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Quantexa raises $153M to build out AI-based big data tools to track risk and run investigations

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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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Source: https://techcrunch.com/2021/07/12/quantexa-raises-153m/

Artificial Intelligence

Wealthech: Fabrick and Prometeia Partner on Wealth Management Solution Incorporating Open Banking, AI

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Fabrick, an Open Banking Fintech and Prometia, a company offering wealth management solutions, have joined to launch the Global Investment Portfolio, a digital wealth management solution that utilizes artificial intelligence (AI) as well as Open Banking tech.

According to a release, the two companies have pooled assets and skills in open banking and AI to develop the Global Investment Portfolio that puts together an investor’s overall financial portfolio through the aggregate analysis of the bank accounts held by them across various institutions. The Wealthtech leverages AI to spot information generated by asset management activities run by other banks without the need for direct access to all of an investor’s separate investment accounts.

Global Investment Portfolio uses Fabrick’s PSD2 Gateway that allows access to comprehensive bank data through the account aggregation service which provides analysis of all current accounts. The service provides a multi-bank experience that allows customers to view all information from a single touch point. The service is designed to allow investors to monitor all their investments from a single platform while providing real-time comparisons of investments and the ability to easily see which are performing and which are not.

Matteo Necci, a Partner at Prometeia, explained:

“Global Investment Portfolio is a cutting-edge solution with respect to the main trends in Digital Finance and is proposed as a distinctive element in the automation and digitisation of customer advisory processes. The combination of our know-how in artificial intelligence solutions for wealth management with Fabrick’s open banking expertise and ecosystem allows intermediaries to have in-depth knowledge of the investor’s financial portfolio, fully developing the potential of PSD2”.

Paolo Zaccardi, CEO of Fabrick, said that wealth management is a sector that is proving to be very active in exploiting the benefits of Open Finance to develop new digital services that meet the needs of the public and end consumers:

“Fabrick is an active part of this process and the partnership with Prometeia demonstrates how access to current account data represents only the tip of the iceberg of the numerous opportunities presented by our ecosystem and the collaborative approach we promote. You just have to look at the Global Investment Portfolio solution to understand the great value that the combination of account aggregation and data categorisation brings to all the players involved, tangibly enabling a new and more complete and personalised offer model.”

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Source: https://www.crowdfundinsider.com/2021/07/178142-wealthech-fabrick-and-prometeia-partner-on-wealth-management-solution-incorporating-open-banking-ai/

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U.S. Issues Warning Advisory on Travel for the UK Over Rising COVID-19 Cases

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Americans should not travel to the United Kingdom – including England, Scotland, Wales, and Northern Ireland – because of the rise in Covid-19 cases caused by the virus in the Delta variant. Both the U.S. Centers for Disease Control and Prevention (CDC) and the U.S. Department of State have given the UK their very high warning levels.

Yesterday the CDC raised travel advisory in the United Kingdom to level 4, which means “the highest level of Covid-19.” They issued a notice that reads “If you must travel to the UK make sure that you are vaccinated”. “Due to the current situation in the United Kingdom, even fully vaccinated travelers may be at risk of receiving and distributing COVID-19,” the CDC notice said.

Covid-19 cases grew by more than 50,000 a day in the UK and hundreds of thousands of Britons were asked to go for self-isolation for ten days. In the U.K. the warning level previously was at level 3, indicating a “high” level of Covid-19 and warns that only fully vaccinated travelers should travel.

The U.S. Department of State raised its United Kingdom tourism warning to Level 4, which means “don’t visit the Uk.”The United Kingdom is currently recording an average of 65 new Covid-19 cases per 100,000 people, from the data issued by the Brown School of Public Health. That level of exposure puts the country “tipping point,” according to Brown’s Covid-19 risk assessment map.

U.S. Warnings were issued last Monday just after England abandoned the last of its epidemic restrictions and celebrated festive events to celebrate “Freedom Day”. This raised eyebrows for many countries including the US. However, Scotland, Wales, and Northern Ireland keep certain restrictions such as compulsory masks and social distances in public places.

Covid-19 is also rapidly spreading in the United States. Delta’s variant of Covid-19 exacerbates an increase in the number of deaths nationwide, say U.S. health officials. The United States currently records 12 new cases every day for every 100,000 people. The American epidemic epicenter is the state of Florida, currently recording 49.3 new cases a day out of 100,000. “This has become a pandemic for the uninitiated,” said Dr. Rochelle Walensky, director of the CDC.

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Source: https://1reddrop.com/2021/07/21/u-s-issues-warning-advisory-on-travel-for-the-uk-over-rising-covid-19-cases/?utm_source=rss&utm_medium=rss&utm_campaign=u-s-issues-warning-advisory-on-travel-for-the-uk-over-rising-covid-19-cases

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Praemium’s machine learning takes platform accuracy to a new level

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Praemium has expanded its machine learning and artificial intelligence capabilities to benefit users of its non-custodial Virtual Managed Account solution, by reducing human errors in data entry and improving data integrity.

Using machine learning across a range of data sets, Praemium has been able to identify transactions that may have been incorrectly entered or categorised by administrators.

Praemium’s Chief Technology Officer, Adam Pointon said, “When managing large volumes of data, human errors happen. Through machine learning we have been able to identify which transactions may be incorrect and predict the correct classification. For example, a buy transaction might be incorrectly entered as a withdrawal, or income as a deposit. These errors could provide incorrect portfolio performance information or have tax implications for investors.”

“This functionality allows for errors to be detected at scale and rectified quickly and is already being used successfully with several of Praemium’s institutional clients,” Pointon added.

The functionality expands upon Praemium’s existing machine learning capability Insights, launched in 2019, that is able to provide highly accurate predictive analytics that a client is demonstrating behaviours that indicate they are needing advice.

“Praemium’s non-custodial solution is recognised as the market-leader and we continue to enhance our technology with these exciting innovations.” Pointon continues.

Recent research undertaken by Praemium with Investment Trends showed that almost 60% of advisers are managing non-custodial client assets off-platform. Typically, these assets are managed manually via spreadsheets, consuming two extra hours of adviser resource per client.

Praemium’s Chief Commercial Officer Mat Walker also commented, “Praemium’s non-custodial solution has $140bn in assets under administration and offers advisers and wealth managers the benefit of managing both custodial and non-custodial assets on a single platform. Our research indicates that advisers are feeling the burden of administering these assets and our technology not only does this efficiently but also more accurately. We also offer the option to remove the administration burden completely by outsourcing to Praemium’s Administration Service who also utilise this functionality for large volume data processing.”

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Source: https://australianfintech.com.au/praemiums-machine-learning-takes-platform-accuracy-to-a-new-level/

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Will AI Developments Help Open Banking Take Off?

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Artificial intelligence has become a gamechanger in the banking industry in recent years. The global market for AI in Fintech was valued at nearly $8 billion last year. It is projected to be worth nearly $27 billion by 2026.

There are a number of reasons that AI is becoming an integral part of the banking industry. One reason is that it is driving process automation. However, AI is starting to show potential with even more complicated automation issues.

AI has made open banking possible. New advances in AI could help open banking become even more popular in the near future.

AI Drives the Future of Open Banking

Open banking is the technical process that allows financial providers to dip in and see the banking history and activity of a customer before they apply. It has been made possible through new developments in AI technology.

The process was recently introduced in the UK and many suggest that it could be the future of underwriting and eligibility for products such as credit cards, loans and mortgages. Antonio Tinto wrote an article about the evolution of open banking in the context of AI in fintech in his LinkedIn post Open Banking and AI – The Rise of Cognitive Banking.

Customers must agree for lenders to see their transactional history and financial information during the application process – but this should be able to provide lenders with a better understanding of the customer’s borrower spending, including highlighting any gambling or debt problems with machine learning algorithms. 

For lenders this offers a very insightful look into a customer’s spending habits and should provide much better decisions in terms of loan approvals, credit limits, loan amounts and more.

Budget planning programs and also fall under the umbrella of open banking. These machine learning programs compile data sourced from multiple locations such as credit cards and bank accounts, providing a full picture of spending habits. 

What Are the Benefits of Open Banking with AI?

Open banking gives lenders a better picture of a spender’s habits, allowing them to make an informed decision regarding potential loan and credit applications. Lenders use complex data-driven algorithms to make these analyses.


Currently, lenders rely heavily on customer credit scoring and other metrics including income checks and affordability checks, but for the average personal loan or credit card, there is no real delving into someone’s banking activity or machine learning analysis.

This allows lenders to find concrete information if there are recurring gambling issues, multiple loans taken out or huge overdrafts – something that typically goes unnoticed by lenders in basic checks.

Beyond this, lenders and credit providers can use these findings to improve their underwriting and build models to determine eligibility patterns – and thus approve better customers and increase their repayment rates.

What Are the Risks Associated with Online Banking?

Risks associated with online banking tend to include concerns about privacy policies and data protection. Financial data from various sources is merged in order to be analyzed in comparison with other datasets to create predictive algorithms. This can then forecast future spending habits.

This requires the access of private financial data, giving firms access to any transactions. Lenders are able to see any financial transactions taking place with customer consent, which could prevent them offering a loan.

The Difference Between Open Banking and Credit Scoring

Open banking can potentially offer more accurate reflections of a person’s financial situation and can also utilize existing credit scores to make decisions surrounding potential loans even stronger.

As open banking increases in popularity, different types of loans will be able to use it to provide lenders with clear insights of borrowers financial habits. Mortgages and other types of loans have the potential to operate in this manner, as open banking is adopted by more and more businesses.

Will Open Banking Take Off as AI Becomes More Widely Used in the Financial Sector?

AI technology has made open banking possible. Banking institutions are relying more heavily than ever on machine learning algorithms.

David Beard, founder of price comparison site, Lending Expert, commented:

“Open banking is certainly revolutionary and will definitely help lenders to better understand their applicants. Being able to see a customer’s bank statement history can highlight potential risks such as gambling debts or if they are starting with huge debts to begin with. This could help lenders steer clear of troubled customers or approve those that look more appealing.”

“The only challenge is that people have to opt into open banking, which not every customer will want to do – and ideally you need real volumes to make a difference to your bottom line and to build future models.”

“If lenders and providers can present this in a smart way that is data compliant and abides by regulation, open banking could be transformative.”

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Source: https://www.smartdatacollective.com/will-ai-developments-help-open-banking-take-off/

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