems in the market that efficiently tapped the world of data available to companies — matching up and parsing both their internal information as well as external, publicly available data — to get more meaningful insights into potential fraud, money laundering and other illegal activities quickly and accurately.

Quantexa’s machine learning system approaches that challenge as a classic big data problem — too much data for a humans to parse on their own, but small work for AI algorithms processing huge amounts of that data for specific ends.

Its so-called “Contextual Decision Intelligence” models (the name Quantexa is meant to evoke “quantum” and “context”) were built initially specifically to address this for financial services, with AI tools for assessing risk and compliance and identifying financial criminal activity, leveraging relationships that Quantexa has with partners like Accenture, Deloitte, Microsoft and Google to help fill in more data gaps.

The company says its software — and this, not the data, is what is sold to companies to use over their own datasets — has handled up to 60 billion records in a single engagement. It then presents insights in the form of easily digestible graphs and other formats so that users can better understand the relationships between different entities and so on.

Today, financial services companies still make up about 60% of the company’s business, Marria said, with 7 of the top 10 UK and Australian banks and 6 of the top 14 financial institutions in North America among its customers. (The list includes its strategic backer HSBC, as well as Standard Chartered Bank and Danske Bank.)

But alongside those — spurred by a huge shift in the market to relying significantly more on wider data sets, to businesses updating their systems in recent years, and the fact that, in the last year, online activity has in many cases become the “only” activity — Quantexa has expanded more significantly into other sectors.

“The Financial crisis [of 2007] was a tipping point in terms of how financial services companies became more proactive, and I’d say that the pandemic has been a turning point around other sectors like healthcare in how to become more proactive,” Marria said. “To do that you need more data and insights.”

So in the last year in particular, Quantexa has expanded to include other verticals facing financial crime, such as healthcare, insurance, government (for example in tax compliance), and telecoms/communications, but in addition to that, it has continued to diversify what it does to cover more use cases, such as building more complete customer profiles that can be used for KYC (know your customer) compliance or to serve them with more tailored products. Working with government, it’s also seeing its software getting applied to other areas of illicit activity, such as tracking and identifying human trafficking.

In all, Quantexa has “thousands” of customers in 70 markets. Quantexa cites figures from IDC that estimate the market for such services — both financial crime and more general KYC services — is worth about $114 billion annually, so there is still a lot more to play for.

“Quantexa’s proprietary technology enables clients to create single views of individuals and entities, visualized through graph network analytics and scaled with the most advanced AI technology,” said Adarsh Sarma, MD and co-head of Europe at Warburg Pincus, in a statement. “This capability has already revolutionized the way KYC, AML and fraud processes are run by some of the world’s largest financial institutions and governments, addressing a significant gap in an increasingly important part of the industry. The company’s impressive growth to date is a reflection of its invaluable value proposition in a massive total available market, as well as its continued expansion across new sectors and geographies.”

Interestingly, Marria admitted to me that the company has been approached by big tech companies and others that work with them as an acquisition target — no real surprises there — but longer term, he would like Quantexa to consider how it continues to grow on its own, with an independent future very much in his distant sights.

“Sure, an acquisition to the likes of a big tech company absolutely could happen, but I am gearing this up for an IPO,” he said.

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his might seem a particularly bad time to be investing in big tech.

President Trump said Tuesday morning that his administration would look into accusations that Google has been secretly working with the Chinese military. The charge came from Peter Thiel, a PayPal (PYPL – Get Report) co-founder and strong supporter of the president.

On the other hand, Bloomberg reported Tuesday that DeepMind, the artificial-intelligence arm of Alphabet,  (GOOGL – Get Report)  might be on the cusp of a major breakthrough in the way new drugs are discovered.

It’s an important innovation. It’s hiding inside the search giant. And it couldn’t come at a better time.

This business is on to something really big. Using data, machine learning and AI, Alphabet managers are incubating vibrant new businesses with innovative business models. One or more of these will become exciting stand-alone businesses.

Some analysts are already doing sum-of-the-parts analyses and they like what they see.

A Jefferies analyst pegged the value of Waymo, Alphabet’s self-driving-car business, at $250 billion in December 2018, according to a story at Business Insider.

Alphabet’s market capitalization is $798 billion, with units including YouTube, Google Search, Google Cloud, Android, the Nest security camera and peripheral businesses, Google Capital, and Stadia, its new video game streaming service set to launch in November.

Together, these parts are probably worth well over $1 trillion.

Until now, the business opportunity for DeepMind was not even on investors’ radar.

The subsidiary has its roots in DeepMind Technologies, a British AI startup that was making progress teaching computers the quirks of human short-term memory. Alphabet acquired the business in 2014.

Two years later, its custom AlphaGo code was so advanced that it became the first computer program to defeat a human in a match of Go, the ancient Chinese strategy game. That human happened to be Lee Sedol, the 18-time world champion.

At the CASP13 meeting in Mexico in December 2018, DeepMind was at it again. This time its human challengers were the brightest minds in biology. The task was predicting the shapes of proteins.

Understanding these structures is important because they govern how diseases form. The problem is there are more possible protein shapes than there are atoms in the universe, Bloomberg notes.

The math has vexed computational biologists for the past 25 years. They have been trying to build more predictive software models for protein folding, the process that leads to proteins taking three-dimensional shapes.

Despite its limited experience with folding, AlphaFold, DeepMind’s entrant, predicted the most accurate structure for 25 out of 43 proteins, taking the top spot over 98 participating teams, according to a report in the Guardian.

For perspective, the second-place team accurately predicted only three of the 43 proteins.

This does not mean Alphabet has an inside track to the next big drug discovery. It doesn’t work that way. Developing new drugs is both expensive and fraught with regulatory hurdles, patient trials and marketing expenses.

Even then, a 2013 study published by Nature Review Drug Discovery found that only 10% of medicines in development ever reach patients.

The business opportunity is increasing those odds.

In The Future Awakens, a November 2017 research study by Deloitte Center for Health Solutions, analysts posit that by 2022 medicine will be predictive, preventative (based on risk), personalized and participatory.

Computational biologists in hoodies and jeans will build personalized drug treatments based on what they know about a patient’s individual genomic makeup. Behind the scenes, data scientists using A, will comb through algorithmic models, looking for previously unseen biomarkers.

DeepMind has come out of nowhere to be a major player in that ecosystem, and it is hiding inside Alphabet shares, practically for free.

The parent’s stock trades at 21 times forward earnings and 5.6 times sales. These metrics reflect the consensus view that Alphabet is an advertising business, subject to regulatory attacks.

The regulation is coming. That’s true.

But the story of the stock is its valuable pieces. Investors are fretting about a potential breakup of Alphabet. They should be embracing that possibility. It will lead to much higher stock prices as the value of its businesses comes to light.

Growth investors should consider buying Alphabet shares into any material weakness.

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