7 AI Myths in Financial Services

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Large organisations in the banking and financial services industry have come a long way over the past two decades in cutting costs, restructuring IT systems and redefining customer relationship management. And, as if that was not enough, they now face the challenge of having to adapt to ongoing global technological shifts or the challenge of having to “do something with AI” without being AI-ready in terms of strategy, skills and culture.  

Most organisations in the industry have started approaching AI implementation in a conventional way, based on how they have historically managed IT initiatives. Their first attempts at experimenting with AI have led to rapid conclusions forming seven common myths. However, as experience with AI grows, these myths are gradually being debunked. Let us put these myths to a reality check. 

1. We can rely solely on external tech companies

Even in a highly regulated industry like banking and financial services, internal processes and data management practices can vary significantly from one institution to another. Experience shows that while external providers – many of whom lack direct industry experience – can offer solutions tailored to the more obvious use cases and provide customisation, they fall short when it comes to identifying less apparent opportunities and driving fundamental changes in workflows. No one understands an institution’s data better than its own employees. Therefore, a key success factor in AI implementation is active internal ownership, involving employees directly rather than delegating the task entirely to external parties. While technology providers are essential partners, organisations must also cultivate their own internal understanding of AI to ensure successful implementation.

2. AI is here to be applied to single use cases  

In the early stages of experimenting with AI, many financial institutions treated it as a side project, focusing on developing minimum viable products and solving isolated problems to explore what worked and what didn’t. Given their inherently risk-averse nature, organisations often approached AI cautiously, addressing one use case at a time to avoid disrupting their broader IT landscape or core business. However, with AI’s potential for deep transformation, the financial services industry has an opportunity not only to address inefficiencies caused by manual, time-consuming tasks but also to question how data is created, captured, and used from the outset. This requires an ecosystem of visionary minds in the industry who join forces and see beyond deal generation. 

3. We can staff AI projects with our highly motivated junior employees and let our senior staff focus on what they do best – managing the business 

Financial institutions that still view AI as a side hustle, secondary to their day-to-day operations, often assign junior employees to handle AI implementation. However, this can be a mistake. AI projects involve numerous small yet critical decisions, and team members need the authority and experience to make informed judgments that align with the organisation’s goals. Also, resistance to change often comes from those who were not involved in shaping or developing the initiative. Experience shows that project teams with a balanced mix of seniority and diversity in perspectives tend to deliver the best results, ensuring both strategic insight and operational engagement. 

4. AI projects do not pay off 

Compared to conventional IT projects, the business cases for AI implementation – especially when limited to solving a few specific use cases – often do not pay off over a period of two to three years. Traditional IT projects can usually be executed with minimal involvement of subject matter experts, and their costs are easier to estimate based on reference projects. In contrast, AI projects are highly experimental, requiring multiple iterations, significant involvement from experts, and often lacking comparable reference projects. When AI solutions address only small parts of a process, the benefits may not be immediately apparent. However, if AI is viewed as part of a long-term transformational journey, gradually integrating into all areas of the organisation and unlocking new business opportunities over the next five to ten years, the true value of AI becomes clear. A conventional business case model cannot fully capture this long-term payoff. 

5. We are on track with AI if we have several initiatives ongoing 

Many financial institutions have begun their AI journey by launching multiple, often unrelated, use case-based projects. The large number of initiatives can give top management a false sense of progress, as if they are fully engaged in AI. However, investors and project teams often ask key questions: Where are these initiatives leading? How do they contribute? What is the AI vision and strategy, and how does it align with the business strategy? If these answers remain unclear, it’s difficult to claim that the organisation is truly on track with AI. To ensure that AI initiatives are truly impactful and aligned with business objectives, organisations must have a clear AI vision and strategy – and not rely on number of initiatives to measure progress.

6. AI implementation projects always exceed their deadlines 

AI solutions in the banking and financial services industry are rarely off-the-shelf products. In cases of customisation or in-house development, particularly when multiple model-building iterations and user tests are required, project delays of three to nine months can occur. This is largely because organisations want to avoid rolling out solutions that do not perform reliably. The goal is to ensure that users have a positive experience with AI and embrace the change. Over time, as an organisation becomes more familiar with AI implementation, the process will become faster. 

7. We upskill our people by giving them access to AI training  

Learning by doing has always been and will remain the most effective way to learn, especially with technology. Research has shown that 90% of knowledge acquired in training is forgotten after a week if it is not applied. For organisations, the best way to digitally upskill employees is to involve them in AI implementation projects, even if it’s just a few hours per week. To evaluate their AI readiness or engagement, organisations could develop new KPIs, such as the average number of hours an employee actively engages in AI implementation or the percentage of employees serving as subject matter experts in AI projects. 

Which of these myths have you believed, and where do you already see changes?  

Singapore Fintech Festival 2024
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AI Startups: Powering India’s Digital Future

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The rapid adoption of technology in India is driving a surge in demand for AI solutions across sectors like finance, education, healthcare, and agriculture. AI is revolutionising these industries by making services more efficient, personalised, and accessible. This growing dependence on AI has created a fertile ground for innovation, propelling India’s emergence as a global hub for AI startups. With over 6,200 AI startups operating in the country, India offers a dynamic and challenging landscape for entrepreneurs seeking to make a meaningful impact.

Fuelling AI Innovation: India’s Strategic Investment

Earlier this year, the government allocated USD 1.3 billion for the India AI Mission, solidifying its commitment to AI. This comprehensive program is designed to catalyse the AI innovation ecosystem within the country. At the heart of this ecosystem’s development lies the expansion of compute infrastructure, a critical resource for AI startups. By providing access to powerful computing resources, the India AI Mission is empowering startups to scale their solutions and compete on a global level.

Beyond infrastructure, the initiative focuses on fostering collaborations between academia, industry, and startups to drive R&D. By creating a supportive environment that promotes knowledge sharing and resource accessibility, the India AI Mission aims to position India as a leader in the AI landscape.

A Spotlight on Indian Startups

Driving Industry Innovation

Healthcare. India’s vibrant AI startup ecosystem is driving innovation in healthcare, with companies leveraging AI to address critical challenges and improve patient outcomes.

  • Cancer-Focused AI Startups. Several startups are revolutionising cancer care with AI-driven innovations. Niramai, globally recognised for its innovation, uses AI and thermal imaging for early breast cancer detection, particularly effective in younger women and dense breast tissue. Onward Assist provides predictive analytics for oncology, helping oncologists manage patient data and improve the accuracy of cancer care decisions. Similarly, Atom360 focuses on oral cancer screening with an AI-powered app that offers quick, affordable access to critical information, enhancing oral healthcare in underserved areas.
  • AI-Driven Diagnostic Solutions. AI is significantly advancing diagnostics, enhancing accuracy, and reducing misdiagnosis. SigTuple develops AI-driven diagnostic solutions for medical imaging and pathology, improving accuracy and efficiency in disease detection. Endimension Technology, incubated at IIT Bombay, develops algorithms for detecting abnormalities in medical scans, aiming to reduce misdiagnosis and radiologist workload. Tricog Health delivers AI solutions for rapid heart attack diagnosis, reducing diagnosis time and improving outcomes, especially in underserved regions.

Financial Services. Fintechs have been at the forefront of AI-led innovations, offering innovative solutions for insurance, lending, and microfinance. Artivatic uses AI to transform traditional insurance systems into digital, personalised offerings, making coverage more accessible and affordable for a broader range of consumers. ZestMoney leverages AI for digital lending, providing credit to individuals without a credit history through easy EMI plans, and enhancing financial access. Meanwhile, mPokket offers instant micro-loans to students and young professionals, addressing short-term financial needs with flexible loan options and minimal documentation.

Other Industries. Beyond healthcare and financial services, AI startups are driving innovation across various industries, tackling critical challenges. Entropik uses AI to analyse human emotions and behaviour, helping businesses gain deeper insights into consumer preferences for market research and optimising user experiences. In agriculture, Intello Labs applies AI and computer vision to assess the quality of fresh produce, reducing food waste and improving supply chain efficiency. Similarly, AgNext enhances food value chains by offering AI-driven, real-time quality assessments through its SaaS platform, promoting safety and transparency in agribusiness.

Transforming Businesses

Technology for Security & Fraud. AI startups are offering innovative solutions tailored to organisations’ needs. SpoofSense combats deepfakes and identity fraud with advanced facial liveness detection, ensuring secure user verification by distinguishing between real users and spoofed images. Eagle Eye Networks provides cloud-based video surveillance solutions, using AI to offer real-time monitoring and analytics. In the e-commerce space, ThirdWatch uses AI to detect and prevent fraud in real-time by analysing user behaviour and transaction patterns, reducing financial losses for online retailers.

Tech Development. AI startups are empowering organisations to accelerate innovation and enhance productivity. Haptik helps businesses build intelligent virtual assistants, powering chatbots and voice bots across industries to improve customer engagement. DhiWise automates the development process, enabling faster app creation by converting designs into code. Additionally, Fluid AI provides advanced AI solutions like predictive analytics and natural language processing for sectors like finance, retail, and healthcare. Mihup enhances contact centre performance with its conversation intelligence platform, while Yellow.ai enables enterprises to automate customer engagement through its GenAI-powered platform, creating seamless and scalable customer service experiences.

Empowering People

AI startups are empowering individuals by providing personalised services that enhance learning, creativity, and financial management. SuperKalam and ZuAI offer students tailored learning experiences, using AI to create interactive lessons and assessments that adapt to individual learning styles, improving student engagement and outcomes. For creative professionals, Mugafi combines AI with human mentoring to assist writers in generating ideas and developing scripts, enabling them to create intellectual property with greater efficiency. Wright Research empowers individuals to make informed financial decisions through AI-powered investment advice, while Vahan simplifies job searches for blue-collar workers by using AI to match candidates with suitable employment opportunities via WhatsApp.

Promoting ESG

AI startups are driving meaningful change by optimising processes and creating economic opportunities. Ossus Biorenewables enhances biofuel production through AI, reducing waste and increasing efficiency in renewable energy generation, while Ishitva Robotic Systems promotes sustainability by automating waste sorting and recycling, contributing to a more efficient and circular economy. Karya connects rural workers with digital tasks, offering fair wages and skills development by matching them to tasks suited to their abilities using machine learning. In agriculture, KissanAI helps farmers improve crop yields and manage resources effectively through personalised, data-driven recommendations. ElasticRun improves last-mile delivery logistics in rural areas, enabling businesses to reach underserved markets.

Conclusion

Nvidia CEO Jensen Huang noted India’s potential to become the “largest exporter of AI,” signalling vast global opportunities. India’s AI startups are at the forefront of innovation but face hurdles such as fierce competition for skilled talent, navigating complex regulations, and securing funding. With strategic focus on these challenges and the backing of initiatives like Digital India and Startup India, India’s AI ecosystem can seize emerging market opportunities, accelerate tech advancements, and make a substantial impact on the global AI landscape.

The Future of Industries
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Building a Successful Fintech Business​

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Fintechs have carved out a niche both in their customer-centric approach and in crafting solutions for underserved communities without access to traditional financial services. Irrespective of their objectives, there is an immense reliance on innovation for lower-cost, personalised, and more convenient services.​

However, a staggering 75% of venture-backed fintech startups fail to scale and grow – and this applies to fintechs as well. 

Here are the 5 areas that fintechs need to focus on to succeed in a competitive market.​

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Expanding AI Applications: From Generative AI to Business Transformation

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Generative AI has stolen the limelight in 2023 from nearly every other technology – and for good reason. The advances made by Generative AI providers have been incredible, with many human “thinking” processes now in line to be automated.  

But before we had Generative AI, there was the run-of-the-mill “traditional AI”. However, despite the traditional tag, these capabilities have a long way to run within your organisation. In fact, they are often easier to implement, have less risk (and more predictability) and are easier to generate business cases for. Traditional AI systems are often already embedded in many applications, systems, and processes, and can easily be purchased as-a-service from many providers.  

Traditional vs Generative AI

Unlocking the Potential of AI Across Industries 

Many organisations around the world are exploring AI solutions today, and the opportunities for improvement are significant: 

  • Manufacturers are designing, developing and testing in digital environments, relying on AI to predict product responses to stress and environments. In the future, Generative AI will be called upon to suggest improvements. 
  • Retailers are using AI to monitor customer behaviours and predict next steps. Algorithms are being used to drive the best outcome for the customer and the retailer, based on previous behaviours and trained outcomes. 
  • Transport and logistics businesses are using AI to minimise fuel usage and driver expenses while maximising delivery loads. Smart route planning and scheduling is ensuring timely deliveries while reducing costs and saving on vehicle maintenance. 
  • Warehouses are enhancing the safety of their environments and efficiently moving goods with AI. Through a combination of video analytics, connected IoT devices, and logistical software, they are maximising the potential of their limited space. 
  • Public infrastructure providers (such as shopping centres, public transport providers etc) are using AI to monitor public safety. Video analytics and sensors is helping safety and security teams take public safety beyond traditional human monitoring. 

AI Impacts Multiple Roles 

Even within the organisation, different lines of business expect different outcomes for AI implementations. 

  • IT teams are monitoring infrastructure, applications, and transactions – to better understand root-cause analysis and predict upcoming failures – using AI. In fact, AIOps, one of the fastest-growing areas of AI, yields substantial productivity gains for tech teams and boosts reliability for both customers and employees. 
  • Finance teams are leveraging AI to understand customer payment patterns and automate the issuance of invoices and reminders, a capability increasingly being integrated into modern finance systems. 
  • Sales teams are using AI to discover the best prospects to target and what offers they are most likely to respond to.  
  • Contact centres are monitoring calls, automating suggestions, summarising records, and scheduling follow-up actions through conversational AI. This is allowing to get agents up to speed in a shorter period, ensuring greater customer satisfaction and increased brand loyalty. 

Transitioning from Low-Risk to AI-Infused Growth 

These are just a tiny selection of the opportunities for AI. And few of these need testing or business cases – many of these capabilities are available out-of-the-box or out of the cloud. They don’t need deep analysis by risk, legal, or cybersecurity teams. They just need a champion to make the call and switch them on.  

One potential downside of Generative AI is that it is drawing unwarranted attention to well-established, low-risk AI applications. Many of these do not require much time from data scientists – and if they do, the challenge is often finding the data and creating the algorithm. Humans can typically understand the logic and rules that the models create – unlike Generative AI, where the outcome cannot be reverse-engineered. 

The opportunity today is to take advantage of the attention that LLMs and other Generative AI engines are getting to incorporate AI into every conceivable aspect of a business. When organisations understand the opportunities for productivity improvements, speed enhancement, better customer outcomes and improved business performance, the spend on AI capabilities will skyrocket. Ecosystm estimates that for most organisations, AI spend will be less than 5% of their total tech spend in 2024 – but it is likely to grow to over 20% within the next 4-5 years. 

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Meeting Emerging Threats with Intelligent Strategies in BFSI

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Trust in the Banking, Financial Services, and Insurance (BFSI) industry is critical – and this amplifies the value of stolen data and fuels the motivation of malicious actors. Ransomware attacks continue to escalate, underscoring the need for fortified backup, encryption, and intrusion prevention systems. Similarly, phishing schemes have become increasingly sophisticated, placing a burden on BFSI cyber teams to educate employees, inform customers, deploy multifactor authentication, and implement fraud detection systems. While BFSI organisations work to fortify their defences, intruders continually find new avenues for profit – cyber protection is a high-stakes game of technological cat and mouse!

Some of these challenges inherent to the industry include the rise of cryptojacking – the unauthorised use of a BFSI company’s extensive computational resources for cryptocurrency mining.

What Keeps BFSI Technology Leaders awake at night?

Building Trust Amidst Expanding Threat Landscape

BFSI organisations face increasing complexity in their IT landscapes. Amidst initiatives like robo-advisory, point-of-sale lending, and personalised engagements – often facilitated by cloud-based fintech providers – they encounter new intricacies. As guest access extends to bank branches and IoT devices proliferate in public settings, vulnerabilities can emerge unexpectedly. Threats may arise from diverse origins, including misconfigured ATMs, unattended security cameras, or even asset trackers. Ensuring security and maintaining customer trust requires BFSI organisations to deploy automated and intelligent security systems to respond to emerging new threats. 

Ecosystm research finds that nearly 70% of BFSI organisations have the intention of adopting AI and automation for security operations, over the next two years. But the reality is that adoption is still fairly nascent. Their top cyber focus areas remain data security, risk and compliance management, and application security.

Areas that BFSI organisations are not prioritising enough today

Addressing Alert Fatigue and Control Challenges

According to Ecosystm research, 50% of BFSI organisations use more than 50 security tools to secure their infrastructure – and these are only the known tools. Cyber leaders are not only challenged with finding, assessing, and deploying the right tools, they are also challenged with managing them. Management challenges include a lack of centralised control across assets and applications and handling a high volume of security events and false positives.

Software updates and patches within the IT environment are crucial for security operations to identify and address potential vulnerabilities. Management of the IT environment should be paired with greater automation – event correlation, patching, and access management can all be improved through reduced manual processes.

Security operations teams must contend with the thousands of alerts that they receive each day. As a result, security analysts suffer from alert fatigue and struggle to recognise critical issues and novel threats. There is an urgency to deploy solutions that can help to reduce noise. For many organisations, an AI-augmented security team could de-prioritise 90% of alerts and focus on genuine risks

Taken a step further, tools like AIOps can not only prioritise alerts but also respond to them. Directing issues to the appropriate people, recommending actions that can be taken by operators directly in a collaboration tool, and rules-based workflows performed automatically are already possible. Additionally, by evaluating past failures and successes, AIOps can learn over time which events are likely to become critical and how to respond to them. This brings us closer to the dream of NoOps, where security operations are completely automated. 

Threat Intelligence and Visibility for a Proactive Cyber Approach

New forms of ransomware, phishing schemes, and unidentified vulnerabilities in cloud are emerging to exploit the growing attack surface of financial services organisations. Security operations teams in the BFSI sector spend most of their resources dealing with incoming alerts, leaving them with little time to proactively investigate new threats. It is evident that organisations require a partner that has the scale to maintain a data lake of threats identified by a broad range of customers even within the same industry. For greater predictive capabilities, threat intelligence should be based on research carried out on the dark web to improve situational awareness. These insights can help security operations teams to prepare for future attacks. Regular reporting to keep CIOs and CISOs informed of the changing threat landscape can also ease the mind of executives.

To ensure services can be delivered securely, BFSI organisations require additional visibility of traffic on their networks. The ability to not only inspect traffic as it passes through the firewall but to see activity within the network is critical in these increasingly complex environments. Network traffic anomaly detection uses machine learning to recognise typical traffic patterns and generates alerts for abnormal activity, such as privilege escalation or container escape. The growing acceptance of BYOD has also made device visibility more complex. By employing AI and adopting a zero-trust approach, devices can be profiled and granted appropriate access automatically. Network operators gain visibility of unknown devices and can easily enforce policies on a segmented network.

Intelligent Cyber Strategies

Here is what BFSI CISOs should prioritise to build a cyber resilient organisation.

Automation. The volume of incoming threats has grown beyond the capability of human operators to investigate manually. Increase the level of automation in your SOC to minimise the routine burden on the security operations team and allow them to focus on high-risk threats. 

Cyberattack simulation exercises. Many security teams are too busy dealing with day-to-day operations to perform simulation exercises. However, they are a vital component of response planning. Organisation-wide exercises – that include security, IT operations, and communications teams – should be conducted regularly. 

An AIOps topology map. Identify where you have reliable data sources that could be analysed by AIOps. Then select a domain by assessing the present level of observability and automation, IT skills gap, frequency of threats, and business criticality. As you add additional domains and the system learns, the value you realise from AIOps will grow. 

A trusted intelligence partner. Extend your security operations team by working with a partner that can provide threat intelligence unattainable to most individual organisations. Threat intelligence providers can pool insights gathered from a diversity of client engagements and dedicated researchers. By leveraging the experience of a partner, BFSI organisations can better plan for how they will respond to inevitable breaches. 

Conclusion

An effective cybersecurity strategy demands a comprehensive approach that incorporates technology, education, and policies while nurturing a culture of security awareness throughout the organisation. CISOs face the daunting task of safeguarding their organisations against relentless cyber intrusion attempts by cybercriminals, who often leverage cutting-edge automated intrusion technologies.

To maintain an advantage over these threats, cybersecurity teams must have access to continuous threat intelligence; automation will be essential in addressing the shortage of security expertise and managing the overwhelming volume and frequency of security events. Collaborating with a specialised partner possessing both scale and experience is often the answer for organisations that want to augment their cybersecurity teams with intelligent, automated agents capable of swiftly

The Resilient Enterprise
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Fintech Frontrunner: How MAS is Accelerating Financial Innovation

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As they continue to promote innovation in the Financial Services industry, the Monetary Authority of Singapore (MAS) introduced the Financial Sector Technology and Innovation Scheme 3.0 (FSTI 3.0) earlier this week, pledging up to SGD 150 million over three years. FSTI 3.0 aims to boost innovation by supporting projects that use cutting-edge technologies or have a regional scope, while strengthening the technology ecosystem in the industry. This initiative includes three tracks:

  • Enhanced Centre of Excellence track to expand grant funding to corporate venture capital entities
  • Innovation Acceleration track to support emerging tech based FinTech solutions, and
  • Environmental, Social, and Governance (ESG) FinTech track to accelerate ESG adoption in fintech

Additionally, FSTI 3.0 will continue to support areas like AI, data analytics, and RegTech while emphasising talent development. We can expect to see transformative financial innovation through greater industry collaboration.  

MAS’ Continued Focus on Innovation

Over the years, the MAS has consistently been a driving force behind innovation in the Financial Services industry. They have actively promoted and supported technological advancements to enhance the industry’s competitiveness and resilience.

The FinTech Regulatory Sandbox framework offers a controlled space for financial institutions and FinTech innovators to test new financial products and services in a real-world setting, with tailored regulatory support. By temporarily relaxing specific regulatory requirements, the sandbox encourages experimentation, while ensuring safeguards to manage risks and uphold the financial system’s stability. Upon successful experimentation, entities must seamlessly transition to full compliance with relevant regulations.

Innovation Labs serve as incubators for new ideas, fostering a culture of experimentation and collaboration. They collaborate with disruptors, startups, and entrepreneurs to develop groundbreaking solutions. Labs like Accenture Innovation Hub, Allianz Asia Lab, Aviva Digital Garage, ANZ Innovation Lab, and AXA Digital Hive drive create prototypes, and roll out market solutions.

Building an Ecosystem

Partnerships between financial institutions, technology companies, startups, and academia contribute to Singapore’s economic growth and global competitiveness while ensuring adaptive regulation in an evolving landscape. By creating a vibrant ecosystem, MAS has facilitated knowledge exchange, collaborative projects, and the development of innovative solutions. For instance, in 2022, MAS partnered with United Nations Capital Development Fund (UNCDF) to build digital financial ecosystems for MSMEs in emerging economies.

This includes supporting projects that address environmental, social, and governance (ESG) concerns within the financial sector. For instance, MAS worked with the People’s Bank of China to establish the China-Singapore Green Finance Taskforce (GFTF) to enhance collaboration in green and transition finance. The aim is to focus on taxonomies, products, and technology to support the transition to a low-carbon future in the region, co-chaired by representatives from both countries.

MAS has also promoted Open Banking and API Frameworks to encourage financial institutions to adopt open banking practices enabling easier integration of financial services and encouraging innovation by third-party developers. This also empowers customers to have greater control over their financial data while fostering the development of new financial products and services by FinTech companies.

Regulators in Asia Pacific Taking a Proactive Approach

While Singapore is at the forefront of financial innovations, other regulatory and government bodies in Asia Pacific are also taking on an increasingly proactive role in nurturing innovation.  This stance is being driven by a twofold objective – to accelerate economic growth through technological advancements and to ensure that innovative solutions align with regulatory requirements and safeguard consumer interests.

Recognising the potential of fintech to enhance financial services and drive economic growth, the Hong Kong Monetary Authority (HKMA) established the Fintech Facilitation Office (FFO) to facilitate communication between the fintech industry and traditional financial institutions. The central bank’s Smart Banking Initiatives, including the Faster Payment System, Open API Framework, and the Banking Made Easy initiative that reduces regulatory frictions help to enhance the efficiency and interoperability of digital payments.

The Financial Services Agency of Japan (FSA) has been actively working on creating a regulatory framework to facilitate fintech innovation, including revisions to existing laws to accommodate new technologies like blockchain. In 2020, FSA launched the Blockchain Governance Initiative Network (BGIN) to facilitate collaboration between the government, financial institutions, and the private sector to explore the potential of blockchain technology in enhancing financial services.

The Central Bank of the Philippines (Bangko Sentral ng Pilipinas – BSP) has launched an e-payments project to overcome challenges hindering electronic retail purchases, such as limited interbank transfer facilities, high bank fees, and low levels of trust among merchants and consumers. The initiative included the establishment of the National Retail Payment System, a framework for retail payment, and the introduction of automated clearing houses like PESONet and InstaPay. These efforts have increased the percentage of retail purchases made electronically from 1% to over 10% within five years, demonstrating the positive impact of effective cooperation and innovative policies in driving a shift towards a cash-lite economy.

The promotion of fintech innovation highlights a collective belief in its potential to transform finance and boost economies. As regulations adapt for technologies like blockchain and open banking, the Asia Pacific region is promoting collaboration between traditional financial institutions and emerging fintech players. This approach underscores a commitment to balance innovation with responsible oversight, ensuring that advanced financial solutions comply with regulatory standards.

The Future of Industries
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Building Synergy Between Policy & Technology​

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Zurich will be the centre of attention for the Financial and Regulatory industries from June 26th to 28th as it hosts the second edition of the Point Zero Forum. Organised by Elevandi and the Swiss State Secretariat for International Finance, this event serves as a platform to encourage dialogue on policy and technology in Financial Services, with a particular emphasis on adopting transformative technologies and establishing the necessary governance and risk frameworks.

As a knowledge partner, Ecosystm is deeply involved in the Point Zero Forum. Throughout the event, we will actively engage in discussions and closely monitor three key areas: ESG, digital assets, and Responsible AI.

Read on to find out what our leaders — Amit Gupta (CEO, Ecosystm Group), Ullrich Loeffler (CEO and Co-Founder, Ecosystm), and Anubhav Nayyar (Chief Growth Advisor, Ecosystm) — say about why this will be core to building a sustainable and innovative future. 

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