What is Fintech?

Considering some definitions provided by Oxford English Dictionary (n.d.) and Shueffel (2016), Fintech is represented by a set of computer technologies that are to assist in and enable financial services to improve such aspects as customer service or data processing. Despite that some definitions presume its centrality to financial services, some highlight its external nature, as a variety of processes being added to existing infrastructure (for example, the addition of new technology like automated processes or machine learning). The term among the folk has started its rise in 2011, with some rare mentions to skyrocket starting from 2015, as many aspiring post-crisis startups and technologies began to receive prominence.

Common Features of Fintechs

Fintech organisations tend to have a variety of similar traits that are worth highlighting. 

1. Fintech businesses are “need-focused,” which means they unbundle financial services and rely on customisation to create personalised solutions.
2. They tend to expand the services of incumbents to previously ill-covered customer groups or create totally new forms of service.
3. Customers, as a result of their strategically central role in developing the businesses’ value propositions, are facing the blurring line between who makes products and services and who utilises them, since the creation of a platform frequently entails just bridging the network rather than fully separating each participant.
4. Quite often one of the premises is to take out third parties or additional steps of the process.
5. The need for expansion and fresh views on user experience/user interface are pushing forward the simplicity and “playfulness” of brand interactions.

Fintech Landscape

In its quest to capture the landscape of Fintech in Australia, KPMG (2020) divided Fintech among the following domains (plus a couple of other we considered essential):

Fintech Origins

In a study report published in 2015, Arner et al., (2015) attempted to evaluate the evolution of Fintech. “The term’s roots may be traced to the early 1990s and related to the ‘Financial Services Technology Consortium,’ an initiative started by Citigroup to assist technical collaboration efforts,” write Ibid. Indeed, the authors’ source, an article published by the media site American Banker, references a Citigroup initiative called “Fintech” that began in the early 1990s (Hochstein, 2015). The American Banker also released another story titled “Friday Flashback: Did Citi Coin the Term ‘Fintech’?” on the term Fintech. “[t]he story below published in American Banker in August 1993, and features the first use we could discover of the now-trendy phrase ‘Fintech’”, according to an editor’s note (Ibid). It references Fintech as a project label used by Citibank. This is the first paper cited by Hochstein (2015) and subsequently Arner et al. (2015) when claiming that the term Fintech originated in the early 1990s.

Despite this, the word Fintech was coined in 1972. Mr Abraham Bettinger, from Hanover Trust, provided the following definition in a scholarly article detailing models on how he had analysed and solved daily banking problems encountered at the bank (Oshodin et al., 2019): “Fintech is an acronym that stands for financial technology, combining bank expertise with modern management science techniques and the computer (Bettinger, 1972). Bettinger’s work was not wholly overlooked during his lifetime, as evidenced by an early reference of his work by Warschauer several years after (Oshodin et al., 2019). Even so, it’s possible that the Citibank Fintech project’s imitators in the early 1990s were unaware of Bettinger’s study and chose the same title for their initiative by chance. It is important noting at this time that neither academia nor practice can be definitively recognised as the birthplace of the word Fintech, given the phrase was coined by a practitioner in a scientific journal article.

History of Fintech

Yet, the development itself did not seem to start that late and could be associated with early 1950s, as the first stage of the transit from physical means of information exchange to digital among financial service providers. It could be linked to first attempts to create a credit card by Diners Club International (n.d.), or progress on Federal Reserve Wire System. According to Nelson (2018), in late June 1967, Barclays installed the first ATM. Though quite coarse when it comes to functionalities, such as a limit of £10 or frequent bugs, it has marked a huge leap forward as it lifted long queues and expanded the service opening time.

Later, the 1970s showcased some other major shifts such as the FINRA-led creation of Nasdaq, which could partly be regarded as the first digital exchange. Though, it needed years to shift from the over-the-counter nature and meaningfully automate reporting and trading (Widder, 1992). One would also mention the creation of SWIFT, i.e. the framework for international bank communication. Its exchange software and messaging standards created a fertile ground for automated and unimpeded interaction between thousands of institutions (SWIFT, n.d.).

Then, the process gained momentum: the 1980s opened the stage for online banking (yet only partial); the 1990s popularised e-commerce and introduced more participants to this movement. Though the biggest impact was brought by the global shake-up, as old institutional arrangements have not only led the world economy to turmoil but also gave birth to a massive distrust in the system by people. As a result, ideas for alternatives were welcomed, ranging from less conventional ways to improve the legacy systems (i.e. make them more transparent or reduce transaction costs) to totally new ways of thinking (like that proposed by blockchain). As some of these endeavours have proved utterly successful, more attention has been paid to corresponding ventures in the following years. Though, it is not yet known whether this phenomenon will peak and fade any time soon, or whether any single set of arrangements will displace the other.

Fintech-related Terms

Thanks to some research provided by Deloitte (n.d.) and CFTE’s older publications, here one could find a list of some of the most common terms associated with Fintech, and thus be able to expand their knowledge.

Fintech Examples

1. Lufax

Once focused on P2P lending, Lufax is a technology-driven online Internet financial marketplace that provides retail credit and wealth management services. 

2. Tinkoff

Made as an experiment for the Russian market, Tinkoff is the region’s digital banking champion, highlighting openness and big data solutions.

3. Brex

Brex, a shining example of a Silicon Valley unicorn, fills a developing vacuum in banking ambitious tech startups, assisting them with cash management and credit needs.

Fintech Regulation

The centricity of technology has provided massive regulatory advantages, as many entities to challenge the traditional incumbents were not subject to regulatory scrutiny. However, as they were increasingly becoming full-fledged players in the industry, be them independent organisations or vassals of larger traditional banking institutions, they are now getting exposed to existing regulation or supplied with the new one.

The major driver of this attention, however, is the state’s sensitivity to potential contagion effects, as consumer bases of the former are growing remarkably. Special attention is currently paid to blockchain-related phenomena, as state representatives and major institutions behind are utterly uncomfortable with potential economic shocks, Ponzi schemes, loss of central power and transaction transparency/irreversibility.

Fintech Largest Centres

fintech startupsrobo advisorsbuy or selltraditional bankingstock trading app

The post What is Financial Technology (Fintech)? appeared first on CFTE.

In today’s day and age, it’s next to impossible to have not completed even a single task without the help of a virtual assistant who adheres to your ‘voice command’. A growing preference for voice interaction with virtual assistants and bots is booming, and it could be because ‘voice’ has always been the most natural way to engage with people and customers too – as the tech giant, Amazon, claims. A voice interface also provides convenience to millions as they save on time and the cognitive burden that goes into typing a query they want answered or a task they want done.

This raises an important question today – what makes this convenience laden technology a reality? 

And is it this the same technology that gave birth to Siri, Alexa and Google Assistant?

Like most revolutionary technologies, this too is driven by AI technology – particularly conversational artificial intelligence. It would be enriching to understand what this technology is and how it has empowered products like Siri, Alexa and Google Assistant to have more human-like capabilities.

What is Conversational Artificial Intelligence?

Conversational artificial intelligence can be defined as an intelligent mechanism of imitating real-life human conversations. This game-changing technology is made possible as it is built on the sturdy foundation of machine learning (ML), and natural language processing (NLP). By feeding in extensive volumes of data, machines can be wired to learn the essence of human interactions, giving them the power to recognise speech and text inputs while also translating its meaning into an array of desired languages.

Are Siri, Alexa and Google Assistant examples of conversational AI?

Yes! Technologies like Siri, Alexa and Google Assistant that are ubiquitous in every household today are excellent examples of conversational AI.  These conversational AI bots are more advanced than regular chatbots that are programmed with answers to certain questions. These virtual assistants are configured to be more human-like, generating responses that are more natural and aligned to real human conversations as compared to chatbot conversations. 

If you have ever asked your virtual assistant, “What’s the weather like today?” Or asked it to, “Play my saturday night music playlist!”, this intelligent machine is able to give you the right answer in a matter of seconds and can perform the task quicker than if you had to lift a finger. All this is made possible with conversational software created with artificial intelligence, which has led to an explosive growth of a number of voice assistants across the world. 

But how are these advanced virtual assistants able to understand human commands and perform a variety of tasks?

Let’s investigate!

How does Siri, Alexa and Google Assistant’s Conversational AI Technology Work?

Machine learning is an integral part of giving Siri, Alexa and Google Assistant the human superpowers they possess today. Machine learning  is an application of Artificial Intelligence that focuses on training systems to improve their ability to learn so they can better perform tasks – in this case, interact better with humans. This is done by feeding data to computer systems so that they can automatically analyse patterns to guide decision-making in the future. What’s exciting is that machines learn on their own without the need of having to be programmed by a human, allowing them to develop greater capabilities. Other fields of machine learning like natural language processing and deep learning are also integral in helping conversational AI become a reality.

So each time Alexa, Siri or Google assistant make an error while responding to your query, it uses this information to learn and rectify its mistake in the future. Thus by taking in data of when it makes a mistake or when its response was accurate it uses this as a rulebook to guide its behaviour in the future.  

Though this process could be deceptively simple from the outside, it involves the synthesises of a number of complex processes behind the scenes to get the desired result. To help break down these processes into understandable bites of information, we compiled the following steps to explain how conversational AI works:

Step 1 : Automatic Speech Recognition (ASR)

Once a query is shot out, the voice assistant’s artificial intelligence must understand what has been asked through automatic speech recognition (ASR), which converts speech to text. 

Step 2  : Natural Language Understanding (NLU)

Once this is done, natural language processing through natural language understanding (NLU), helps the system interpret the input to understand the sentiment and intent behind the query. It deciphers this despite the grammatical errors or shortcuts. 

Step 3 : Machine Learning to Configure a Response

Next, to fabricate an appropriate response, the machine determines the correct answer based on the context of the user’s intent, with the help of machine learning. Over time, by learning different variations of a query guided by the same intent, the machine learns the most appropriate response.

Step 4 : Natural Language Generation (NLG)

After having synthesized all this information, the machine now works to produce a comprehendible response with the help of natural language processing through natural language generation (NLG). Whether it may be completing a task or answering a question, it completes the process in a way that is more akin to human interaction.

Step 5 : Text-to-speech Software

With a response in place, the virtual assistant now translates the text into a voice response with text-to-speech software.

To summarize how conversational AI works:

1. Automatic speech recognition (ASR) converts speech to text.
2. Natural language understanding (NLU) interprets the query intent.
3. Machine learning helps configure a response.
4. Natural language generation (NLG) helps generate a human-like response.
5. Text-to-speech software converts the generated response into speech.

Share our infographic to help your network understand how your virtual assistant’s conversational AI works!

Now that we understand the nuts and bolts of what facilitates a conversation with a robot, it would also be worth considering why this technology is useful at all! 

Consumers are always on the hunt for the next best experience or product to make their life easier and this radically changes their behaviour over time as they adapt to new technologies.

Businesses today are designed with customers taking center stage at every step – from conception to execution. With this as a guiding principle for businesses, when any revolutionary technology props up in the market to bolster that mission, it is impossible to go forward without paying heed to those technological advancements. To paint a clear picture of why conversational AI can help business owners take their offerings up a notch, let’s explore the benefits of conversational AI for businesses.

Why is Conversation AI Important for your Business?

1. Enhancing Customer Experience

With the evolution of myriad businesses missioned to simplify our lives with tailored apps on smart devices, one can often have a disjointed experience toggling from one app to another. This predicament has led to the rise of the voice assistant as a single interface for interaction. Though popularly used as virtual assistants at home to dim the lights or play some music, the possible applications of voice assistants can go well beyond the regular uses we see.

For instance, we now have conversational AI for banking whereby customers can bid adieu to complex navigation menus and could check their account balances by simply asking their smart device. 

2. Optimising Repetitive Tasks

From e-commerce sites to call centres, business employees are often burdened with a continuous flow of repetitive tasks. Though robotic process automation (RPA) is designed to perform recurring tasks, they aren’t always performed with a customer-centric approach in mind. In these cases using conversational AI to generate more intuitive and real, human interactions can help fill in the widened chasm today. This will also help to automate a high volume of tasks and interactions in the avenue of customer servicing while giving support staff the bandwidth to deal with more pressing issues.

3. Gaining Insights from a Wealth of Data

Conversational AI, being an unstructured mechanism of communication gathers information during every interaction for algorithmic improvement. This generates rich data that can be utilised to create better customer insights and drive the business forward by closely aligning to user objectives. 

Conclusion

Though today, businesses have not successfully been able to navigate the range of possibilities created by conversational AI, this sector is on track to radically change the way consumers interact with businesses. The conversational AI market size is set to grow from $4.8 Billion in 2020 to a staggering $13.9 Billion by 2025, at a Compounded Annual Growth Rate of 21.9%. The main drivers of this unprecedented growth are increasing demand for conversational AI for customer service, the possibility of omnichannel deployment and a reduction in development costs of these advanced technologies. As organisations realise the true value of conversational AI and grapple for successful execution, they will need to combat the lack of accuracy seen in this technology today and innovate to produce better solutions. As innovation advances, it’s safe to say that the applicability of conversational AI could be endless.

The post Conversational AI Examples: How Siri, Alexa & Google Assistant have Human-like Conversations appeared first on CFTE.

In today’s digitally enhanced world, automation is lurking in every corner of our lives – from your Alexa speaker at home to driving decisions in the boardroom. As this technology infiltrates every-day life, it is hard not to hear the buzzwords – Artificial intelligence (AI) and Machine Learning (ML), ringing in our ears. 

People often use the terms interchangeably – but are they really one and the same thing? Not exactly.

What is Artificial Intelligence?

Artificial Intelligence can be defined as the science of training machines to perform human tasks. AI is the method of developing computer systems to emulate human intelligence and is also the engineering behind giving machines human-like properties.

To successfully implement knowledge engineering, machines or computer software need to have access to a large amount of information to be programmed to act and think like a human. This helps build up the machine brain power needed to problem-solve, to allow analytical reasoning and to wire in common sense into a machine. Artificial intelligence has two main subsets: Machine learning and deep learning.

Artificial intelligence is ubiquitous in the field of tech today, and to better understand the concept, it would be worthwhile to get a grip on what artificial intelligence can do by identifying some of the AI applications that exist today. 

AI Applications:

• Image Recognition: Have you ever wondered how facebook automatically recognises faces to tag in a photo? This is an ideal example of AI being used for image recognition.

• Recommendations: AI if equipped with the right information can make recommendations for movies, music, products or services, be it through Spotify’s recommended playlists or targeted online advertisements.

• Natural Language Processing: AI has the capability of understanding human communication to respond in a way that is natural. A chatbot is a perfect example for this. They are programmed to interpret a wide array of queries to give you the best answer to your question.

What is Machine Learning?

To give you a comprehensive introduction to Machine learning (ML), it is necessary to first understand that ML is an element embedded into the wider realm of Artificial Intelligence. This means that machine learning is AI, but not all types of AI are machine learning. ML focuses on training systems to improve their ability to learn so they can better perform tasks. This learning is done by feeding data to computer systems so that they can automatically analyse patterns to guide decision making in the future. This process does not require humans to explicitly hard code tasks to be performed, but allows machines to learn on its own. 

For machine’s to be exceptionally advanced at ‘doing by learning’, they require large amounts of granular data that covers a sizably diverse area. Much like humans who learn to do certain things from experience, machines are able to do the same but in a matter of seconds.

Machine learning can be segmented into 3 types:

1. Supervised learning

To enable supervised machine learning, systems need to have data with observations as well as the labels and classifications of that data. To give a simple example, a set of data can consist of images of both cats and dogs that are labeled for the machine to interpret. In the future, this knowledge is then used to predict whether an image presented is a cat or dog.

2. Unsupervised learning

In this case machines require a dataset with observations but without the compulsion of the data being classified or labeled. So if a machine was to learn how to cross a road, you wouldn’t teach it to do so with precise rules, but rather show it a set of 10,000 videos of someone crossing safely and another 10,000 showing the opposite. The machine is then left to learn on its own.

3. Reinforcement learning

This kind of learning is evident from the name itself, the machine is penalised or rewarded based on its performance in an attempt to help it learn the right way of doing a task. If the error is low the reward is high, if the error is high then the penalty is high.

To draw a clearer picture of what is machine learning, and to help see how machine learning is more specific as compared to the broader concept of AI, let’s dive into some of the ways machine learning is used today.

Machine Learning Applications:

• Segmenting Data: Machine learning can help carry out targeted online campaigns, as mentioned under the applications of AI, by segmenting the large database of customers to give you segments based on some criteria, who can then be individually targeted. 

• Predicting Outcomes: Machine learning is known to assimilate data continually and learn from it to improve predictions. An example of this would be estimating the ETAs for rides with a cab like Uber. The machine learns from experience the time it would take to reach a certain location at a certain time and makes predictions accordingly.

To help clearly Identify the differences between Artificial Intelligence and Machine Learning, here is a concise comparison table of Artificial Intelligence versus Machine Learning:

Artificial Intelligence versus Machine Learning

The post What is the Difference between Artificial Intelligence (AI) and Machine Learning (ML)? appeared first on CFTE.

4 Fintech Trends That Will Transform 2019

The past twelve months proved to be a banner year for Fintech, read more about that in our summary of the industry in 2018, as emerging technologies like artificial intelligence and machine learning created many new applications in financial services. Can the same be expected for the next calendar year? Absolutely.
Below, we outline four key Fintech trends that will likely transform financial services in some way in 2019.

1. Artificial Intelligence Sees Increased Adoption

After observing the impact of Artificial Intelligence across many other industries, we are truly witnessing the positive consequences of its effective implementation in Finance. 2019 will see greater adoption of Artificial Intelligence in financial services applications, backed by machine learning.
David Sosna, co-founder & CEO of Personetics, claims

“Self-Driving Finance is a practical implementation of AI that is already used in one form or another by millions of bank customers around the globe and will only get better in the coming years. Based on projects that are currently underway with banks at different parts of the world, I see a big uptake in the number of customers that will rely on AI to ‘drive’ their finances and take automated actions to help them reach their financial goals.”

Furthermore, consumers can expect more prominent voice-operated assistants in banking, with the likes of OCBC Bank partnering with Google to recently launch the first AI-powered voice banking service in Singapore. The service can converse with customers about bank services like future savings, nearby ATMs and calculate loan interests.

2. Blockchain and Financial Inclusion

In 2019, Blockchain will enable financial inclusion for more individuals than ever before, by offering the unbanked easy access to a verifiable online identity. Most of this will occur in regions with poor entry-level banking accessibility, such as South Asia, Africa and Latin America. An IBM study expects 65 percent of banks to have adopted blockchain technology by 2019’s end.
This increase will mean a massive amount of people newly contributing to the global economy, and will generate more opportunities for individuals and businesses to improve their financial standing and grow by building credit.

3. Growth of Mobile Payments

Already an enormous industry, processing over a billion dollars daily in 2017, the mobile banking and payments sector will see meteoric growth through the new year, with widespread adoption coming through areas of significant financial exclusion, such as Latin America, South Asia and sub-Saharan Africa. In the latter, 21 percent held a mobile money account in 2017, which is double the figure of 2014, illustrating the growing trajectory of adoption in underdeveloped banking regions.
Tied into blockchain technology, it will provide individuals an accessible method of becoming part of the financial ecosystem without approaching the high barriers to entry offered by traditional banking. This is driven by unanimous adoption of smartphones, which when coupled with access to the internet provides users the opportunity of financial management without the necessity of opening a traditional bank account at a retail location, a problem for plenty in rural areas.

4. Rise of Digital Banks

Traditional banking has had a successful and prosperous long run. While it would be premature to ring the death knell for the incumbents in finance, they are definitely very concerned about the rise of digital-only banks. Banks without a physical presence, so no waiting in lines to open an account or to seek financial guidance. Banks with financial services you can receive from the comfort of your homes and offices, at the tap of an app or secured website.
These digital-only banks are gaining rapid momentum with millenials, with bank visits expected to drop 36 percent by 2022. Investors are also taking sharply to the concept of a banking institution without a physical presence, with Monzo recently raising £20m – the largest ever crowdfunding round for a Fintech startup – in just 2 hours earlier this month. Another instance can be found in Revolut, a finance app that facilitates worldwide currency and cryptocurrency exchange. As of late 2018, Revolut has more than 2 million users, with around 8,000 new users signing up for the service on a daily basis.

How can I get prepared for Fintech in 2019?

Understanding the intricacies of financial technology has never been more important to succeed in finance. Luckily, CFTE has bridged this knowledge gap with our Fintech Foundation Course — designed for learners to get a solid foundation in Fintech concepts, applicable for beginners & professionals. Feel free to join our courses above, or sign up to our weekly newsletter after the jump below!
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4 Fintech Trends That Will Transform 2019

The past twelve months proved to be a banner year for Fintech, read more about that in our summary of the industry in 2018, as emerging technologies like artificial intelligence and machine learning created many new applications in financial services. Can the same be expected for the next calendar year? Absolutely.
Below, we outline four key Fintech trends that will likely transform financial services in some way in 2019.

1. Artificial Intelligence Sees Increased Adoption

After observing the impact of Artificial Intelligence across many other industries, we are truly witnessing the positive consequences of its effective implementation in Finance. 2019 will see greater adoption of Artificial Intelligence in financial services applications, backed by machine learning.
David Sosna, co-founder & CEO of Personetics, claims

“Self-Driving Finance is a practical implementation of AI that is already used in one form or another by millions of bank customers around the globe and will only get better in the coming years. Based on projects that are currently underway with banks at different parts of the world, I see a big uptake in the number of customers that will rely on AI to ‘drive’ their finances and take automated actions to help them reach their financial goals.”

Furthermore, consumers can expect more prominent voice-operated assistants in banking, with the likes of OCBC Bank partnering with Google to recently launch the first AI-powered voice banking service in Singapore. The service can converse with customers about bank services like future savings, nearby ATMs and calculate loan interests.

2. Blockchain and Financial Inclusion

In 2019, Blockchain will enable financial inclusion for more individuals than ever before, by offering the unbanked easy access to a verifiable online identity. Most of this will occur in regions with poor entry-level banking accessibility, such as South Asia, Africa and Latin America. An IBM study expects 65 percent of banks to have adopted blockchain technology by 2019’s end.
This increase will mean a massive amount of people newly contributing to the global economy, and will generate more opportunities for individuals and businesses to improve their financial standing and grow by building credit.

3. Growth of Mobile Payments

Already an enormous industry, processing over a billion dollars daily in 2017, the mobile banking and payments sector will see meteoric growth through the new year, with widespread adoption coming through areas of significant financial exclusion, such as Latin America, South Asia and sub-Saharan Africa. In the latter, 21 percent held a mobile money account in 2017, which is double the figure of 2014, illustrating the growing trajectory of adoption in underdeveloped banking regions.
Tied into blockchain technology, it will provide individuals an accessible method of becoming part of the financial ecosystem without approaching the high barriers to entry offered by traditional banking. This is driven by unanimous adoption of smartphones, which when coupled with access to the internet provides users the opportunity of financial management without the necessity of opening a traditional bank account at a retail location, a problem for plenty in rural areas.

4. Rise of Digital Banks

Traditional banking has had a successful and prosperous long run. While it would be premature to ring the death knell for the incumbents in finance, they are definitely very concerned about the rise of digital-only banks. Banks without a physical presence, so no waiting in lines to open an account or to seek financial guidance. Banks with financial services you can receive from the comfort of your homes and offices, at the tap of an app or secured website.
These digital-only banks are gaining rapid momentum with millenials, with bank visits expected to drop 36 percent by 2022. Investors are also taking sharply to the concept of a banking institution without a physical presence, with Monzo recently raising £20m – the largest ever crowdfunding round for a Fintech startup – in just 2 hours earlier this month. Another instance can be found in Revolut, a finance app that facilitates worldwide currency and cryptocurrency exchange. As of late 2018, Revolut has more than 2 million users, with around 8,000 new users signing up for the service on a daily basis.

How can I get prepared for Fintech in 2019?

Understanding the intricacies of financial technology has never been more important to succeed in finance. Luckily, CFTE has bridged this knowledge gap with our Fintech Foundation Course — designed for learners to get a solid foundation in Fintech concepts, applicable for beginners & professionals. Feel free to join our courses above, or sign up to our weekly newsletter after the jump below!
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b3giXSArIHNwYW4gbGFiZWwgeyBkaXNwbGF5OmlubGluZTsgfQogI19mb3JtXzlfIC5fcm93IHNwYW4gbGFiZWwgeyBtYXJnaW46MCAhaW1wb3J0YW50OyB3aWR0aDppbml0aWFsICFpbXBvcnRhbnQ7IHZlcnRpY2FsLWFsaWduOm1pZGRsZSAhaW1wb3J0YW50OyB9CiAjX2Zvcm1fOV8gLl9mb3JtLWltYWdlIHsgbWF4LXdpZHRoOjEwMCU7IGhlaWdodDphdXRvICFpbXBvcnRhbnQ7IH0KICNfZm9ybV85XyBpbnB1dFt0eXBlPSJ0ZXh0Il0geyBwYWRkaW5nLWxlZnQ6MTBweDsgcGFkZGluZy1yaWdodDoxMHB4OyBmb250LXNpemU6MTZweDsgbGluZS1oZWlnaHQ6MS4zZW07IC13ZWJraXQtYXBwZWFyYW5jZTpub25lOyB9CiAjX2Zvcm1fOV8gaW5wdXRbdHlwZT0icmFkaW8iXSwjX2Zvcm1fOV8gaW5wdXRbdHlwZT0iY2hlY2tib3giXSB7IGRpc3BsYXk6aW5saW5lLWJsb2NrOyB3aWR0aDoxLjNlbTsgaGVpZ2h0OjEuM2VtOyBmb250LXNpemU6MWVtOyBtYXJnaW46MCAwLjNlbSAwIDA7IHZlcnRpY2FsLWFsaWduOmJhc2VsaW5lOyB9CiAjX2Zvcm1fOV8gYnV0dG9uW3R5cGU9InN1Ym1pdCJdIHsgcGFkZGluZzoyMHB4OyBmb250LXNpemU6MS41ZW07IH0KICNfZm9ybV85XyAuX2lubGluZS1zdHlsZSB7IG1hcmdpbjoyMHB4IDAgMCAhaW1wb3J0YW50OyB9CiB9CiAjX2Zvcm1fOV8geyBwb3NpdGlvbjpyZWxhdGl2ZTsgdGV4dC1hbGlnbjpsZWZ0OyBtYXJnaW46MjVweCBhdXRvIDA7IHBhZGRpbmc6MjBweDsgLXdlYmtpdC1ib3gtc2l6aW5nOmJvcmRlci1ib3g7IC1tb3otYm94LXNpemluZzpib3JkZXItYm94OyBib3gtc2l6aW5nOmJvcmRlci1ib3g7ICp6b29tOjE7IGJhY2tncm91bmQ6I2ZmZiAhaW1wb3J0YW50OyBib3JkZXI6MHB4IHNvbGlkICNiMGIwYjAgIWltcG9ydGFudDsgd2lkdGg6NTAwcHg7IC1tb3otYm9yZGVyLXJhZGl1czowcHggIWltcG9ydGFudDsgLXdlYmtpdC1ib3JkZXItcmFkaXVzOjBweCAhaW1wb3J0YW50OyBib3JkZXItcmFkaXVzOjBweCAhaW1wb3J0YW50OyBjb2xvcjojMDAwICFpbXBvcnRhbnQ7IH0KICNfZm9ybV85XyAuX2Zvcm0tdGl0bGUgeyBmb250LXNpemU6MjJweDsgbGluZS1oZWlnaHQ6MjJweDsgZm9udC13ZWlnaHQ6NjAwOyBtYXJnaW4tYm90dG9tOjA7IH0KICNfZm9ybV85XzpiZWZvcmUsI19mb3JtXzlfOmFmdGVyIHsgY29udGVudDoiICI7IGRpc3BsYXk6dGFibGU7IH0KICNfZm9ybV85XzphZnRlciB7IGNsZWFyOmJvdGg7IH0KICNfZm9ybV85Xy5faW5saW5lLXN0eWxlIHsgd2lkdGg6YXV0bzsgZGlzcGxheTppbmxpbmUtYmxvY2s7IH0KICNfZm9ybV85Xy5faW5saW5lLXN0eWxlIGlucHV0W3R5cGU9InRleHQiXSwjX2Zvcm1fOV8uX2lubGluZS1zdHlsZSBpbnB1dFt0eXBlPSJkYXRlIl0geyBwYWRkaW5nOjEwcHggMTJweDsgfQogI19mb3JtXzlfLl9pbmxpbmUtc3R5bGUgYnV0dG9uLl9pbmxpbmUtc3R5bGUgeyBwb3NpdGlvbjpyZWxhdGl2ZTsgdG9wOjI3cHg7IH0KICNfZm9ybV85Xy5faW5saW5lLXN0eWxlIHAgeyBtYXJnaW46MDsgfQogI19mb3JtXzlfLl9pbmxpbmUtc3R5bGUgLl9idXR0b24td3JhcHBlciB7IHBvc2l0aW9uOnJlbGF0aXZlOyBtYXJnaW46MjdweCAxMi41cHggMCAyMHB4OyB9CiAjX2Zvcm1fOV8gLl9mb3JtLXRoYW5rLXlvdSB7IHBvc2l0aW9uOnJlbGF0aXZlOyBsZWZ0OjA7IHJpZ2h0OjA7IHRleHQtYWxpZ246Y2VudGVyOyBmb250LXNpemU6MThweDsgfQogQG1lZGlhIGFsbCBhbmQgKG1pbi13aWR0aDozMjBweCkgYW5kIChtYXgtd2lkdGg6NjY3cHgpIHsgI19mb3JtXzlfLl9pbmxpbmUtZm9ybS5faW5saW5lLXN0eWxlIC5faW5saW5lLXN0eWxlLl9idXR0b24td3JhcHBlciB7IG1hcmdpbi10b3A6MjBweCAhaW1wb3J0YW50OyBtYXJnaW4tbGVmdDowICFpbXBvcnRhbnQ7IH0KIH0KPC9zdHlsZT4KPGZvcm0gbWV0aG9kPSJQT1NUIiBhY3Rpb249Imh0dHBzOi8vY2Z0ZS5hY3RpdmVob3N0ZWQuY29tL3Byb2MucGhwIiBpZD0iX2Zvcm1fOV8iIGNsYXNzPSJfZm9ybSBfZm9ybV85IF9pbmxpbmUtZm9ybSAgX2RhcmsiIG5vdmFsaWRhdGU+CiAgPGlucHV0IHR5cGU9ImhpZGRlbiIgbmFtZT0idSIgdmFsdWU9IjkiIC8+CiAgPGlucHV0IHR5cGU9ImhpZGRlbiIgbmFtZT0iZiIgdmFsdWU9IjkiIC8+CiAgPGlucHV0IHR5cGU9ImhpZGRlbiIgbmFtZT0icyIgLz4KICA8aW5wdXQgdHlwZT0iaGlkZGVuIiBuYW1lPSJjIiB2YWx1ZT0iMCIgLz4KICA8aW5wdXQgdHlwZT0iaGlkZGVuIiBuYW1lPSJtIiB2YWx1ZT0iMCIgLz4KICA8aW5wdXQgdHlwZT0iaGlkZGVuIiBuYW1lPSJhY3QiIHZhbHVlPSJzdWIiIC8+CiAgPGlucHV0IHR5cGU9ImhpZGRlbiIgbmFtZT0idiIgdmFsdWU9IjIiIC8+CiAgPGRpdiBjbGFzcz0iX2Zvcm0tY29udGVudCI+CiA
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The post 4 Fintech Trends That Will Transform 2019 appeared first on CFTE.

Record Investment meets the Rise of AI in Finance: A Handy Summary of 2018 Fintech

Wow, what a whirlwind of a year in Fintech. Just as we predicted, 2018 proved itself to be a massive year of growth for Fintech firms and emerging technologies, like Artificial Intelligence, alike. Once rather placid with few rippling trends, fintech has transformed finance into a dynamic market with innovative firms competing to raise the bar above one another. As consumers continue to have greater expectations for personalised services across other industries like electronics and retail, the realm of financial services has realised the lucrativeness of meeting these needs.

Let’s sift through 2018 in Fintech, together:

More money than ever in Fintech

We’ll begin with the rather large elephant in the room. This last month saw the largest-ever crowdfunding round for a Fintech startup, with Monzo reaching their goal of £20m in just 2 hours and 45 minutes when it opened its round to the public on the morning of December 5th. Over 36,000 investors forked out their wallets to ensure Monzo is now valued 40 times more than their initial value during their first raise in early 2016. What’s more, this follows October’s £85 million Series E round, led by General Catalyst and Accel, which made the digital mobile-only bank the latest UK start-up to achieve bonafide unicorn status.

Speaking of Fintech unicorns, there are now about 29 of them merrily strolling the fields of finance, at an aggregated USD $84.4 billion. Q2 gave birth to five new unicorns, with Ribbit Capital and QED Investors the most active fintech unicorn investor.

Overall investment across venture capital, private equity and mergers & acquisitions are markedly above 2017’s totals. In H1, global investment in fintech firms surpassed $57.9 billion, with $16.8 and $26 billion of this going to the Asian and European continents respectively. Moreover, global median venture deal size for late stage investment rose from 2017’s now paltry $14 million to a more reasonable $25 million this year.

This, in part, was driven by two monumental deals: the record-shattering $14 billion raise by Ant Financial in Q2 and Worldpay being acquired by Vantivin Q1 for $12.9 billion. Outside of these two major outliers, the worldwide fintech market built some great momentum, as the geographic diversity of fintech investment expanded beyond expectations. The usual faces of the UK, USA, India and China aside, other nations like Japan, South Korea, Switzerland and France all saw outstanding fintech deals. Most notably, Brazil held the honour of introducing its native Nubank into the fintech unicorn club.

A broad array of companies spent big on emerging areas of innovation in finance, with artificial intelligence, machine learning and data analytics receiving particular attention from major fintech investors.

The rise of Artificial Intelligence in Financial Services

The growth and adoption of artificial intelligence in financial services throughout the year cannot be overstressed. AI has been quietly transforming the inner, core workings of banks, leading early major movers to service their AI applications to their competitors. This leads users to accelerate the program’s learning, and eventually flipping previous cost centres into future profitable divisions.

The fintech scene in UK is one of the fastest growing sectors in business, with multiple challenger banks emerging from the shadows of monolithic financial institutions. One example, OakNorth, has created its own AI application, entitled Acorn Machines, which employs data to empower lenders to offer finance to SMEs. Other startups, like ComplyAdvantage, demonstrate the utility of artificial intelligence in financial services, by providing banks with practical solutions for money laundering and trading applications.

Across the globe, artificial intelligence continues to make waves. The Monetary Authority of Singapore (MAS) formed a set of principles to guide the nation’s usage of artificial intelligence in finance. The principles promote fairness, ethics, accountability, and transparency (F.E.A.T) in the use of AI & Data Analytics (AIDA) across the finance sector. The FEAT Principles informs financial firms on the ethics when using AIDA.

Elsewhere, Beijing’s Tsinghua University partnered with the University of Hong Kong (HKU) to accelerate projects employing Artificial Intelligence, including those related to FinTech and environmental protection. The new initiative features collaboration between the Ministry of Science and Technology and Sensetime as they develop new AI applications. This follows China’s continued support for rapid innovation in technology across Hong Kong.

For some North American representation, Montreal impressively finds itself playing host to three new AI firms: QuantumBlack, WinningMinds, and BIOS, with 130 new jobs expected in the city. The trio find themselves in good company, with Montreal receiving almost CA$1.1 billion in AI investmentsince 2016 — according to Montreal International.

All this shows plenty has happened in 2018, and having a firm understanding of the concepts behind these moves is paramount to industry success.This is especially important with the close of 2018, as the turn of the New Year presents a wondrous opportunity to finally jump aboard the Artificial Intelligence movement.

Fortunately, CFTE has formed AI in Finance — a leading programme which allows participants to learn from senior industry leaders who hail from the large financial institutions, fast growing startups and regulatory agencies. Featuring 18 guest speakers and 5 senior lecturers, learners will understand how AI transforms finance. Designed for those novices or professionals who want to be part of this flourishing industry, and with no prerequisites, — join the AI revolution in Finance today!

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Record Investment meets the Rise of AI in Finance: A Handy Summary of 2018 Fintech

Wow, what a whirlwind of a year in Fintech. Just as we predicted, 2018 proved itself to be a massive year of growth for Fintech firms and emerging technologies, like Artificial Intelligence, alike. Once rather placid with few rippling trends, fintech has transformed finance into a dynamic market with innovative firms competing to raise the bar above one another. As consumers continue to have greater expectations for personalised services across other industries like electronics and retail, the realm of financial services has realised the lucrativeness of meeting these needs.

Let’s sift through 2018 in Fintech, together:

More money than ever in Fintech

We’ll begin with the rather large elephant in the room. This last month saw the largest-ever crowdfunding round for a Fintech startup, with Monzo reaching their goal of £20m in just 2 hours and 45 minutes when it opened its round to the public on the morning of December 5th. Over 36,000 investors forked out their wallets to ensure Monzo is now valued 40 times more than their initial value during their first raise in early 2016. What’s more, this follows October’s £85 million Series E round, led by General Catalyst and Accel, which made the digital mobile-only bank the latest UK start-up to achieve bonafide unicorn status.

Speaking of Fintech unicorns, there are now about 29 of them merrily strolling the fields of finance, at an aggregated USD $84.4 billion. Q2 gave birth to five new unicorns, with Ribbit Capital and QED Investors the most active fintech unicorn investor.

Overall investment across venture capital, private equity and mergers & acquisitions are markedly above 2017’s totals. In H1, global investment in fintech firms surpassed $57.9 billion, with $16.8 and $26 billion of this going to the Asian and European continents respectively. Moreover, global median venture deal size for late stage investment rose from 2017’s now paltry $14 million to a more reasonable $25 million this year.

This, in part, was driven by two monumental deals: the record-shattering $14 billion raise by Ant Financial in Q2 and Worldpay being acquired by Vantivin Q1 for $12.9 billion. Outside of these two major outliers, the worldwide fintech market built some great momentum, as the geographic diversity of fintech investment expanded beyond expectations. The usual faces of the UK, USA, India and China aside, other nations like Japan, South Korea, Switzerland and France all saw outstanding fintech deals. Most notably, Brazil held the honour of introducing its native Nubank into the fintech unicorn club.

A broad array of companies spent big on emerging areas of innovation in finance, with artificial intelligence, machine learning and data analytics receiving particular attention from major fintech investors.

The rise of Artificial Intelligence in Financial Services

The growth and adoption of artificial intelligence in financial services throughout the year cannot be overstressed. AI has been quietly transforming the inner, core workings of banks, leading early major movers to service their AI applications to their competitors. This leads users to accelerate the program’s learning, and eventually flipping previous cost centres into future profitable divisions.

The fintech scene in UK is one of the fastest growing sectors in business, with multiple challenger banks emerging from the shadows of monolithic financial institutions. One example, OakNorth, has created its own AI application, entitled Acorn Machines, which employs data to empower lenders to offer finance to SMEs. Other startups, like ComplyAdvantage, demonstrate the utility of artificial intelligence in financial services, by providing banks with practical solutions for money laundering and trading applications.

Across the globe, artificial intelligence continues to make waves. The Monetary Authority of Singapore (MAS) formed a set of principles to guide the nation’s usage of artificial intelligence in finance. The principles promote fairness, ethics, accountability, and transparency (F.E.A.T) in the use of AI & Data Analytics (AIDA) across the finance sector. The FEAT Principles informs financial firms on the ethics when using AIDA.

Elsewhere, Beijing’s Tsinghua University partnered with the University of Hong Kong (HKU) to accelerate projects employing Artificial Intelligence, including those related to FinTech and environmental protection. The new initiative features collaboration between the Ministry of Science and Technology and Sensetime as they develop new AI applications. This follows China’s continued support for rapid innovation in technology across Hong Kong.

For some North American representation, Montreal impressively finds itself playing host to three new AI firms: QuantumBlack, WinningMinds, and BIOS, with 130 new jobs expected in the city. The trio find themselves in good company, with Montreal receiving almost CA$1.1 billion in AI investmentsince 2016 — according to Montreal International.

All this shows plenty has happened in 2018, and having a firm understanding of the concepts behind these moves is paramount to industry success.This is especially important with the close of 2018, as the turn of the New Year presents a wondrous opportunity to finally jump aboard the Artificial Intelligence movement.

Fortunately, CFTE has formed AI in Finance — a leading programme which allows participants to learn from senior industry leaders who hail from the large financial institutions, fast growing startups and regulatory agencies. Featuring 18 guest speakers and 5 senior lecturers, learners will understand how AI transforms finance. Designed for those novices or professionals who want to be part of this flourishing industry, and with no prerequisites, — join the AI revolution in Finance today!

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The post Record Investment meets the Rise of AI in Finance: A Handy Summary of 2018 Fintech appeared first on CFTE.

Let’s look 6 applications/use cases of Artificial Intelligence in Finance:

Wealth and Asset Management

Did you know the worlds biggest investment group, BlackRock with $6.3 trillion assets under management now has a dedicated AI Lab?

A key differentiator in the world of wealth management is the ability to determine which assets and securities will yield the best results. In this sense, Artificial Intelligence has a huge role to play in giving wealth managers a competitive edge. In 2014, UBS pursued an ambitious plan to acquire AI firm Sqreem technologies to give their wealth advisors the ability to provide tailored advice to their wealthy clientele. Others have followed suit, and in an effort to better predict market trends and make better performance calculations, organisations such as Blackstone, S&P Global and Euronext are adopting machine learning in finance to improve forecasting and, ultimately, increase value for their clients.

Insurance

“98% of insurance executives believe artificial intelligence will play a disruptive role in the industry”

When you take a step back to look at the inner workings of insurance industry it becomes clear that AI has a wide range of applications. For one, the industry is driven by data. An insurer’s key role is to understand as much about you as possible before making an assessment – about your lifestyle, your education, your health and so on. Given AI algorithms enable better modelling and the fact that developments in IOT have led to the explosion of available data points (just think about wearables) it means insurers have access to a new formula to understand you and serve you better.

For example, US startup Lemonade employed a bot called ‘Jim’ who took less than 3 seconds to settle an insurance claim by executing multiple back-end processes concurrently. On the front-end, it’s also an example of a big step forward in customer service and product development.

Another US startup called MetroMile is using AI to develop an entirely new business model in which the insurance premium is calculated based on usage by installing an IOT device on cars to pull data on the user.

Customer Service

The Millennial Disruption Index reports that 71% of Millennials would rather go the dentist than listen to what a bank has to tell them!

The fact is that a lot of the new Fintech upstarts that are disrupting the industry have built a solid reputation of excellent customer service by utilising emergent AI technologies such as NLP to provide instant service. If you’ve ever used Revolut’s banking app then their support service utilises smart chat to direct your enquiry to the relevant support staff. So no need to go through multiple hoops to speak to the right person. Taking it a step further, machines can now fully address customer queries where relevant. Take for example Royal Bank of Scotland, who as early as 2016, installed an AI assistant Called Luvo to help users deal with user queries and when it’s not able to the system hands over to an actual human. The rise of these robo-advisors will seemingly re-define customer experiences and give incumbents a tool to bring up their A game.

Robotic Process Automation

Did you know that the global RPA market is expected to reach USD 8.75 billion by 2024?

RPA is software that can automate repetitive tasks normally done by humans. Understanding this, one can look at the middle and front office processes in financial services (think deposit processing, underwriting support, billing and so on) and see the benefits for firms to employ this technology. From increased productivity to better time-management and huge cost savings, the advantages are clear.

A leader in the field of RPA is Romanian Unicorn UiPath, which offers banking automation to a wide range of clients in the banking sector and their rapid global expansion serves as a barometer of that banks are increasingly turning to RPA to bump up the bottom line.

Credit Scoring

AI and machine learning are helping 80% of the Indian population with no credit score gain access to credit. How, you might ask?

New Fintech companies such as Upstarts is using AI to gather alternate data to prove creditworthiness. This alternate data may take multiple forms that ultimately analyse a user’s digital footprint to gather data to help make lending decisions. In the case of Upstart, the company analyses your employment history, educational background and other sources of data such as social media to make credit decisions. Upstart is not alone with a host of companies employing machine learning in finance, especially those targeting emerging markets, such as Cashe in Mumbai, where billions remained unbanked to make credit decisions.

It’s a brave new word and AI-powered algorithms are already disrupting the lending industry to enable millions of undeserved customers to access financing options previously out of reach.

Compliance and Fraud Detection

Did you know that the regulatory compliance technology market is expected to be over worth over $118 billion by 2020?

Following the financial crisis of 2009, firms operating in the financial services are under immense pressure to be compliant with complex regulatory frameworks designed to protect the system against undue risk and fraudulent behaviour. The process of compliance is in itself long and arduous with mountains of paperwork and legal documents often involved. A prime example AI in action to help streamline the processes is JP Morgans implementation of COIN, a machine learning system that completed 360,000 hours of compliance work in seconds!

More than just helping to analyse documents, AI is also helping to redefine KYC and AML processes to help reduce the risk of fraud. Using advanced predictive analytics, banks have the ability to analyse massive data sets that pick up on suspicious activities. In early 2018, HSBC integrated software from UK startup Quantexa, to pick up on fraudulent activities and at a significant cost reduction.

How you can be part of the AI revolution in Finance

There is increasing industry importance on the introduction and implementation of Artificial Intelligence. As a result, having a robust understanding of AI, its use cases and its best implementations is vitally important for industry success.

Fortunately, CFTE has formed AI in Finance — a leading programme which allows participants to learn from senior industry leaders who hail from the large financial institutions, fast growing startups and regulatory agencies. Featuring 18 guest speakers and 5 senior lecturers, learners will understand how AI transforms finance.

 Designed for those novices or professionals who want to be part of this flourishing industry (and with no prerequisites)

 Join the AI revolution in Finance today!

If you’d like to learn more about the course speakers, content and structure, please click the button below where you can learn more about artificial intelligence & machine learning applications in finance, the course itself and download the full prospectus.

Follow us on LinkedIn, Twitter and Facebook to receive the newest updates about fintech from all around the world!

This is an updated version of a blog originally published May 23rd 2018. Updated on October 25th 2019.

The post 6 Applications of Artificial Intelligence & Machine Learning in Financial Services appeared first on CFTE.

This has been months in the making, and we are delighted to announce “Artificial Intelligence in Finance”, the first major online course about the impact of AI and Machine Learning on the finance industry.
This is the first collaboration between CFTE and Ngee Ann Polytechnic, a leading institution of higher learning that has been driving entrepreneurship and Fintech in Singapore, and this joint initiative has led to a truly global course, that benefits from the insights of more than 20 experts from the US, Europe and Asia.
AI is the “single most important technology” impacting finance today, and financial institutions are now heavily investing in the space. AI in Finance launches with the sponsorship of two forward-looking Singaporean banks, OCBC and UOB, that will make the course accessible to a significant number of employees for them to adapt to an AI-world.
For more information about the course:
– Press release
– AI in Finance (International site)
– AI in Finance (Singapore site)
 

The post Major online course about "Artificial Intelligence in Finance" announced appeared first on CFTE.

This year jolted us into a digital sprint, accelerating the adoption of technologies like artificial intelligence and its key arm – machine learning. Developments in the field of AI are bound to significantly transform how we live, work and play as we march into the future.

If you’re wondering how you can enter the world of artificial intelligence and machine learning then you’re not alone, as many professionals are wondering how to enhance their respective industry prospects, including those looking to boost their Fintech careers. In this article, we’ve highlighted the best artificial intelligence and machine learning courses that can help you develop a deeper understanding of the subject and develop your future career.

The fact is, artificial intelligence is transforming every industry from healthcare to entertainment and of course financial services. Innovation in AI is promising a new era of economic growth and in response, the pace of development is accelerating:

• Governments across the globe are investing heavily in AI, the US government has announced a  $1 billion investment to fund AI and quantum computing research as of August 2020, with other countries following suit.

• Companies across the globe are investing heavily in AI too with 64.8% of firms investing more than $50 million in AI in 2020, a percentage significantly greater than 39.7% in 2018.

• The market for AI technologies to reach $190+ by 2025 according to a leading market research firm.

In the midst of such upheaval in policy and macroeconomic trends, The artificial intelligence and machine learning courses outlined below will help you understand the potential of artificial intelligence. The courses explore key areas in machine learning such as artificial neural networks, data analysis, deep learning, python and much more. And the beautiful thing about the courses that we’ve highlighted is that they’re suited for all levels. Whether you just looking for a basic introduction or want to gain an expert level understanding, there’s something for everyone.

CFTE Presents: 8 Best Artificial Intelligence(A.I) and Machine Learning Courses in Finance [2021]

1. CFTE’s online AI in Finance course

Summary: AI in Finance is the first industry led online course on the artifical intelligence revolution, developed by CFTE and Ngee Ann Polytechnic for professionals to understand the applications of Artificial Intelligence and Machine Learning in financial services. The course follows a similar format to CFTE’s Fintech Specialisation Course.

Over the course of 18 modules and 4 chapters, over 20+ finance and technology thought leaders and industry insiders will discuss key artificial intelligence topics such as Natural Language Processing (NLP), data science, recommendation engines, artificial neural networks (ANN) and more. It’s a knowledge-based course so it’s the perfect platform for those beginning their journey to understanding AI in financial services

Advantages:  

• Learners can join an online community to connect with professionals to continue their learning journey and build the connections that can help to further their career
• Self-Paced Online Course – Suitable for busy professionals
• Industry Led Knowledge – Experts from UBS, Citi, ADDO AI, IO Tahoe, PingAn Technology and many more.
• 6 months access to course content from day of enrolment
• Suitable for those on a budget as course can be bought outright or payments can be split across three months
• Boost your CV and Job Prospects with a SSG, IBF, ACT and CPD Accredited Certificate.

Key lecturers:

• Ayesha Khanna (CEO and Co-Founder – ADDO.AI | Co-Founder – Hybrid Reality Institute | Faculty Advisor – Singularity University)
• Stephan Murer (Ex Global CTO – UBS)
• Philip Watson (Head of Investments Lab and Chief Innovation Officer, Citi)
• Dr. Winne Cheng (Chief Data Scientist, IO Tahoe)
• Jon-Tzen Ng (Chief Strategy and Innovation Officer)

Level: Beginner

Cost: £600 OR £200 for 3 months. 

Certification: SG Accredited Certification

Study Method:

• Online Based Course – Learn at your own schedule and pace.
• 18 structured modules to understand the applications of Artificial Intelligence in Finance.
• 18 Quizzes/Assessments at the end of each module to test your knowledge.
• Video lectures – Course consists of video lectures and video interviews with industry experts.
• Comprehensive readings to compliment content within video lectures.

Want to know more?  Click here for more information.

2. Stanford University, Coursera –  Machine Learning Courses

Summary: This 11-week program from Andrew Ng, the co-founder of Coursera, co-founder of Google Brain and former Chief Scientist at Baidu, is one of the most popular online courses for understanding artificial intelligence and machine learning. This AI course covers a variety of core concepts such as supervised and unsupervised learning, and data mining. The material is learned through a mixture of text-based reading and videos lectures that help to break up study patterns.

Advantage: Flexible coursework which means if a learner misses the deadline they can switch to another session.

Key lecturer:

• Andrew Ng (CEO and Founder – Landing AI | Co-founder, Coursera | Adjunct Professor – Stanford University | Former Chief Scientist – Baidu | Former Founding Lead – Google Brain

Level: Introductory

Cost: Free

Certification: US$79 for grading/certification

Study Method: Online

Click here for more information.

3. Udacity Machine Learning Nanodegree

Summary: Two three-month programmes that aim to help you “master the skills necessary to become a successful Machine Learning Engineer”. It’s definitely one of the more career-focused programs that we’ve listed and like the Stanford course, covers the core machine learning principles and also dives deeper into the realm of predictive modelling. It’s beginner-focused but expect a vigorous test.

Advantage: The last two out of the nine projects will contribute heavily to your CV and level of job readiness. One-to-one technical mentor available. Also suitable for those on a budget, as access is charged on a monthly basis, making it potentially cheaper if you can complete the course faster.

Key lecturers:

• Cezanna Camacho (Machine Learning Educator)
• Luis Serrano (Former Machine Learning Engineer – Google)
• Dan Romuald Mbanga (Principal Machine Learning Business Development Manager – Amazon Web Services)
• Mat Leonard (Former physicist, research neuroscientist and data scientist)

Level: Beginner to Intermediate

Cost: US$399 per month (est. 3 months needed to complete each course)

Certification: N/A

Study Method: Online

Click here for more information.

4. International Faculty of Finance, IFF – Artificial Intelligence in Banking Training Course

Summary: A two-day training programme which covers Artificial Intelligence in banking by examining current leading practices across the global industry. It offers an in-depth look into the best available practices and how they can be applied in any organisation.

Advantage: Course offers detailed insight into how AI can best suit your burgeoning business through 10 rigorous levels. Also suitable for those short of time.

Key lecturer:

• Christian Spindler (Senior Manager at PwC)

Level: Beginner to Intermediate

Cost: London – £2638.80 (including VAT), Online – £2158.80 (including VAT)

Certification: CPD (Continuing Professional Development) Certification.

Study Method: In-person or virtual classrooms to conduct practical sessions and discussions.

Click here for more information.

5. Columbia University, edX – Machine Learning for Data Science and Analytics

Summary: Another Ivy League School, Columbia University, brings their 5-week course to Edx, a global online learning platform. This artificial intelligence course gives learners a solid understanding of the principles in machine learning, covering its relation to statistics and data analysis, computer algorithms to detect patterns, how to utilise data patterns in decision making and more.

Advantage: Self-paced course suitable for busy individuals.

Key lecturers:

• Cliff Stein (Professor of IEOR and of Computer Science – Columbia University)
• David Blei (Professor of Computer Science and Statistics – Columbia University)
• Mihalis Yannakakis (Professor of Computer Science – Columbia University)

Level: Intermediate to advanced

Cost: Free or US$99 for certification

Certification: Can be transferred into credits for the Master of Computer Science program at Columbia University

Study Method: Online

Click here for more information.

6. MIT – Artificial Intelligence: Implications for Business Strategy 

Summary: MIT once again partners with e-learning platform GetSmarter to address the growing demand among business professionals to get an understanding of what exactly artificial intelligence is and how it will impact business. This online AI course is practically focused and follows a similar pattern to the MIT Fintech Certificate in which learners were first given an introduction to the subject and then given a capstone project to apply their understanding.

One of CFTE’s alumni who completed this AI course called it “comprehensive with plenty of videos, readings, discussion forums and assignments”. 

Advantage: Strategic in focus versus theoretical. Multiple semester intakes and self-paced course to suit your schedule.

Key lecturer:

• Thomas W. Malone (Professor of Information Technology | Director, MIT Center for Collective Intelligence)
• Andrew Lo (Professor of Finance | Director, Laboratory for Financial Engineering – MIT Sloan School of Management)
• Alex Pentland (Toshiba Professor of Media Arts & Sciences Director | Human Dynamics Lab and MIT Media Lab Entrepreneurship Program)
• Daniela Rus (Director – Computer Science and Artificial Intelligence Laboratory (CSAIL), MIT)
  

Level: Intermediate 

Cost: US$3,200

Certification: MIT Certification of Completion 

Study Method: Online

Click here for more information.

7. Open Badge Programme, IBM Skills Gateway

Summary: Learners on the Open Badge Programme have access to a variety of artificial intelligence and machine learning short courses (or badges) that have been designed to help you understand the basics of AI and machine learning. As it’s IBM, the courses pivot towards familiarising learners with their ecosystem of AI products such as IBM Watson and IBM Cloud.

Note: Courses are aimed at enterprise developers with an academic background in computer science.

Advantage: IBM badges are more focused on the business application of AI on an enterprise level. Courses available for all skill / experience levels.

Key lecturer:

• N/A

Level: 4 levels – Explorer, Instructor, Author and Mastery

Cost: Varies

Certification: IBM Badge

Study Method: Online

Click here for more information.

8. Data Science, Deep Learning, & Machine Learning with Python, Udemy

Summary: Of all the courses mentioned on the list, this is probably one of the best for beginners. It’s a self-paced course that focuses on using a lot of video content to deliver the teaching material – more than 80 lectures spanning 12 hours of video. It covers all the AI and Machine Learning concepts and provides a great platform to dive deeper.

Advantage: Easy to understand, free of complex jargon and built for complete beginners.

Key lecturer:

• Frank Kane (Founder – Sundog Education | Former Senior Manager – IMDb.com, Amazon)

Level: Beginner

Cost: US$11.99

Certification: Course Completion Certification

Study Method: Online

Click here for more information.

About CFTE

CFTE is an education platform supported by senior leaders from the largest institutions, startups and universities. It addresses the needs of professionals in finance and technologists to up-skill in a rapidly changing industry being transformed by emerging technologies.

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