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The latest Blockchain and Digital Assets session brought together global participants to explore one of the most transformative shifts now reshaping global trade: the tokenisation of trade finance assets. Led by Saloi Benbaha, Head of XDC Network Enterprise Alliance & Ventures and Global Women in AI Ambassador, the session unpacked how tokenisation, harmonised standards and cross-industry collaboration are redefining efficiency, transparency and trust across the trade ecosystem.

With a career spanning telecom, banking, blockchain and energy, and fresh from receiving the Policy Pioneer Award at the House of Lords, Saloi delivered a masterclass on what financial institutions must understand to stay competitive in a rapidly digitising world.

Tokenisation as a Catalyst in Trade Finance

Saloi began by outlining why tokenisation is emerging as a foundational pillar in trade finance. Traditional trade processes are burdened by fragmented systems, inconsistent data, paper-heavy workflows and long settlement cycles. Tokenisation offers a fundamentally different model—one where assets, documents and processes can be digitised, standardised and exchanged with programmable trust.

By converting trade assets such as invoices, letters of credit, guarantees or receivables into tokens, institutions can benefit from:

• Faster settlement

• Improved liquidity

• Greater transparency and auditability

• Reduced operational risk

• Enhanced interoperability across platforms and borders

Tokenisation, Saloi emphasised, is not simply a technology upgrade. It represents a structural shift toward digital-native trade finance.

Why Standards and Interoperability Matter

A central theme in Saloi’s presentation was the critical need for standards. Tokenisation cannot scale in isolation. It requires:

• Shared data models

• Common identity frameworks

• Legal and compliance harmonisation

• Industry-wide agreement on asset definitions

Saloi highlighted how emerging frameworks—such as the Digital Standards Initiative (DSI) and those developed by leading industry bodies—are paving the way for unified digital trade ecosystems. Without such standards, tokenisation risks becoming siloed, fragmented and incompatible across markets.

Collaboration as the Engine of Adoption

Tokenisation thrives through collaboration, not competition. Banks, corporates, fintechs, regulators and technology networks must co-design solutions. Saloi’s work at the XDC Network Enterprise Alliance exemplifies this collaborative model, bringing stakeholders together to build shared infrastructure and tackle adoption barriers collectively.

She also emphasised the growing importance of public-private coordination, particularly around regulatory clarity and cross-border legal frameworks.

Becoming Tokenisation-Ready: Skills and Capabilities

Saloi provided a clear blueprint for organisations preparing to embark on tokenisation initiatives. Key skill areas include:

• Understanding digital asset frameworks

• Navigating compliance and regulatory design

• Smart contract literacy

• Process redesign and workflow digitisation

• Cross-functional collaboration between technology, operations and legal teams

Tokenisation is not an IT project, it is an organisational transformation that requires new digital competencies across the value chain.

Integrating Tokenised Solutions with Legacy Infrastructure

One of the session’s most practical discussions centred on how banks can integrate tokenisation into existing trade finance systems. Saloi highlighted that the most effective strategy is to begin with well-defined, high-value use cases, and then:

1. Identify assets that benefit most from digitisation

2. Map the current processes and pain points

3. Build tokenised modules that integrate via APIs

4. Adopt a phased rollout that maintains continuity with legacy operations

The goal is not to replace existing infrastructure immediately but to extend it with digital layers that introduce automation and interoperability.

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5 Key Learning Outcomes from the Session

1. Tokenisation transforms trade finance by digitising assets end-to-end
It streamlines processes, increases liquidity, accelerates settlement and reduces friction that has long slowed global trade.

2. Standards are essential for scalable adoption
Without alignment on data, identity and legal frameworks, tokenisation remains isolated. Industry-wide standards unlock interoperability and global connectivity.

3. Collaboration drives real-world implementation
Tokenisation succeeds when banks, corporates, regulators and networks work together to redesign trade processes, not when each operates alone.

4. Successful tokenisation requires new digital skills across organisations
Teams must understand blockchain concepts, regulatory expectations, smart contracts and process redesign, not just technology.

5. Integration with legacy systems must be phased and strategic
Institutions should start with clearly defined assets and use cases, then layer tokenisation onto existing infrastructure through modular, API-driven approaches.

As trade finance undergoes rapid digital transformation, tokenisation stands out as a powerful enabler of transparency, speed and global interoperability. Saloi’s session showed that the future of trade will not simply be digitised, it will be standardised, collaborative and token-powered. For institutions looking to stay competitive, now is the moment to build the skills, partnerships and technological foundations needed to thrive in this new era. With growing momentum across industries and governments, the shift toward digital trade finance is no longer a possibility, it is already underway.

 

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The 4th Industrial Revolution is here. Disruption is taking place across organisations, and companies continue launching new products based on the Machine Learning and Artificial Intelligence technology. This is the crucial moment to utilise executive education options to foster AI knowledge within the workforce and gain the opportunities presented by the new technologies.

Therefore, this guide presents an introduction to executive learning workshops and the best products on the market.

What are Executive Learning Workshops?

Executive learning workshops are specialized training sessions designed for senior-level professionals, now widely utilised to teach professionals to apply latest technologies to drive positive change and improve process efficiency within organisations. These workshops can be conducted in various formats, including in-person and online, providing flexible and accessible learning environments for busy professionals.

Here is the comprehensive list to the leading industry programs in Artificial Intelligence:

1. Bespoke AI Workshop for Senior Leaders by CFTE

Summary:  CFTE’s AI Workshop provides industry experienced practitioners with a comprehensive understanding of AI and its strategic implications specifically in the financial services industry. The AI Workshop has been designed to maximise senior leader engagement and includes discussions, demonstrations and applied projects facilitating collaboration and networking amongst the cohort. Topics cover from AI foundations, AI strategy for banks to real-world use cases of AI in finance.

Advantages:

• Learn directly from those who build the ecosystem. The workshop is led by leading industry experts in AI and GenAI;
• End-to-end pathway to the practitioners. Learn the hands-on experience with Generative AI solutions and tools;
• Engagement reporting after the workshop. Keep learning after the workshop with the resources such as handbook, glossaries and reports provided by CFTE;
• Learn the real-world uses cases of AI in finance. Learn how AI can transform your industry with real-life scenarios with bespoke case studies.

Key lecturers:

• Huy Nguyen Trieu (CEO – The Disruptive Group | Co-Founder – CFTE | Associate Fellow – Oxford)
• David Haran (Portfolio Chief Financial Officer – The CFO Centre Ireland)
• Meaghan Johnson (Founder – Digital Magss)
• Alvaro Rojo (Product & Venture Leader – HSBC)

And more.

Cost: Depends on the learning package

Location: Online and in-person options available

Duration: Custom duration depending on learning goals

2. AI for Business by INSEAD

Summary:  Designed to demystify Artificial Intelligence in the digital age, this course by INSEAD aims to cover the recent advances in machine learning, such as Large Language Models(LLMs) applications such as ChatGPT, as well as Generative AI applications such as DALL-E2. As a result, the workshop would enable businesses and managers in utilisation of AI technology to improve various business processes within organisations.

Advantages: Provides a strong focus on the knowledge of the capabilities of Artificial Intelligence technology, and how senior leaders can effectively manage projects utilising machine learning algorithms. 

Key lecturers:

• Phanish Puranam (Professor of Strategy – INSEAD)
• Theodoros Evgeniou (Professor of Decision Sciences and Technology Management – INSEAD)
• Ville Satopaa (Associate Professor of Technology and Operations Management – INSEAD)

Cost: €10,600

Location: Fontainebleau, France

Duration: 5 days

3. Digital Strategy, Analytics and AI by IMD

Summary:  Program provides participants with a comprehensive framework that integrates digital strategy with data analytics and AI, enabling them to add value in digital transformation. Using a hands-on experiential approach, the program will equip professionals with practical tools and new confidence to leverage digital strategies, analytics and AI, and create sustainable competitive advantage for the long term.

Advantages: Participants represent governments, pharma, banking, technology and manufacturing, as well as platform-based companies, thus fostering collaboration and engaging approach to learning. 

Key lecturers:

• Mikolaj Jan Piskorski (Program Co-director – IMD),
• Amit Joshi (Program Co-director – IMD) 

Cost: CHF 10,900

Location: Lausanne, Switzerland

Duration: 5 days

4. Data Science and AI for Executives by LSE

Summary:  This fast-paced, intensive programme gives participants a comprehensive, non-practitioner grounding in data science, AI and machine learning. Professionals will also explore its real-world application across business, government and law. The course gives the understanding and vocabulary to assess quantitative evidence, design big data studies and make more intelligent, data-driven decisions.

Advantages: The programme includes actionable application strategies for executives in the fields of business, government and law.

Key lecturers:

• Pr Kenneth Benoit (Professor of Computational Social Science and Head of the LSE Data Science Institute)
• Dr Edgar Whitley (Associate Professor of Information Systems, Department of Management – LSE)
• Professor Sabine Benoit (Professor of Marketing – University of Surrey)

Cost: Contact LSE for pricing

Location: London, United Kingdom

Duration: 1 week

5. Data & AI for Business by HEC

Summary:  The focus is on practical application and strategic planning to leverage data science and AI for business success. Course guides participants through understanding essential concepts, identifying requisite skills and professional profiles for effective data transformation, and aligning data strategies and organisation with company objectives. Participants will also learn to define and develop a portfolio of data projects crucial for crafting their company’s broader Data Project.

Advantages: The workshop features opportunity to collaborate with peers in group projects and includes an individual assignment at the end of the course, thus strengthening learning and ability to effectively apply AI in business functions.

Key lecturers:

• Rodolphe Zoubedi (Program advisor – HEC)

Cost: €4,000

Location: France

Duration: 2 days

What Executive Learning AI Workshop is Best for My Organisation?

The workshops listed in this guide offer a range of focused, practical, and strategic programs, tailored for senior professionals aiming to pivot their organisations towards a future driven by data and technology. Programs by LSE and IMD give a thorough overview and actionable insight into AI technology, building hard knowledge within executive professionals, while HEC opts for a generalist approach to learning and provides an introduction to machine learning systems.

Custom learning options are also available and are offered by some providers. One of those is the Bespoke AI Workshop by CFTE – designed to provide a one-of-a-kind learning experience for executive, this option can be great for organisations building a strategy for AI transformation within their processes.

Whichever executive workshop you choose, the time to act is now – and the effect of AI on the world is undeniable, so start learning to future-proof your organisation at scale.

Pivot Your Organisation Today

There is no time to waste. You can start your upskilling strategy right now.

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Have you fallen into the trap of thinking chatbots and conversational AI are the same thing? Don’t worry, you are not the only one.

It might come as a surprise to many to realise that, although sometimes used interchangeably, the terms “chatbot” and “conversational AI” don’t actually mean exactly the same. In fact, these two terms don’t even fall into the same classification! Chatbots are computer programmes. They can be rule-based or AI-based. So conversational AI is just one of the different technologies that can power chatbots.

What you are really trying to understand is how rule-based or keyword-based chatbots compare to AI-led chatbots.

To spare you from making the same mistake again, we have broken down here what rule-based chatbots and conversational AI chatbots really are, and where their difference lies.

What are rule-based chatbots and conversational AI chatbots?

Chatbots are computer programmes that simulate a human conversation. They are the pop-up chat that appears on the corner of a website, your bank’s customer support chat, the automated voice which asks you to state your issue before directing you to the right human agent when you call a contact centre. Ultimately, chatbots and conversational AI are the new way in which customer service is automated, providing you support around the clock.

However, rule-based chatbots and the ones using conversational AI differ in their abilities and how they are programmed. They both work differently from one another. Here is how.

How do chatbots work?

An easy way to understand how keyword-based chatbots work is thinking of them as flow charts. Chatbots works thanks to a team behind the bot that feeds it with keyword questions and predetermined answers to those. Each answer is automated in advance to lead to the next response.

This way, the chatbot will know, for instance, that when you request to see your bank account it should offer you a login link or a forgot password option. Depending on which you take, the next display of answers will vary. For example, selecting the forgot password option might trigger the chatbot to offer a recovery option via email or phone, and what you choose will lead to another pre-programmed action.

This works well for simple tasks like finding certain information within a bank’s website. However, keyword-led chatbots do not allow for queries that fall out of their programming and so the team must have foreseen and programmed the bot for any possible customer query.

Traditional chatbots are keywords-based and they do not understand a customer intent if it is not formulated exactly as the team has programmed it. Thus, chatbots can result in customers being frustrated as the limited scope of the bot means it is not always capable of resolving the customer’s issue.

How does conversational AI work?

Conversational AI chatbots use Natural Language Processing (NLP) and Automatic Semantic Understanding (ASU) to understand the clients’ needs no matter how they phrase it. Unlike the traditional chatbots, conversational AI does not require each exact query to be preprogrammed and it will understand what the client wants no matter how it is worded. This means that the bot using AI will understand that you want to see your bank statements either if you typed “bank statements” or “I’d like to see my accounts for the past month”. It will even understand you if you mispelled something in your message!

Conversational AI will also learn the more it interacts with clients. This means that, with time, it will understand language better and be more efficient at providing customers the assistance they need. 

What are the differences between keyword-based and conversational AI chatbots?

How traditional and AI-powered chatbots work results in a substantial difference between them. Here are the main differences you should know:

What are the use cases of conversational AI in banking?

The use cases of conversational AI in banking are many. Consider the following:

• Helping customers speed up and reduce friction in their credit cards or loans applications.
• Manage personal finances: from viewing your account balance, to making transactions or paying bills, conversational AI can help you do this easily.
• Alert customers of possible fraudulent activity in their accounts and validate with the client this unusual activity. In the case of confirmed fraud, the bot can automate the cancellation and issue of a new card.
• Offer personalised prices and rewards to customers to nurture customer loyalty.
• Provide basic answers to customer queries, helping them navigate the bank’s website. This can range from “how to apply to a loan” or “where to login”.
• Send reminders to customers for when their bills are due, for example.
• Provide insights into the customer’s expending habits, recurring expenses and more.

What are the benefits of conversational AI in banking?

The ultimate benefit of conversational AI in banking is a better customer experience. This is enabled by 3 interconnected factors.

• A better interface for queries

Conversational AI allows for an enhanced customer experience thanks to its ability to allow customers to engage via text, now one of the preferred communication methods. It also allows for customer support to be available 24/7, and it is simple and efficient to use.

• Shorter waiting times

By automating a high-volume of customer interactions, conversational AI frees up capacity for human customer service. This all results in shorter waiting times for both the customers talking to the bot and those in line with a human agent. Shorter waiting times means happier customers.

• Efficient human support

As a result of all of this, human agents can resolve more queries in less time and have a constant stream of information that can aid them in their duties. Overall, support to the client is faster and tailored to their needs and preferences.

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Go a step beyond: learn more about Conversational AI in banking with CFTE’s short online course

A technology that is already enabling for a more efficient and improved customer experience in banking should not be overlooked now that banks are challenged by incumbents offering a much friendlier banking experience.

Discover more about conversational AI programmes and how you can implement them in your business with our short online course on Conversational AI in Banking.

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AI in Finance Roundtable

We were honoured to organise an exclusive roundtable during the Forum to discuss the pressuring topic on how AI transforms business models in Finance. We invited some of the most prominent leaders on Fintech transformation, including Ronit Ghose, Global Head of Research at HSBC and Christophe Chazot, ex global head of Innovation, to gather their views on the topic. Chaired by CFTE’s Co-Founder, Huy Nguyen Trieu, the round table discussion generated some great points which we will summarise below.

What is AI?  

It is hard to define, as it possesses a broad definition which differs from one academic paper to another. To illustrate, Huy highlights an AlphaZero study that showcased an AI algorithm beating the reigning world champion in chess. From this, it is demonstrated that Neural Networks are very relevant to what Artificial Intelligence actually is. 

A Neural Network is system software that works similar to the tasks performed by neurons of a human brain. Besides AI, the other technologies associated with it are Deep Learning and Machine Learning.

Recently, we have witnessed the creation of various new business models. For example, AirBnB leverages the sharing economy while Facebook and YouTube are platforms. 

3 key factors that resulted in new Business Models:  

• Artificial Intelligence 
• New analytics (think of how Facebook leverages user data)
• Automation tools 

Interestingly, there have also been some advancements in the utilisation of AI and Data analytics (AIDA) combined, resulting in a more complete framework to apprehend new Business Models. 

Paradox raised:  

During the summit, various CEOS and CFOs advocated strongly on the ways in which their banks/companies leveraged robust Digital Transformation & AI strategies. However, upon closer inspection of their activities, it is difficult to shed clarity on how exactly these organisations utilise AI, if they really do at all.

Historical perspective:  

The concepts currently discussed are not necessarily new. The term ‘Artificial Intelligence’ was coined in 1956. Similarly, in the late 1950s, the idea that information might be more valuable than money had already emerged.

Thoughts from the discussion:  

There are some nuances between how Supervised Learning vs Unsupervised Learning can disrupt Business Models. Different points of views were raised regarding the robustness of reinforcement learning.

Supervised Learning and Unsupervised Learning are both Machine Learning techniques. In Supervised Learning, the machine is trained using data that is ‘labelled’, whereas Unsupervised Learning is when the machine is left unsupervised. Supervised Learning is a simpler method of training, Unsupervised Learning is undoubtedly a more complex process.

On the other hand, reinforcement learning—in the context of AI—is a marriage between psychology and dynamic programming that trains algorithms using a system of reward and punishment.

Applications of AI in Finance:  

• Optical Character Recognition used for cheque processing in the 80s
• Supervised Learning algorithms trained to recognise fraud 
• Facial Recognition for onboarding processes
• AI used to improve risk measurement
• Credit ratings faster and more accurate with AI 

Thoughts from the discussion: what is preventing a quick implementation of AI tools in Banks?   

• The bulk of big companies/banks are very conservative and not agile enough
• The regulatory sphere is complex and heavy for Banks.  
• When people talk about AI, they often picture it as a utopia. This prevents them from leveraging it realistically.

Where is AI the most valuable today and what are the areas with the biggest potential for AI? 

• In cases of market failure, AI can be highly valuable 
• AI can mitigate against anti/adverse selection in insurance
• AI can empower behavior-based lending 
• AI and Machine Learning could be the next frontier for ETFs to outperform the market

It was certainly a fruitful discussion, of which yielded various interesting points about AI in finance. We were delighted to be joined by key opinion leaders in the industry and being able to host an event of this magnitude was a milestone for CFTE.

The biggest takeaway from the roundtable is that the benefits of which AI implementation could bring forth to banks and financial institutions are wide-ranging. However, in order to truly progress, the current biggest pain-point of path dependency—also known as resistance to change—has to be mitigated against.   

Follow us on LinkedIn, Twitter and Facebook for more updates from the world of Fintech!

Latest posts:

• AI Governance Is Not the Brake. It Is the Way Forward.
• UK Finance and CFTE Launch New Offering to Build AI Capability Across UK Financial Services
• Building the Human Infrastructure for the UK’s Agentic AI Economy
• Mythos Has Arrived: Why Financial Services Must Prepare for Exponential AI
• AI Governance in Finance: Why Responsible AI Is Now a Strategic Priority

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1. What does digital transformation mean? 

Source: The Organic Agency

The thing about digital transformation is that it can come across as a rather ambiguous concept. This is especially true when you consider that what constitutes a digital transformation can differ for each company. For example, digital transformation for a financial institution vs. a telecommunications company would require very different strategies. Similarly, a digital transformation for a large multinational company would be very different from the one implemented by a small to medium enterprise.

In the most technical sense, ‘digital transformation’ refers to the integration of digital architecture into all areas of a business resulting in fundamental changes to how the business operates and how it delivers value to customers. Beyond that, however, a digital transformation suggests that there will be a cultural change to how things are done within the company. Employees are expected to adapt to new technology and be innovative in order to do things faster and in a more agile manner. This usually requires walking away from a certain set of procedures that the employees are used to, which may result in technophobia and change-management resistance. Undoubtedly, it is one of the obstacles that the management needs to consider before engaging in digital transformation.

2. Digital transformation – an example

Source: DBS Bank

Context

DBS is a Singaporean multinational banking and financial services corporation. In 2019, it became the first-ever bank to be awarded with the title ‘Best Bank in the World’ by three major publications. A decade ago, however, DBS was actually ranked at the bottom of the list for customer satisfaction. In fact, it was colloquially known as “Damn Bloody Slow”, with lines of customers queueing in front of its cash machines.

Implementation

At the start of their pivot, DBS allocated a significant amount of their resources into strengthening their digital foundations. For example, the bank went from outsourcing 85% to insourcing 85% of their technology capacities, which subsequently allowed them to launch the world’s largest banking API platform. By making available a wide-array of APIs for other banks, corporates, FinTechs and software developers to plug into, DBS effectively boosted its ability to create innovative and customer-centric experiences. 

Another successful product of DBS’ digital transformation, is its launch DigiBank, a mobile-only bank in India. From a business architecture standpoint, the way in which DigiBank operates on Cloud is an impressive feat. In fact, some of DBS’ internal operations also run on Cloud. DBS is known to utilise a combination of Amazon Web Services’ (AWS), Microsoft’s Office 365 productivity suite and Akamai’s cloud-based web application accelerator. Leveraging Cloud-based technologies has allowed them to be more cost-effective whilst simultaneously increasing their scalability.

Results

A significant reason it is considered effective in its digital transformation is due to how DBS’s implementation of modern technology yielded highly quantifiable results:

1. Much faster time to market, with an accelerated launch of new products and businesses
2. Increasing profits, year on year
3. A market capitalisation of €45 billion

3. What are the four pillars of digital transformation?

Strategy

With any project – digital transformation or otherwise – strategy represents the backbone of its implementation. This will include aspects that touch on budget, stakeholders involved, target audience and such. Discussions should also be held on the objectives, desired outcomes and long term growth options of said project.

People 

According to a McKinsey report on ‘The people power of transformations’, 70% of digital transformation projects fail due to a lack of employee engagement. Furthermore, participation by senior management is imperative. McKinsey states that a transformation is 6.3 times more likely to succeed when senior leaders communicate aligned messages about the change process within the organisation with the employees.

Customers

In the era of digital transformation, having the user experience at heart is imperative. Companies that are able to digitally transform successfully, are able to—in turn—create highly engaged customers. In fact, some companies have hired UX consultants to ensure a smoother digital transformation that is best aligned with final objectives.

Methodology

When it comes to digital transformation, strategy and methodology have to go hand-in-hand for effective implementation. Some methodologies that are commonly used by organisations include: design thinking, Minimum Viable Product and agile working. 

4. What are the key drivers of digital transformation?

Source: Altimeter

Customer expectations

Modern customers now have higher expectations than ever before. They expect a constantly improved service, instant responses, seamless experiences, and personalised products. A Salesforce report states that 70% of customers are likely to prioritise connected processes while 84% of consumers say that being treated like a person, and not a number is important to winning their business.

Digital innovation

Technological advancements have made it easier for businesses to scale and expand. For example, Zhong An—a 100% digital-only insurer—was only founded 6 years ago, and already it has sold over 10 billion policies to 500 million customers. In order for traditional insurance incumbents to stay competitive, innovation is necessary.

5. Why should banks go digital?

Source: The Financial Brand

The expansive benefits for banks that opt to go digital include:

1. More efficiently streamlined processes
2. Improved customer experience and engagement
3. Reduced costs in the banking workforce, higher revenue
4. Improved workflow
5. Increased competitiveness

A survey of marketing personnel reports that 80% of senior leaders who have implemented digital transformation technologies such as cloud say they are able to deliver new value for smart, connected products throughout their lifecycle. Furthermore, 60% said they have created new business models as a result.

6. The shortcut for digital transformation

Earlier in the article, we’ve briefly touched on the different processes organisations are able to utilise during the digital transformation. Here, we will be focusing on the Minimum Viable Product (MVP) methodology. While the MVP method is most commonly used during product design, McKinsey states that it doesn’t have to be limited to this only. It is occasionally even termed as ‘The shortcut for digital transformation’.

The Minimum Viable Product framework:

1. Figure out what problem you’re solving, and for whom
2. Analyse your competitors
3. Define the user flow
4. List all your necessary features and prioritise them
5. Build, test, learn

Pacha Bank’s collaboration with IBM iX is a good example of how a bank has leveraged the MVP method. In 6 months, IBM’s in-house digital lab at Pacha was able to launch its first MVP for a Digital Corporate Banking product, providing business clients with vital daily banking capabilities delivered through a new user interface and user experience.

As a whole, increased digitalisation has brought about various disruptions to the financial industry, with changes in consumer behaviour being the most prominent. Underlying this need for digital transformation is the collective shift of businesses prioritising the customer experience, whilst viewing it as a competitive advantage. For example, Zhong An, a digital-only insurer leverages a Natural Language Processing (NLP) powered chatbot for 97% of their customer interactions, allowing for a faster and 24/7 response rate. Not only is it more cost-efficient, as compared to setting up physical call centres, but their consumers are also more satisfied with how quickly they are attended to. 

Research has shown that a staggering 76% of consumers report that it is easier than ever to take their business elsewhere if they do not feel that a specific brand is meeting their expectations. With the development of new technologies increasing competition and reducing barriers to entry, companies are now having to struggle increasingly to maintain brand loyalty. Organisations that were previously product-focused are now having to pivot towards being customer-focused instead. 

In a nutshell, this is precisely what makes the digital transformation so important. In a customer-focused marketplace, it forces banks to rethink how they engage with customers at every touchpoint. 

Looking to future-proof your organisation? CFTE’s bespoke programmes are crafted with leading experts from the largest global universities, organisations and start ups – curated for the industry, by the industry. We specialise in digital transformation, and our signature programmes include AI in Finance and Fintech Foundation. They cover topics of Artificial Intelligence (AI), Machine Learning (ML), data analytics, Robotic Process Automation (RPA) and more. CFTE is also currently working on developing new programmes on Open Banking and Regtech, get in touch to learn more.

Follow us on our LinkedIn, Twitter, and Facebook for more updates.

Latest posts:

• AI Governance Is Not the Brake. It Is the Way Forward.
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• AI Governance in Finance: Why Responsible AI Is Now a Strategic Priority

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Abu Dhabi, 22 October 2019 – CFTE is very excited to partner with Abu Dhabi Global Market (ADGM) Academy to launch the Programme of Artificial Intelligence in Finance, as part of the ADGMA’s School of FinTech, the first of this kind in MENA region. The announcement was made during the FinTech Abu Dhabi Festival 2019. The purpose of the new programme is to increase Artificial Intelligence (AI) knowledge and understanding of its impact on the financial services and its industry both locally and globally.   The partnership was signed between Mr Hamad Al Mazrouei, Managing Director of ADGM Academy and Mr Huy Nguyen Trieu, Co-Founder of CFTE, witnessed by H.E Ahmed Ali Al Sayegh, Minister of State (UAE) and Chairman of ADGM and Mr Khalid Al Suwaidi, Chairman of ADGM Academy.  

The AI in Finance Programme aims to prepare institutions and professionals to the changing landscape of finance that is transformed by Artificial Intelligence, Machine Learning and Automation. From the understanding of AI technologies to their applications in finance and from ethical considerations to implementation in the enterprise, the comprehensive AI in Finance programme will also serve to drive innovation across the industry and reinforce its leading financial services position of Abu Dhabi regionally and globally in the Middle East.

According to PWC, the finance sector is most likely to be revolutionised by AI in the Middle East and Africa, where it is estimated that 25% ($28.3 million) of all AI investment predicted for 2021, will be spent on developing AI solutions. The new partnership and programme merge well with the ADGM Academy’s ongoing efforts and commitment to provide a sustainable pool of highly-skilled human capital and expertise to support Abu Dhabi’s financial industry and economy.  The UAE Government’s ‘Strategy for Artificial Intelligence (AI)’ focuses on future services, sectors and infrastructure projects.

ADGM Academy and CFTE plan to utilise on this immense opportunity and are looking forward to collaborating on the Programme of AI in Finance project. ADGM Academy is part of Abu Dhabi Global Market, one of the leading global financial centres, and is working with experts to deliver high-quality financial education. Located in Abu Dhabi, the capital of the United Arab Emirates, the Academy has been established to build expertise, financial education and literacy to power the region.

Mr Hamad Al Mazrouei, Managing Director of ADGM Academy, said: “We are pleased to further our collaboration with CFTE to raise the level of financial knowledge and expertise of our corporate and financial professionals in Abu Dhabi and across the wider MENA region. ADGM Academy has been a “port of call” for local and global professionals seeking financial and FinTech education, training and workshops to enhance their competitive edge in today’s fast-evolving business models and environment.  The AI in Finance programme is an important addition to our School of FinTech and an extension of our efforts in supporting the UAE’s AI strategy and focus.”

London-based CFTE will help to deliver knowledge from world-class experts to support ADGM Academy’s vision and contribute to the development of the new-age workforce. CFTE sees this launch as a natural expansion to the Middle East market and wants to act as a link to the MENA region and the rest of the world.

Mr Huy Nguyen Trieu, Co-Founder CFTE said, “We are honored to be partnering with ADGM Academy in strengthening the financial expertise and know-how of professionals in the UAE and the wider region. Artificial Intelligence is transforming financial services everywhere in the world, and Human Capital is what makes it possible. The AI in Finance programme will be an important catalyst to help institutions and professionals be part of this transformation.”

Professionals can now email academy@adgm.com to enrol to the programme.

About ADGM Academy

Delivering world-class financial education and literacy, ADGM Academy will help to position Abu Dhabi as a leading global financial centre. This will be achieved through worldwide recognised educational and experiential programmes on a range of topics and qualifications in banking, finance, leadership, entrepreneurship, technical and soft skills.

About CFTE

CFTE is an education platform supported by senior leaders from the largest institutions, startups and universities. To date, over 40,000 finance professionals have been upskilled through CFTE courses worldwide. The signature Fintech Foundation and AI in Finance online courses have been developed by experts from leading Universities, financial institutions, startups and regulators, such as MIT, Imperial College, Citi or Starling Bank.

The post CFTE partners with ADGM Academy to Launch the Programme of AI in Finance in MENA region appeared first on CFTE.

Question: If you’re a CEO of a large bank that is overly exposed to Hong Kong, what would you do?

Answer: Based on the data I have, Hong Kong is one of the world’s most profitable banking markets in the world, full-stop! On average, the banking system in Hong Kong generates profits of US$200,000 per bank employee. In Singapore, it’s about US$120,000. No bank in continental Europe comes close to this, and this is with interest rates being relatively low.

In Hong Kong, there have been eight new digital banking licenses given, with two going to incumbents. So, if you consider four of the big banks, two have gone for a standalone banking license as a defensive mechanism. For the big one that didn’t, they have a very strong market position and they will have the budget to spend on digital and technology if needed. However, peak profitability is probably behind them. But in a way it’s a better problem to have compared to some continental European banks that have 1-2% ROE!

Q: What do you think is the current state of talent, how is it changing and what needs to be done to keep pace with change?

A: I think the bigger issue isn’t about talent – it’s about incentives, governance and strategic direction. Some of the smartest talents we know are went into these banks; if they’re not smart right now, something happened to them in there!

It’s very easy to say talent is only here in my cool Fintech startup and not in banks. There is a lot of talent everywhere. However, there is the question of training and relevance. The bigger issue is how you manage these organisations. Getting new skills and taking courses is great, but for me it’s more important to consider how you think about managing these companies and governance.

Q: How do we deal with the challenge of changing approaches to banking when it comes to the workforce?

A: This reminds me of a lunch we did in Singapore. There was a lady asking Huy how to essentially future-proof her kids! In context to your question, there are many people moving from big companies to Fintechs, startups and challenger banks etc. If I had been asked this, I would’ve said it’s not about their technical, accounting or legal skills. It’s a different thing that suddenly they realise they’re not used to – standing on their own two feet. It’s about going from being an agent to a principal. 

All of us who work in big companies know about this – agents are just passing the buck! That is the biggest difference – the culture and psychology. I hope most of the people in these big companies have the skill set to be able to retrain themselves if they wanted to. It’s all about autonomy and responsibility, and not just thinking like an automaton.

Click here to read part 1.

To watch the full presentation on BrightTalk, click here.

Click here to subscribe to the CFTE BrightTalk channel, where you can catch up on all our presentations in full.

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About the AI in Finance programme

The CFTE AI in Finance course has been developed in partnership with Ngee Ann Polytechnic, a leading institute of higher learning in Singapore and features high-quality content taught by five senior lecturers and 18 industry experts.

With an easy to follow format, the course is perfect for busy professionals to understand the technologies behind AI and machine learning that are disrupting finance.

Click here to learn more about the CFTE AI in Finance course.

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About CFTE

We are a platform supported by senior leaders from the largest institutions, startups and universities. We address the needs of professionals in finance to upskill in a rapidly changing industry being transformed by emerging technologies. More than 50,000 participants learn from our online courses, such as AI in Finance, Fintech Foundation or Extrapreneurship, a mini-MBA with fast growing startups such as Revolut or Shift Technology.

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Latest Posts

• AI Governance Is Not the Brake. It Is the Way Forward.
• UK Finance and CFTE Launch New Offering to Build AI Capability Across UK Financial Services
• Building the Human Infrastructure for the UK’s Agentic AI Economy
• Mythos Has Arrived: Why Financial Services Must Prepare for Exponential AI
• AI Governance in Finance: Why Responsible AI Is Now a Strategic Priority

Next Events

The post Q&A with Ronit Ghose – Part 2 appeared first on CFTE.

Question: For a long time, we’ve had low interest rates, so banks aren’t making the money they’d like to. Is this holding them back from changing their technology platform and so they are just tinkering on the side?

Answer: That’s a very good question. We had a checklist in the report where we tried to identify what allowed banks in different countries or system to embrace digital or technological change. One of the key factors was budget. For the banks in regions like Australia, Singapore and the US, the tech budget has been going up with the typical number being about 15-25% of total costs.

From 2010, that number has been growing steadily. In Europe, that ratio has been going down. There’s a very short-term firefighting focus among many European banks currently. The tech can be bought if needed, but it’s prioritisation and incentive that’s the biggest challenge.

Q: Digital banks are innovating with technology, but their fundamental philosophy is the same. Do you see cryptocurrency and direct ownership of money disrupting banking before digital banks?

A: The challenge that banks have is trying to work out what the use case is right now for blockchain and cryptocurrency. Obviously, there are a lot of problems that could be solved. But for me, perhaps 99% of the population care that a bank is like an institution – if anything goes wrong, I will go to the regulator and complain. So, the crypto critique of the role of institutions in banking is a relatively marginal one in my humble opinion.

However, there are lots of parts of banking are very slow and archaic and there could be solutions. I’ve just not seen one that’s industrialised at scale. My favourite non-answer is that it’s like the Internet, but in 1992! I think the problem is that there was so much hype two to three years ago, and it’s almost created this sort of bubble that blockchain is great, but we haven’t gotten the proof and that scale of applications yet.

Click here to read part 2.

To watch the full presentation on BrightTalk, click here.

Click here to subscribe to the CFTE BrightTalk channel, where you can catch up on all our presentations in full.

——————————————————————————————————————–

About the AI in Finance programme

The CFTE AI in Finance course has been developed in partnership with Ngee Ann Polytechnic, a leading institute of higher learning in Singapore and features high-quality content taught by five senior lecturers and 18 industry experts.

With an easy to follow format, the course is perfect for busy professionals to understand the technologies behind AI and machine learning that are disrupting finance.

Click here to learn more about the CFTE AI in Finance course.

——————————————————————————————————————–

About CFTE

We are a platform supported by senior leaders from the largest institutions, startups and universities. We address the needs of professionals in finance to upskill in a rapidly changing industry being transformed by emerging technologies. More than 50,000 participants learn from our online courses, such as AI in Finance, Fintech Foundation or Extrapreneurship, a mini-MBA with fast growing startups such as Revolut or Shift Technology.

——————————————————————————————————————–

Latest Posts

• AI Governance Is Not the Brake. It Is the Way Forward.
• UK Finance and CFTE Launch New Offering to Build AI Capability Across UK Financial Services
• Building the Human Infrastructure for the UK’s Agentic AI Economy
• Mythos Has Arrived: Why Financial Services Must Prepare for Exponential AI
• AI Governance in Finance: Why Responsible AI Is Now a Strategic Priority

Next Events

The post Q&A with Ronit Ghose – Part 1 appeared first on CFTE.