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Lumiant, client engagement SAAS solution for financial advisers, approaches go-live date and smashes funding goal




Australian financial advice software as a service (SaaS) platform Lumiant has opened up shares in its business to everyday Australians, with leading Australian equity crowdfunding platform Equitise overseeing the up to $1.5 million raise.

Launching today, the company has already surpassed the minimum funding target of $250,000. This raise will close successfully on 16 December.

Lumiant is Australia’s first technology platform which aims to provide a holistic and interactive end-to-end client engagement process for financial advisers and their clients. Through a series of goals and values focussed modules, Lumiant helps financial advisers guide their clients through a process that uncovers what is most meaningful to them, allowing the adviser to tailor their clients’ financial strategies in a way that supports the making of dreams — not just the management of money.

Lumiant founder and CEO elect – Santiago Burridge, says structural changes taking place in the industry are ensuring that clients of financial advisers not only comprehend the advice they are receiving, but engage with it meaningfully.

“If there is one thing that COVID 19 has taught us is that there is much more to life than money; that it is time to live our lives much closer to our values, to align our money with our values and drive more purpose out of what we have created,” he said.

Mr Burridge said that for too long, great financial advisers have been constrained by a product focused industry. This inspired him to build a tech platform where the value of financial advice can be truly recognised, “where an adviser can proudly say their clients’ lives are better for the value of the advice they received”.

Co-founder of Australia’s leading Equity Crowdfunding platform Equitise, Jonny Wilkinson, says that “having worked with so many fintechs we were surprised to discover the lack of technological advancement in the financial advice industry”.

“Tools focused on product and the middle to back office advice process, limited both the financial adviser and their client. Similar to what we’ve seen in the banking industry, it’s time for financial advice technology to be more customer centric and tailored, which is why we thought Lumiant was such a good idea.’’

Consumer confidence, regulatory complexity, investment market volatility and a business model without access to the right technology for decades has made the financial advisory sector primed for disruption and with less than 20% of Australians* seeking financial advice, it’s clear it needs to evolve quickly.

Mr Burridge said progressive advisers “can’t wait to access an innovative, fairer, and more transparent solution that will help them to build better businesses, create stronger communities, manage more clients and live better lives”.

“These advice businesses — including sole traders, partnerships, and larger adviser networks — remain underserved and undervalued by the traditional financial institutions, and are often constrained by legacy business models, technologies, and procedures,” he said.

“Advisers are tired of having to manage their business via a patchwork of ill-conceived, product-based technologies. It is costing them time, money and adding to the stress of running an advice business.” 

The Lumiant SaaS solution in the final stages of development and the company plans to go-live with its first tranche of adviser clients in early 2021.


AI raises $20 million to aggregate enterprise bank accounts with AI




Banking technology startup today announced it has raised $20 million in a series A round led by Wells Fargo Strategic Capital. The investment will be used to deliver new services and accelerate multi-bank APIs globally, the company says, and to add more bank distribution partners.

Trovata founder and CEO Brett Turner, who has spent time at Deloitte and Amazon, predicted that the rise of consumer bank aggregators driving fintech would lead to direct APIs for commercial banking and treasury services from banks globally. These prebuilt bank integrations, he believed, would remove enterprises’ need for legacy implementations or IT support and enable self-setup.

Turner launched 35-employee Trovata in 2019 in anticipation of the transformation, with a platform to aggregate companies’ bank balances and transactions natively on wholesale banking APIs. Using AI and machine learning, Trovata can automate cash-centric workflows such as cash reporting, analysis, and forecasting, allowing companies to see how much cash they have in real time while managing cash flow and building and maintaining forecasts.

Trovata acts as a high-performance data lake to store and manage bank data in a scalable multi-bank environment. The platform collects and normalizes data and then generates a forecast, leveraging machine learning to establish a baseline and analyze historical trends to increase forecast accuracy.

Trovata lets customers including Square tag data by region, entity, division, or arbitrary label. It also translates all non-USD denominated amounts into USD equivalents, offering the ability to drill down and generate forecasts for subsidiaries individually. A Google-like natural language search tool with a 300 millisecond response rate lets users find and tag key vendors, customers, and partners across millions of transactions.

“The tipping point is near, and … Trovata can play [a profound role] in wholesale banking and treasury services. The pandemic has spurned the need for better cash visibility, bank data in real time, and more proactive cash forecasting. Companies growing and contracting are in need of these things which have only accelerated interest,” Turner said in a statement. “Revenue is confidential, but our average deal size is roughly $25,00 and we’ve grown from 0 to nearly 100 mid-market and small enterprise customers in 18 months. We’ll be announcing a new up-market product for the enterprise later this month and expect to grow 4 times to 5 times this year.”

“We are keen on how technology is reinventing the treasury function into a modern, insight-driven operation that helps our clients deliver on their business strategy,” Wells Fargo Strategic Capital managing director Basil Darwish added. “Trovata provides distinctive technology and a client-centric approach to automating treasury services, and we’re excited to support their ongoing growth with this investment.”

Capital One Ventures and Pivot Investment Partners also participated in Trovata’s series A announced today, as well as existing early investors J.P. Morgan and Fintop Capital. This brings the San Diego-based company’s total raised to over $30 million, following seed and venture rounds totaling $10.6 million.


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Artificial Intelligence

The future growth of AI and ML




The Future Growth of AI and ML

By Rachel Roumeliotis

We’ve all come to terms with the fact that artificial intelligence (AI) is transforming how businesses operate and how much it can help a business in the long term. Over the past few years, this understanding has driven a spike in companies experimenting and evaluating AI technologies and who are now using it specifically in production deployments.

Of course, when organisations adopt new technologies such as AI and machine learning (ML), they gradually start to consider how new areas could be affected by technology. This can range across multiple sectors, including production and logistics, manufacturing, IT and customer service. Once the use of AI and ML techniques becomes ingrained in how businesses function and in the different ways in which they can be used, organisations will be able to gain new knowledge which will help them to adapt to evolving needs.

By delving into O’Reilly’s learning platform, a variety of information about the different trends and topics tech and business leaders need to know can be discovered. This will allow them to better understand their jobs and will ensure that their businesses continue to thrive.

Over the last few months, we have analysed the platform’s user usage and have discovered the most popular and most-searched topics in AI and ML. We’ll be exploring some of the most important finding below which gives us a wider picture of where the state of AI and ML is, and ultimately, where it is headed.

AI outpacing growth in ML

First and foremost, our analysis shone a light on how interest in AI is continuing to grow. When comparing 2018 to 2019, engagement in AI increased by 58% – far outpacing growth in the much larger machine learning topic, which increased only 5% in 2019. When aggregating all AI and ML topics, this accounts for nearly 5% of all usage activity on the platform.

While this is just slightly less than high-level, well-established topics like data engineering (8% of usage activity) and data science (5% of usage activity), interest in these topics grew 50% faster than data science. Data engineering actually decreased about 8% over the same time due to declines in engagement with data management topics.

We also discovered early signs that organisations are experimenting with advanced tools and methods. Of our findings, engagement in unsupervised learning content is probably one of the most interesting. In unsupervised learning, an AI algorithm is trained to look for previously undetected patterns in a data set with no pre-existing labels or classification with minimum human supervision or guidance. In 2018, the usage for unsupervised learning topics grew by 53% and by 172% in 2019.

But what’s driving this growth? While the names of its methods (clustering and association) and its applications (neural networks) are familiar, unsupervised learning isn’t as well understood as its supervised learning counterpart, which serves as the default strategy for ML for most people and most use cases.

This surge in unsupervised learning activity is likely driven by a lack of familiarity with the term itself, as well as with its uses, benefits, and requirements by more sophisticated users who are faced with use cases not easily addressed with supervised methods.

It is also likely that that the visible success of unsupervised learning in neural networks and deep learning has helped our interest, as has the diversity of open source tools, libraries and tutorials, that support unsupervised learning.

A Deep Learning Resurrection

While deep learning cooled slightly in 2019, it still accounted for 22% of all AI and ML usage. We also suspect that its success has helped spur the resurrection of a number of other disused or neglected ideas. The biggest example of this is reinforcement learning. This topic experienced exponential growth, growing over 1,500% since 2017.

Even with engagement rates dropping by 10% in 2019, deep learning itself is one of the most popular ML methods among companies that are evaluating AI, with many companies choosing the technique to support production use cases. It might be that engagement with deep learning topics has plateaued because most people are already actively engaging with the technology, meaning growth could slow down.

Natural language processing is another topic that has showed consistent growth. While its growth rate isn’t huge – it grew by 15% in 2018 and 9% in 2019 – natural language processing accounts for about 12% of all AI and ML usage on our platform. This is around 6x the share of unsupervised learning and 5x the share of reinforcement learning usage, despite the significant growth these two topics have experienced over the last two years.

Not all AI/ML methods are treated equally, however. For example, interest in chatbots seems to be waning, with engagement decreasing by 17% in 2018 and by 34% in 2019. This is likely because chatbots were one of the first application of AI and is probably a reflection of the relative maturity of its application.

The growing engagement in unsupervised learning and reinforcement learning demonstrates that organisations are experimenting with advanced analytics tools and methods. These tools and techniques open up new use cases for businesses to experiment and benefit from, including decision support, interactive games, and real-time retail recommendation engines. We can only imagine that organisations will continue to use AI and ML to solve problems, increase productivity, accelerate processes, and deliver new products and services.

As organisations adopt analytic technologies, they’re discovering more about themselves and their worlds. Adoption of ML, in particular, prompts people at all levels of an organisation to start asking questions that challenge what an organisation thinks it knows about itself.

With ML and AI, we’re training machines to surface new objects of knowledge that help us as we learn to ask new, different, and sometimes difficult questions about ourselves. By all indications, we seem to be having some success with this. Who knows what the future holds, but as technologies become smarter, there is no doubt that we will we become more dependent.


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MAS Revises Tech Risk Guidelines Amidst Heightened Cyber Attack Threats




The Monetary Authority of Singapore (MAS) has issued a revised Technology Risk Management guidelines in light of the recent spate of cyber attacks dominating the headlines.

The revised guidelines focuses on addressing technology and cyber risks in financial institutions (FIs) deploying cloud technologies, application programming interfaces, and rapid software development.


The guidelines reinforce the importance of incorporating security controls as part of FIs’ technology development and delivery lifecycle, as well as in the deployment of emerging technologies.

The revised guidelines set out enhanced risk mitigation strategies for FIs which includes establishing a robust process for the timely analysis and sharing of cyber threat intelligence within the financial ecosystem.

It also outlines the importance of conducting cyber exercises to allow FIs to stress test their cyber defenses by simulating the attack tactics, techniques, and procedures used by real-world attackers.

In light of FIs’ growing reliance on third party service providers, the revised guidelines set out the expectation for FIs to exercise strong oversight of arrangements with third party service providers, to ensure system resilience as well as maintain data confidentiality and integrity.

The guidelines also provides additional guidance on the roles and responsibilities of the board of directors and senior management to ensure that a Chief Information Officer and a Chief Information Security Officer, with the requisite experience and expertise, are appointed and accountable for managing technology and cyber risks;

The board should also include members with the relevant knowledge to provide effective oversight of technology and cyber risks.

The revised guidelines have incorporated feedback received from the public consultation conducted in 2019, MAS’ engagement with the industry, and MAS’ Cyber Security Advisory Panel (CSAP).

Mr Tan Yeow Seng, Chief Cyber Security Officer, MAS, said,

Tan Yeow Seng

“Technology now underpins most aspects of financial services. Not only are financial institutions adopting new technologies, they are also increasingly reliant on third party service providers.

The revised guidelines set out MAS’ higher expectations in the areas of technology risk governance and security controls in financial institutions.”

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Singaporeans Encouraged to Hand Out E-Hong Baos for Lunar New Year




The Monetary Authority of Singapore (MAS) is encouraging Singaporeans to use e-hong baos, monetary gifts given in envelopes, during the coming Lunar New Year. This is in line with the safety measures currently in place due to COVID-19.

E-hong baos will enable remote gifting across a variety of visitation practices, including virtual gatherings, during the upcoming Lunar New Year. Giving e-hong baos instead of physical notes is also environmentally more sustainable as it reduces the printing and subsequent wastage of new notes that are returned by the public to banks after each Lunar New Year.


E-hong baos are part of a larger shift towards e-gifting that MAS and ABS are promoting. MAS also encourages fintech firms to develop e-gifting solutions for different purposes, including gifting during festive periods.

DBS had previously debuted its loadable QR red packets in 2019

Members of the public, except those aged 60 and above and persons with disabilities, who prefer physical notes for the Lunar New Year will need to make an appointment through their respective bank’s online reservation system before visiting the branches to collect the new notes. The five banks offering the service are five banks DBS, OCBC, UOB, Standard Chartered and Maybank Singapore.

The pre-order period for new and good-as-new notes will start from 18 January 2021. The collection for online orders, walk-in option for elderly aged 60 and above and persons with disabilities, and withdrawal at DBS’ pop-up ATMs will start from 25 January 2021.

New notes can also be withdrawn without a prior booking at pop-up ATMs offered by DBS.

Customers should refer to the respective bank’s website for details on how to pre-book and collect their orders.

Bernard Wee, Assistant Managing Director, Finance, Risk & Currency at MAS said,

Bernard Wee

“The adoption of e-payments grew significantly this past year as it is more convenient than cash. The coming Lunar New Year offers an opportunity for us to build on this momentum, to spread the benefits of e-gifting, and to forge new traditions with our families and friends.

E-gifting helps to reduce the queues at banks, and also helps to reduce the carbon emissions generated by the production of new notes for each Lunar New Year, estimated to be about 330 tonnes currently. This is equivalent to emissions from charging 5.7 million smart phones or one smart phone for every Singaporean resident for five days.”

Featured image credit: DBS eGift

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