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NICE Renews Support for Use of AI-powered HeartFlow Analysis

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May 12, 2021 — HeartFlow, Inc., a leader in revolutionizing precision heartcare, today announced that the National Institute of Health and Care Excellence (NICE) has renewed its support for the use of its AI-powered HeartFlow FFRct Analysis to fight coronary heart disease (CHD), one of the leading causes of death in the UK.

NICE has found that the HeartFlow Analysis continues to improve patient experience and reduce the need for invasive angiography, four years after its initial recommendation that the technology be used to support CHD diagnosis within the NHS. Furthermore, it has estimated that using the HeartFlow Analysis represents a cost saving of £391 per patient to NHS England, £177 higher than original calculations suggested in 2017.

The latest guidance represents a win for the use of digital technologies in the NHS and reflects its commitment to improving patient care through innovation. The HeartFlow Analysis was recently selected as one of the innovations supported by NHS England’s new MedTech Funding Mandate, which aims to provide new medical devices and digital products to patients faster.

NICE also recognises the non-invasive technology’s improved performance in recent years, including:

  • Ongoing incremental software releases to address security updates, compatibility updates and user experience improvements based on clinician feedback;
  • A reduced turnaround time for sharing findings of the Analysis with clinicians – from 48 hours in 2017 to six hours;
  • Enhanced options for viewing the 3D model, such as via mobile platforms, integration with electronic health records and shared patient management updates.

Tim Fairbairn, M.D., Consultant Cardiologist, at Liverpool Heart & Chest Hospital and honorary senior lecturer at the University of Liverpool Hospitals said: “This latest assessment from NICE that the HeartFlow technology offers significant cost savings through avoidance of invasive investigations and streamlining treatment for patients reinforces what we have been seeing in clinical practice. In light of that, we’re pleased to continue its use within the healthcare service at Liverpool Heart and Chest Hospital where it is helping improve our care of CHD patients.”

The HeartFlow Analysis streamlines the diagnostic experience for patients – often enabling physicians to eliminate invasive diagnostic procedures for those who do not need them. It also helps physicians swiftly and accurately diagnose those who do need invasive procedures. The technology limits redundant non-invasive diagnostic testing, reduces patient time in hospital and face-to-face clinical contact, and helps ensure that hospital visits for those who do need them are streamlined, which is particularly crucial during the Covid-19 pandemic

Campbell Rogers, M.D., Executive Vice President and Chief Medical Officer, HeartFlow, said: “It’s fantastic to see the renewed guidance from NICE which continues to advocate a CTA-first approach to diagnosing CHD. The HeartFlow Analysis has been identified as a best practice, non-invasive option for patients with stable, recent onset chest pain. It helps clinicians to make a diagnosis more confidently, while reducing the time to diagnosis for patients.

“The HeartFlow Analysis is now available in 65 NHS hospitals across England and is planned to be in 30 more by 2022. With the support of NICE and as part of NHSE’s MedTech Funding Mandate, we’re now in a position to scale and ensure that more clinicians and patients can benefit from our non-invasive technology and receive a quicker CHD diagnosis and treatment plan,” he continued.

About the HeartFlow FFRct Analysis

The HeartFlow Analysis is a non-invasive, cardiac test for stable symptomatic patients with CAD, the leading cause of death worldwide. Starting with a standard coronary CTA, the HeartFlow Analysis leverages deep learning and highly trained analysts to create a digital, personalized 3D model of the heart. The HeartFlow Analysis then uses powerful computer algorithms to solve millions of complex equations to simulate blood flow and provides FFRct values along the coronary arteries. This information helps physicians evaluate the impact a blockage may be having on blood flow and determine the optimal course of treatment for each patient. A positive FFRct value (≤0.80) indicates that a coronary blockage is impeding blood flow to the heart muscle to a degree which may warrant invasive management.

Data demonstrating the safety, efficacy and cost-effectiveness of the HeartFlow Analysis have been published in more than 425 peer-reviewed publications, including long-term data out to five years. The HeartFlow Analysis offers the highest diagnostic performance available from a non-invasive test. To date, clinicians around the world have used the HeartFlow Analysis for more than 75,000 patients to aid in the diagnosis of heart disease.

For more information: www.heartflow.com

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AI

Frequent run-ins with Indian government complicates tech giants’ plans

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(Reuters) — Another spat between India’s government and U.S. big tech has exacerbated disillusion among firms which have spent billions to build hubs in their largest growth market, to the extent some are rethinking expansion plans, people close to the matter said.

The government on Saturday said Twitter had not indicated compliance with new rules aimed at making social media firms more accountable to legal requests, and therefore risked losing liability exemptions for content posted on its platform.

Twitter joins compatriots Amazon.com, Facebook, and Facebook-owned WhatsApp in long being at loggerheads with the administration of Prime Minister Narendra Modi over data privacy bills and policies some executives have called protectionist, but tension has escalated in recent weeks.

Police visited Twitter last month to notify it of a probe into the tagging of a political tweet as “manipulated media”, and in February interrogated an Amazon official about the potentially adverse social impact of a political drama. Meanwhile, WhatsApp is challenging the government in court over rules it said would force it to access encrypted data.

“The fear is there,” said a senior tech industry executive in India. “It weighs both strategically and operationally.”

There are no indications the increasing run-ins have led to the delay or cancellation of planned investment.

Still, three senior executives familiar with the thinking of major U.S. tech firms said perceptions of India being an alternative, more accessible growth market to China are changing, and that longstanding plans for India’s role in their operations are being reviewed.

“There always used to be these discussions to make India a hub, but that is being thought through now,” said one of the executives, who works at a U.S. tech firm. “This feeling is across the board.”

Four other executives and advisors also expressed concern about rising tension. All declined to be identified due to the sensitivity of the matter and because discussions were private.

Twitter, Amazon, Facebook, WhatsApp and India’s Ministry of Electronics and Information Technology did not respond to requests for comment.

Misinformation

The government has argued that its rules are needed to stem the spread of misinformation that can spark violence – such as in 2017 when kidnapping rumours shared on message apps including WhatsApp led to lynching. It also said the rules are necessary to hold large technology companies accountable for practices that hurt domestic businesses or compromise customer privacy.

India is a massive market for U.S. tech giants. It is the biggest market for both Facebook and WhatsApp by user numbers, showed data from Statista, and third for Twitter. Amazon has committed as much as $6.5 billion to invest in the country.

To attract small businesses through WhatsApp, Facebook last year invested $5.7 billion in Reliance Industries‘s media and telecommunications arm, Jio Platforms.

Alphabet’s Google also pumped $4.5 billion into Jio last year from a newly created $10 billion fund earmarked for investment in India over five to seven years.

Compliance

The government has tried to balance attracting high-tech investment with nationalist policies aimed at protecting local businesses and, critics say, advancing its political agenda.

A border confrontation with China prompted it to effectively ban Chinese social media apps, including TikTok and WeChat.

The government has also forced foreign firms to store data locally against fierce lobbying, and its promotion of a domestic payment card network prompted Mastercard to complain to the U.S. government about the use of nationalism.

In 2019, compliance issues with new regulations saw Amazon remove thousands of products from its e-commerce platform. The e-tailer is separately facing scrutiny by the Competition Commission of India for its retailing practices.

Twitter publicly refused to comply with some government demands to remove content, a stance which some industry executives said may have aggravated its current situation.

WhatsApp has gone to court rather than comply with a new law requiring social media firms to trace the origin of dangerous or criminal posts on their platforms. The message app operator said it cannot comply without breaking encryption, while observers said yielding could prompt similar demands in other countries.

At the same time, WhatsApp has faced regulatory delays that have limited its payment service to just 4% of its 500 million customers. Nevertheless, it is pressing ahead with hiring for a service it has called a “globally significant” opportunity.

Government officials have shown little patience for objections. IT minister Ravi Shankar Prasad said any robust democracy must have accountability mechanisms, such as the ability to identify the originator of messages.

“A private company sitting in America should refrain from lecturing us on democracy when you are denying your users the right to effective redressal forum,” Prasad said in an interview with the Hindu newspaper published on Sunday.

Still, continued antagonism could imperil Modi’s ambition of making India a go-to investment destination.

“It’s a question of what you would develop in a three-to-five-year horizon,” said another executive familiar with the thinking of U.S. firms. “Do you do that in India or do you do that in another country. That’s where the conversation is.”

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Artificial Intelligence

Global Economic Impact of AI: Facts and Figures

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Sharmistha Chatterjee Hacker Noon profile picture

@sharmi1206Sharmistha Chatterjee

https://www.linkedin.com/in/sharmistha-chatterjee-7a186310/

Summarization of Research Insights from Emerj, Harvard Business Review, MIT Sloan, and Mckinsey

Wall Street, venture capitalists, technology executives, data scientists — all have important reasons to understand the growth and opportunity in the artificial intelligence market to access business growth and opportunities. This gives them insights on funds invested in AI and analytics as well potential revenue growth and turnover. Indeed, the growth of AI, continuing research, development of easier open source libraries and applications in small to large scale industries are sure to revolutionize the industry the next two decades and the impact is getting felt in almost all the countries worldwide.

To dive deep into the growth of AI and future trends, an insight into the type and size of the market is essential along with (a) AI-related industry market research forecasts and (b) data from reputable research sources for insight into AI valuation and forecasting.

The blog is structured as follows :

  • To provide a short consensus on well-researched projections of AI’s growth and market value in the coming decade.
  • To understand the per capita income and GDP of each country from businesses driven by AI and analytics.

Impact of AI is so widespread, touching and vivid that:

IBM’s CEO claims a potential $2 trillion dollar market for “cognitive computing”).

Google co-founder Larry Page states that “Artificial intelligence would be the ultimate version of Google. The ultimate search engine is capable of understanding everything on the web. It will become so much AI driven that in near future ,it would understand exactly what you wanted and it would give you the right thing. We’re nowhere near doing that now. However, we can get incrementally closer to that, and that is basically what we’re working on”.

Different sectors exhibit dynamics in terms of adopting and absorbing AI, leading to different levels of economic impact.

Source

On comparing different industry-sectors we see from the figure above:

In high-tech industries like Telecom and media has already adopted AI relatively rapidly and looking for transformations in all possible avenues. They are then followed by Consumer, Financial Services and Professional Services.

Healthcare and Industrial Sector are adopting AI slowly. Energy and Public Sector are the slowest adaptors to this transition.

Further, the economic impact in the telecom and high-tech sector could be more than double that of healthcare in 2030. If the national average of macroeconomic impact is 100, healthcare might experience 40 percent lower impact (i.e. 60). The fast and rapid adopters like the telecom and high-tech sector are highly influenced by AI and could experience 40 percent higher impact (i.e. 140) than the national average.

Several internal and external factors specific to a country or a state, have been known to affect AI-driven productivity growth, including labor automation, innovation, and new competition. In addition, certain micro factors, such as the pace of adoption of AI, and macro factors such as a country’s global connectedness and labor-market structure also plays a certain factor to the size of the impact.

The end result is to grow the AI value chain and boost the ICT sector, making an important economic contribution to an economy.

Production channels: Direct economic impact of AI aims to automate production and save cost. It primarily considers three production dimensions. Firstly it includes calling labor and capital “augmentation”, where new AI capacity is developed, deployed, and operated by new engineers and big data analysts. Second, investment in AI technologies saves labor as machines take over tasks that humans currently perform. Thirdly, better AI-driven innovation saves overall cost (including infrastructure), enabling firms to produce the same output with the same or lower inputs.

Augmentation: Relates to increased use of productive AI-driven labor and capital.

Substitution: AI-driven technologies offer better results in the field like automation, where it has been found to be more cost-effective. It has also discovered ways and means to substitute other factors of production. Advanced economies could gain about 10 to 15 percent of the impact from labor substitution, compared with an impact of 5 to 10 percent in developing economies.

Product and service innovation and extension: Motivation for investment in AI beyond labor substitution can produce additional economic output by expanding firms’ portfolios, increasing channels for products and services (for e.g. AI-based recommendations), developing new business models, or combination of the three.

Externality channels: It serves as one of the external channels where the application of AI tools and techniques can contribute to economic global flows (for e.g. chatbots, news aggregation engines). Such flow happens inter-country (states and geographical boundaries) and even between countries that facilitate more efficient cross-border commerce. It is found that countries that are more connected and participate more in global flows would clearly benefit more from AI. Further AI could boost supply chain efficiency, reduce complexities associated with global contracts, classification, and trade compliance.

Wealth creation and reinvestment: AI is contributing to higher productivity of economies, efficiency gains. Further innovations result in an increase in wages for workers, entrepreneurs, and firms in the form of profits, higher consumption, and more productive investment.

Transition and implementation costs: Several costs incurred while executing the transition to AI like organization restructuring costs, adoption to new solutions, integration costs, and associated project and consulting fees are known to affect the transition in a negative way. Businesses should do a trade-off between cost and benefit analysis and correctly strategize their roadmap.

Negative externalities: AI could induce major negative distributional externalities affecting workers by depressing the labor share of income and potential economic growth.

The following figure illustrates the detailed overall economic impact sustained due to the wider adoption of AI techniques and strategies by businesses.

Source

AI-driven businesses have led to a positive impact on the growth of revenue over consecutive years. More so, the statements made by renowned founders, CEOs, entrepreneurs and visionary leaders is evident from the figure below as it shows the impact of AI on global GDP, the maximum being obtained from venture-backed startups.

Source: https://emerj.com/ai-sector-overviews/valuing-the-artificial-intelligence-market-graphs-and-predictions/

“Tractica forecasts that the revenue generated from the direct and indirect application of AI software is estimated to grow from $643.7 million in 2016 to $36.8 billion by 2025. This represents a significant growth curve for the 9-year period with a compound annual growth rate (CAGR) of 56.8%.”

Tractica has taken a conservative adoption of AI in the hedge fund and investment community, with an assumption that roughly 50% of the hedge fund assets traded by 2025 will be AI-driven. Under this estimate, the algorithmic trading use case remains the top use case among the 191 use cases identified by Tractica.

Further as per reports from Tractica, the market for enterprise AI systems will increase from $202.5 million in 2015 to $11.1 billion by 2024, as depicted in the following figure.

View of Worldwide growth of AI revenue, Source — Tractica

The growth forecasts over the next decade clearly show China’s dominance over the AI market yielding a significant increase in GDP, followed by USA, Nothern Europe, and other nations.

In China, AI is projected to give the economy a 26% boost over the next 13 years, measuring an equivalent of an extra $7 trillion in GDP, helping China to rise to the top. As North America’s companies are widely using AI, the adaptation is at an accelerating phase that it can expect a 14.5% increase in GDP, worth $3.7 trillion.

As the GDP growth varies across continents and nations, the level of AI absorption also varies significantly between the country groups with the most and the least absorption. The below figure demonstrates statistics of economies with higher readiness to benefit from AI. Such countries achieve absorption levels about 11 percentage points higher than those of slow adopters by 2023, and this gap looks set to widen to about 23 percentage points by 2030. This further gives an indication of the digital divide created from AI, between advanced and developing economies.

Source: Mckinsey

The resulting gap in net economic impact between the country groups with the highest economic gains and those with the least is likely to become larger, for e.g. a large gap in economic impact between the leading and the lagging — between Sweden and Zambia. The gap could widen from three percentage points in 2025 to 19 percentage points in 2030 in terms of net GDP impact.

AI is internationally recognized as the main driver of future growth and productivity, innovation, competitiveness and job creation for the 21st century. However, there remain certain technical challenges, that need to be overcome to take it to the next step. The key challenges include

  • Labeled training data
  • Obtaining sufficiently large data sets. 
  • Difficulty explaining results
  • Difficulty generalizingScaling challengesRisk of bias

Apart from the common technical challenges, risks, and barriers faced by organisations implementing AI are evident.

It is now the responsibility of policymakers and business leaders to take measurable actions to address the challenges, support researchers, data scientists, business analysts, and all included in the AI ecosystem to drive the economy with huge momentum.

As rightly quoted by Stephen Hawking, Famous Theoretical Physicist, Cosmologist, and Author:

“Success in creating AI would be the biggest event in human history. Unfortunately, it might also be the last, unless we learn how to avoid the risks.”

References

  1. Valuing the Artificial Intelligence Market, Graphs and Predictions: https://emerj.com/ai-sector-overviews/valuing-the-artificial-intelligence-market-graphs-and-predictions/
  2. NOTES FROM
    THE AI FRONTIERMODELING THE IMPACT OF AI ON THE WORLD ECONOMY: 
    https://www.itu.int/dms_pub/itu-s/opb/gen/S-GEN-ISSUEPAPER-2018-1-PDF-E.pdf
  3. USA-China-EU plans for AI: where do we stand: https://ec.europa.eu/growth/tools-databases/dem/monitor/sites/default/files/DTM_AI%20USA-China-EU%20plans%20for%20AI%20v5.pdf
  4. https://hbr.org/insight-center/interacting-with-ai
  5. https://sloanreview.mit.edu/projects/reshaping-business-with-artificial-intelligence/

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AI

This AI Prevents Bad Hair Days

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Louis Bouchard Hacker Noon profile picture

@whatsaiLouis Bouchard

I explain Artificial Intelligence terms and news to non-experts.

Could this be the technological innovation that hairstylists have been dying for? I’m sure a majority of us have had a bad haircut or two. But hopefully, with this AI, you’ll never have to guess what a new haircut will look like ever again.

This AI can transfer a new hairstyle and/or color to a portrait to see how it would look like before committing to the change. Learn more about it below!

Watch the video

References:

►The full article: https://www.louisbouchard.ai/barbershop/

►Peihao Zhu et al., (2021), Barbershop, https://arxiv.org/pdf/2106.01505.pdf

►Project link: https://zpdesu.github.io/Barbershop/

►Code: https://github.com/ZPdesu/Barbershop

Video Transcript

00:00

This article is not about a new technology in itself.

00:03

Instead, it is about a new and exciting application of GANs.

00:06

Indeed, you saw the title, and it wasn’t clickbait.

00:10

This AI can transfer your hair to see how it would look like before committing to the

00:15

change.

00:16

We all know that it may be hard to change your hairstyle even if you’d like to.

00:19

Well, at least for myself, I’m used to the same haircut for years, telling my hairdresser

00:24

“same as last time” every 3 or 4 months even if I’d like a change.

00:29

I just can’t commit, afraid it would look weird and unusual.

00:33

Of course, this all in our head as we are the only ones caring about our haircut, but

00:38

this tool could be a real game-changer for some of us, helping us to decide whether or

00:43

not to commit to such a change having great insights on how it will look on us.

00:48

Nonetheless, these moments where you can see in the future before taking a guess are rare.

00:53

Even if it’s not totally accurate, it’s still pretty cool to have such an excellent approximation

00:57

of how something like a new haircut could look like, relieving us of some of the stress

01:02

of trying something new while keeping the exciting part.

01:06

Of course, haircuts are very superficial compared to more useful applications.

01:10

Still, it is a step forward towards “seeing in the future” using AI, which is pretty cool.

01:17

Indeed, this new technique sort of enables us to predict the future, even if it’s just

01:22

the future of our haircut.

01:24

But before diving into how it works, I am curious to know what you think about this.

01:28

In any other field: What other application(s) would you like to see using AI to “see into

01:34

the future”?

01:38

It can change not only the style of your hair but also the color from multiple image examples.

01:44

You can basically give three things to the algorithm:

01:47

a picture of yourself a picture of someone with the hairstyle you

01:51

would like to have and another picture (or the same one) of the hair

01:55

color you would like to tryand it merges everything on yourself realistically.

01:59

The results are seriously impressive.

02:02

If you do not trust my judgment, as I would completely understand based on my artistic

02:06

skill level, they also conducted a user study on 396 participants.

02:12

Their solution was preferred 95 percent of the time!

02:17

Of course, you can find more details about this study in the references below if this

02:21

seems too hard to believe.

02:22

As you may suspect, we are playing with faces here, so it is using a very similar process

02:27

as the past papers I covered, changing the face into cartoons or other styles that are

02:33

all using GANs.

02:34

Since it is extremely similar, I’ll let you watch my other videos where I explained how

02:39

GANs work in-depth, and I’ll focus on what is new with this method here and why it works

02:45

so well.

02:46

A GAN architecture can learn to transpose specific features or styles of an image onto

02:52

another.

02:53

The problem is that they often look unrealistic because of the lighting differences, occlusions

02:58

it may have, or even simply the position of the head that are different in both pictures.

03:04

All of these small details make this problem very challenging, causing artifacts in the

03:09

generated image.

03:10

Here’s a simple example to better visualize this problem, if you take the hair of someone

03:11

from a picture taken in a dark room and try to put it on yourself outside in daylight,

03:12

even if it is transposed perfectly on your head, it will still look weird.

03:13

Typically, these other techniques using GANs try to encode the pictures’ information and

03:15

explicitly identify the region associated with the hair attributes in this encoding

03:21

to switch them.

03:22

It works well when the two pictures are taken in similar conditions, but it won’t look real

03:27

most of the time for the reasons I just mentioned.

03:30

Then, they had to use another network to fix the relighting, holes, and other weird artifacts

03:36

caused by the merging.

03:38

So the goal here was to transpose the hairstyle and color of a specific picture onto your

03:43

own picture while changing the results to follow the lighting and property of your picture

03:49

to make it convincing and realistic all at once, reducing the steps and sources of errors.

03:55

If this last paragraph was unclear, I strongly recommend watching the video at the end of

03:56

this article as there are more visual examples to help to understand.

03:57

To achieve that, Peihao Zhu et al. added a missing but essential alignment step to GANs.

04:01

Indeed, instead of simply encoding the images and merge them, it slightly alters the encoding

04:07

following a different segmentation mask to make the latent code from the two images more

04:12

similar.

04:13

As I mentioned, they can both edit the structure and the style or appearance of the hair.

04:18

Here, the structure is, of course, the geometry of the hair, telling us if it’s curly, wavy,

04:24

or straight.

04:25

If you’ve seen my other videos, you already know that GANs encode the information using

04:30

convolutions.

04:31

This means it uses kernels to downscale the information at each layer and makes it smaller

04:37

and smaller, thus iteratively removing spatial details while giving more and more value to

04:43

general information to the resulting output.

04:46

This structural information is obtained, as always, from the early layers of the GAN,

04:52

so before the encoding becomes too general and, well, too encoded to represent spatial

04:58

features.

04:59

Appearance refers to the deeply encoded information, including hair color, texture, and lighting.

05:05

You know where the information is taken from the different images, but now, how do they

05:10

merge this information and make it look more realistic than previous approaches?

05:15

This is done using segmentation maps from the images.

05:18

And more precisely, generating this wanted new image based on an aligned version of our

05:24

target and reference image.

05:26

The reference image is our own image, and the target image the hairstyle we want to

05:31

apply.

05:32

These segmentation maps tell us what the image contains and where it is, hair, skin, eyes,

05:38

nose, etc.

05:40

Using this information from the different images, they can align the heads following

05:44

the target image structure before sending the images to the network for encoding using

05:49

a modified StyleGAN2-based architecture.

05:52

One that I already covered numerous times.

05:55

This alignment makes the encoded information much more easily comparable and reconstructable.

06:00

Then, for the appearance and illumination problem, they find an appropriate mixture

06:05

ratio of these appearances encodings from the target and reference images for the same

06:11

segmented regions making it look as real as possible.

06:15

Here’s what the results look like without the alignment on the left column and their

06:19

approach on the right.

06:21

Of course, this process is a bit more complicated, and all the details can be found in the paper

06:26

linked in the references.

06:27

Note that just like most GANs implementations, their architecture needed to be trained.

06:32

Here, they used a StyleGAN2-base network trained on the FFHQ dataset.

06:38

Then, since they made many modifications, as we just discussed, they trained a second

06:42

time their modified StleGAN2 network using 198 pairs of images as hairstyle transfer

06:50

examples to optimize the model’s decision for both the appearance mixture ratio and

06:55

the structural encodings.

06:57

Also, as you may expect, there are still some imperfections like these ones where their

07:02

approach fails to align the segmentation masks or to reconstruct the face.Still, the results

07:08

are extremely impressive and it is great that they are openly sharing the limitations.

07:13

As they state in the paper, the source code for their method will be made public after

07:18

an eventual publication of the paper.

07:21

The link to the official GitHub repo is in the references below, hoping that it will

07:25

be released soon.

07:27

Thank you for watching!

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Artificial Intelligence

The rising importance of Fintech innovation in the new age

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The rising importance of Fintech innovation in the new age

The rise of fintech has opened an array of opportunities for smart cities to develop and thrive. Its importance has actually increased in the age of the pandemic that calls for social distancing or contactless transactions.

The leading global payment solutions provider Visa recently indicated the increasing role of digital payments. Thanks to the expanding role of fintech, digital payments are expected to enter different smart city sectors.

Reportedly, fintech application is going to be instrumental in the transportation sector. It will come to people in different forms of contactless payments. It will also ease the process of paying for parking or hiring bikes and scooters.

More than that, whether it’s about loans, money transfer, investment, accounting and bookkeeping, airtime or fundraising. Smart cities and businesses are going to hugely rely on fintech in the coming future. 

Going ahead, we are delving into understanding the fintech situation in three smart cities. All three are important fintech hubs that the entire world looks upon.

London

In the smart city culture, London has the reputation of being the ‘fintech capital’ of the world. The number of fintech giants in the city is valued at more than $1 billion.

However, the pandemic has caused a number of businesses to shut down. At the same time, it has also catalysed the shift to digital and contactless. Businesses are now adopting new ways to support their customers.

Even in this time of crisis, London is at the foremost position of producing the next generation of fintech leaders. This is as per the Ed Lane, VP of Sales for the EMEA region at nCino, a US-based cloud banking provider. 

Remote work is becoming a necessity due to COVID-19. Hence, investments in different technologies and solutions in financial organisations and service providers are “more important than ever”. And so Lane claims that this has increased the adoption of cloud-based banking software developed by his firm. 

The UK recently introduced the Bounce Back Loan Scheme and the Coronavirus Business Interruption Loan Scheme (CBILS). This is helping Lane’s company nCino and others. They are offering a Bank Operating System to aid SMEs with effective processing of loan applications. 

Fintech companies are surviving and tapping into benefits in the COVID-19 age due to their disruptive mindset. The dot.com crash of 2001 and the financial crash of 2008 are drivers that lead them to become proactive.

Innovatively, fintech companies started offering mobile banking, online money management tools and other personalised solutions. Today, the same is enabling them to prevail during this pandemic. Besides all, partnerships have proven to be key strategies in achieving even the impossible, as experts say. 

Singapore

Singapore is showcasing a pioneering move in the fintech industry. Fintech is at the core of Singapore’s vision to become a ‘Smart Nation’ with a “Smart Financial Centre.”

To achieve the dream, the city-state has been showing constant efforts by using innovative technology. With this, it intends to pave the way for new opportunities, enhance efficiency and improve national management of financial risks.

Until 2019, Singapore was already home to over 600 fintech firms. These companies attracted more than half of the total funding for the same year. And amidst the COVID-19 pandemic, the Monetary Authority of Singapore (MAS) introduced two major support packages.

First on April 8, 2020, it announced a S$125 million COVID-19 care package for the financial and fintech sectors. This package aims at aiding the sectors in fighting the challenges from the COVID-19 health crisis. It will help in supporting workers, accelerate digitalisation, and improve operational readiness and resilience. 

Second, on May 13, 2020, MAS, the Singapore Fintech Association (SFA) and AMTD Foundation launched the MAS-SFA-AMTD Fintech Solidarity Grant. The S$6 million grant proposes to support Singapore-based fintech firms.

A specific focus is on managing cash flow, producing new sales and seeking growth strategies. At the individual level, many industry participants have launched their own initiatives to support the sector.

Hong Kong

HongKong’s fintech startup sector tells us a different story which involves the role of blockchain. Blockchain-based companies are dominating the city’s startup sector.

In 2019, enterprise DLT and crypto-assets exchanges earned rankings as the most popular sectors in Hong Kong’s fintech industry. The report comes from the Financial Services and Treasury Bureau. It confirms that blockchain startups make up 40% of the 57 Fintech firms established in the city in 2019.

As per reports, 45% of new companies are focused on developing applications for large businesses. This is the reason that enterprise blockchain firms were the most popular. Another 27% account for blockchain-related firms in Hong Kong involved in digital currency.  

The increase in the number of blockchain-based fintech startups is due to the Special Administrative Region of the People’s Republic of China. The authority introduced new policies towards blockchain tech development – making it a priority.

Blockchain is thriving in Hong Kong due to a number of reasons. The city has laid down clear regulatory guidelines for blockchain-related businesses. Many have leveraged the benefits of the QMAS program. It enables applicants to settle down in the region before having to look for employment. This has immensely encouraged several blockchain specialists to move to Hong Kong.

The city government is also entering partnerships to expand its fintech footprint in the right direction. For example, in November 2019, the government collaborated with Thailand’s officials to explore the development of Central Bank Digital Currencies (CBDCs). Blockchain is a promising technology for the fintech industry. It supports quick, secure and cost-effective transaction-related services.

More importantly, it provides transparency that other traditional technologies were not capable of. Thanks to the use of encrypted distributed ledgers. These enable real-time verification of transactions without the need for mediators such as correspondent banks.

Why Is Fintech Innovation Important For The Development Of Smart Cities?

Fintech Boosting Business And Growth Opportunities In Smart Cities

Advanced cities that are now smart cities have been using fintech for their development. With that, they are also leading the way for others to follow. Many experts confirm that innovation in fintech is a must for any city to become a ‘smart city.’

It enables easy national as well as international business. For the residents, it makes life more convenient by encouraging contactless, economical, sustainable and efficient payment-related operations. 

One important aspect that smart city development and fintech innovation has in common is their determination to cut bureaucracy. A city that manages to enable speedy and inexpensive international transfers will also enable its citizens with greater access to the global market. This is as said by Hans W. Winterhoff from KPMG in one of his articles.

Furthermore, fintech innovations of the past have demonstrated their success. Some fintech applications have simplified procedures that became unnecessarily complex over time. Traditional banking services are one of the biggest examples. 

The innovative fintech services opened doors for online shopping and easy international money transfers. Fintech is able to provide the same product or service to consumers. But that’s happening in less time, with fewer steps, and at more affordable rates.

Besides, transparency is another important factor that is allowing consumers to have faith in fintech services. With the current potential of fintech, we can now say that it is one of the essential pillars of successful smart city development. The results are already here in the age of this pandemic.

Coinsmart. Beste Bitcoin-Börse in Europa
Source: https://www.fintechnews.org/the-rising-importance-of-fintech-innovation-in-the-new-age-2/

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