It is not a controversial opinion that increasing technological intensity in an economy increases prosperity. Certainly, the benefits of a tractor over a farm animal or automated mass production over handicraft production make this apparent.

Thailand, like many countries, has been attempting to build a local tech innovation ecosystem where local startups grow to be tech unicorns.

In the period after the global financial crisis with zero per cent interest rates, it seemed plausible that every country could build its own version of a mini-silicon Valley as global venture capital investment exploded from US$43 billion in 2007 to over US$300 billion a year from 2018 on.

Unfortunately, despite huge efforts by many fantastic people in the private sector and the government, the level of start-up activity has stubbornly refused to grow and is about the same level now as 5 years ago.

And the output of the ecosystem reflects this. The UK, which has a similar population size to Thailand, has about 3–4000 funded deals a year. Of which about 2000 are seed or early stage. Thailand is having a great year if it gets over 30.

Also Read: The upside of conglomerate influence in Thailand’s tech industry

The cumulative result of this over time is that Thailand has 658 funded tech startups in its ecosystem and the UK has over 43 thousand.

Source: StartupBlink

So now that we have the evidence that the current strategy is not achieving the desired output, what should a country like Thailand do?

The Silicon Valley/Cambridge Cluster model of tech startup and scale up requires a number of factors to all be in place simultaneously to work:

• It takes a lot, a lot, of patient yet high risk capital
• It requires large numbers of highly educated young people willing to take risks
• It requires a lot of experienced service providers to support startups (70 per cent of the employment in tech clusters is in support companies such as marketing agencies, lawyers, etc)
• It requires world class universities that have the right policies on IP for spin outs
• It requires a legal and tax system that is conducive to the risk and reward nature of tech investing
• It requires a culture that views failure as something that develops skills so everyone in the system is more willing to take risk

If any of these factors are missing, the ecosystem fails to thrive. And out of these six factors Thailand has, it could be argued, none.

And as shown above, the outputs reflect this with a small number of funded startups each year that is barely growing.

Which of these factors can Thailand realistically change in the short to medium term? Again, probably none.

This is not because Thailand is worse than any other country. Despite a tsunami of money over the last decade and more, 75 per cent of all unicorns still come from just three ecosystems. Its just not a model that works in many places.

So what should a country like Thailand do? Just give up and accept it will just be a customer buying new technology from overseas forever?

An alternative strategy is that Thailand (and other countries in a similar position) should do in technology entrepreneurship what it does in every other business, understand that it cant do everything itself, that its part of global supply chains and find its place in those chains where it can create prosperity for itself.

An example for Thailand that worked before was its focus on being part of global automotive component supply chains rather than building a national car company like Malaysia tried.

Also Read: Thailand’s startup ecosystem in 2024: Fewer funding announcements, but promising opportunities ahead

Once the component production was in place and the workforce developed skills, experience and international relationships, and the government and local partners understood what investors needed, more of the supply chain began to be deployed in Thailand until, eventually, Thailand not only became a global assembly hub for cars, “The Detroit of Asia”, but now is a leading destination for global investment in EV production based on this foundation of acquired expertise and infrastructure.

However, now is not then. And the companies producing the technologies that create enormous value today have different requirements than in the past when Thailand was first industrialising.

Cheap labor, cheap land for factories, easy environmental regulations, good physical infrastructure in the industrial zones, policies based on large investments paying off over several years and a government capable of working with large foreign companies aren’t what are needed anymore.

The new industrial strategy needs to work with the smaller and midsized growth tech companies rather than the mature tech companies where its just a customer or competes with low cost countries to be a supplier.

So it needs to be focused around highly skilled local staff, flexibility in location, ease of foreign workers working in the country in flexible time periods, high environmental standards, excellent digital infrastructure and a government that knows how to work with foreign startups and SMEs.

And similarly, government goals based on investment amounts, employment generated and exports aren’t appropriate in an age when 55 employees can generate in four years US$19 billion of value as those at WhatsApp did.

The great thing about the leading tech clusters is that they are already highly internationalised and are very open to working with all comers. Its well known the majority of US unicorns are created by immigrants as just one example, showing the willingness of the VC industry to invest in newcomers and development of the global internet in the last 25 years means physical proximity becomes optional.

Thailand’s tech ecosystem should become something that extends beyond it’s borders and overlaps the existing global tech ecosystems. And vice versa, it should be easier for the world to work with and in Thailand.

And most importantly, there needs to be real deployment projects that both sides get to work alongside each other and build the mutual understanding, trust and ability to achieve goals together and solve problems that can’t be developed theoretically or through discussion at conferences.

And, paradoxically, the interactions with the earlier stage of the global tech ecosystems must be led by the larger Thai companies. As I’ve discussed before the presence of “Institutional Gaps” means that the deployment of new technologies is easier for companies that have the highly skilled staff on hand and the resources to bridge those gaps.

It looks initially as if its cementing large corporate dominance, but it will help create an ecosystem that is intertwined and connected to the leading tech clusters of the world, prosperous and growing, with exposure to the new technologies and trends earlier that will fuel the next generation of Thai entrepreneurs that will have benefited from working on successful tech deployment projects with young international tech companies and built up the skills, experience and international networks to give them the confidence and access to resources to leave the corporates and make their own entrepreneurial activities a success.

So anyone looking to have an impact on an emerging market tech ecosystem I would suggest not becoming the 47th investment fund or the 23rd accelerator, but to help build bridges, working relationships and extended networks between locals and the global clusters that will make the country an extended part of a larger ecosystem that supplies from its entirety, not just locally, all the factors necessary for tech innovation success.

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The Echelon X fireside chat titled ‘Market Access in the Middle East: Myth or Truth in This Opportunity?’ explored the often-overlooked potential of the Middle East as a lucrative market for businesses seeking global expansion. The session aimed to separate fact from fiction and provide practical insights for enterprises interested in entering and succeeding in the Middle Eastern market.

Moderated by Fatima Almubbad, Director of Singapore and Southeast Asia at the Bahrain Economic Development Board (EDB), the fireside chat featured Hian Goh, Partner at Openspace Ventures.

In this discussion, participants delved into the opportunities and challenges associated with accessing the Middle Eastern market. The conversation highlighted that while the Middle East presents significant potential for growth, it is also a region filled with complexities that require a nuanced understanding. The panellists examined key factors such as market readiness, cultural considerations, regulatory environments, and the unique economic landscape of the region.

The discussion underscored the need for businesses to be adaptable and informed when considering expansion into the Middle East. The fireside chat concluded by reaffirming that the Middle Eastern market, though often misunderstood, holds considerable potential for businesses willing to navigate its complexities.

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In the vibrant and competitive startup ecosystem of Singapore, a new wave of female entrepreneurs is making its mark.

These dynamic women are not only co-founding innovative ventures but are also redefining what it means to be a leader in the tech-driven world of business.

From groundbreaking tech platforms to disruptive services, their startups are pushing boundaries and setting new standards for success.

This feature highlights the inspiring stories of Singapore-based startups co-founded by women, showcasing the diversity, creativity, and resilience that these entrepreneurs bring to the table.

Grab

A super-app platform to book various services, including transportation, deliveries, mobility, and financial services.

Founding year: 2012
Female co-founder: Tan Hooi Ling
Total investment raised: US$10.38 billion
Investors: Emtek, Signite Partners, Hana Financial Group, GGV Capital, K3 Ventures, Flourish, Arbor Ventures, STIC Investments, Krungsri, MUFG Innovation Partners, TIS, Kymco Capital, Experian, Invesco, others.

Advance Intelligence Group

A startup providing big data- and AI-based digital transformation, fraud prevention, and process automation solutions for enterprise clients in banking, fintech, retail, and e-commerce. It is the parent company of Atome.

Also Read: Advance Intelligence Group raises US$80M to further develop AI innovations

Founding year: 2016
Female co-founder: Tongtong Li
Total investment raised: US$700 million
Investors: Warburg Pincus, Northstar Group, EDBI, Vision Plus Capital, Gaorong Capital, Northstar Group Services, SoftBank Vision Fund, K3 Ventures.

PatSnap

A startup offering patent analytics and management software. It provides users with patent search and analysis to manage and advance IP positions. The platform monitors the patent risk and thereby protects the IP assets in real-time. Its product offerings include market and competitor data discovery solutions, data visualisation, reports, and managing datasets that enable users to add patent and legal data.

Founding year: 2007
Female co-founder: Guan Dian
Total investment raised: US$352 million
Investors: Tencent, SoftBank Vision Fund, CITIC, Shunwei Capital, Vertex Ventures, HongShan, Qualgro, Summit Partners, Vertex Growth, Global Brain, NUS Enterprise, JIC Investment.

Zilingo

An online B2B marketplace platform offering multi-category fashion products. The product catalogue includes loungewear, kidswear, shirts, winter wear, and sleepwear bed linen.

Founding year: 2015
Female co-founder: Ankiti Bose
Total investment raised: US$304 million
Investors: Sequoia Capital, Temasek, Burda Principal Investments, Sofina, EDBI, Beenext, Venturra Capital,  SIG, Wavemaker Partners, Beenos, Amadeus Capital, Draper Associates,
Draper Venture Network, Koru Partners, DG Ventures, Beeble Brox, Angel Capital Management, Dahlia Investments & Consulting.

YouTrip

An NFC-enabled travel card and wallet provider for travellers. The wallet can be recharged via credit/debit cards and used for online/offline purchases, bill payments, and cash withdrawals. The wallet can be used for managing transactions of the card, in-store payments, and online payments.

Also Read: The 4 steps that YouTrip has taken to ensure financial resilience in a time of crisis

Founding year: 2018
Female co-founder: Caecilia Chu
Total investment raised: US$105.5 million
Investors: Lightspeed Venture Partners and Insignia Ventures Partners.

Silent Eight

An AI-based fraud management startups. The platform offers a platform that enables users to scan data sources in variable formats, including local and remote online news articles. It also provides solutions for automated alert adjudication, name screening, transaction screening, and transaction monitoring.

Founding year: 2013
Female co-founder: Julia Markiewicz
Total investment raised: US$61 million
Investors: TYH Ventures, HSBC, OTB  Wavemaker Partners, SC Ventures, Aglaia, Crystal Horse Investments, Joyful Frog Digital Innovation, Singapore Angel Network, Fanjul Capital, Riverwalk Holdings, Fintonia Group, SpinUp Partners.

cxagroup

Connexions Asia (CXA) provides an AI-based benefits marketplace for employer insurance. It offers a platform for health insurance, HR management, employee benefits, and health & wellness plans. CXA also provides an AI-based app for people to connect them with health & wellness products and services.

Founding year: 2013
Female co-founder: Rosaline Chow Koo
Total investment raised: US$580 million
Investors: Humanica, HSBC, Heritas Capital Management, MDI Ventures, Sumitomo, Openspace Ventures, Singtel Innov8, Singapore Economic Development Board, HSBC, B Capital, EDBI, BioVeda Capital, RGAX, FengHe Asia, Philips, FengHe Group, Bansea, Propell Group, WoodOwl, Redmoon Advisors, Insurtech Hub.

Wiz.AI

The startup provides AI-powered voice and speech recognition solutions for multiple industries. Its talkbot platform allows users to record voice conversations with text translations and interact with real humans. It uses a neural network technology that allows customer classification, identification of customers, and segments to prioritise follow-ups.

Founding year: 2019
Female co-founder: Jennifer Zhang
Total investment raised: US$58 million
Investors: Tiger Global Management, Yunqi Partners, Gaorong Capital, GL Ventures, K3 Ventures, Singtel Innov8, GGV Capital, Wavemaker Partners, Insignia Ventures Partners,
Hillhouse, Singtel, Graphene Ventures, Gaorong Partners Fund, K3 Aquarius Fund, ZWC,
Weiguang Ventures, Em monster, ZWC Ventures, GLOBAL ACCELERATION ACADEMY,
Plug and Play APAC.

Paktor

Paktor is a provider of a dating platform based on mutual likes. The platform enables users to register and swipe left/right to like/pass registered members’ profiles. It matches profiles based on mutual likes. Users can chat and connect with the profiles via the app.

Founding year: 2013
Female co-founder: Charlene Koh
Total investment raised: US$575 million
Investors: K2 Global, Media Nusantara Citra, Vertex Ventures, YJ Capital, Golden Equator Capital, Sebrina, Majuven, Convergence Ventures, Turn Capital, K2 Global

TurtleTree

A producer of cell-based sustainable food and dairy product alternatives. The company uses cell-based technology to extract cell samples from cattle that are further grown in a proliferate growth medium into muscle fibre and dairy ingredients and are used for the production of cultivated meat and dairy products.

Founding year: 2019
Female co-founder: Fengru L
Total investment raised: US$42.3 million
Investors: Verso Capital, Artesian, SHOSHIN8, Green Monday, KBW Ventures, Verso Holdings,
Eat & Beyond, EWC, CPT Capital, New Luna Ventures, Lever VC, K2 Global, Unreasonable, Good Startup, Smile, Chaos Ventures, XA Network, Siddhi Capital, Plug and Play APAC, Wavemaker Impact, Highfield Capital.

Browzwear

A startups working in interactive 3D product design and customisation software solutions for the fashion industry. The company provides 3D software for apparel design and development through size ranges, graphics, fabrics, trims, colourways, styling, & photorealistic 3D rendering, visualises fabric folds in real-time and enables product creation and design through digital samples.

Founding year: 2012
Female co-founder: Lena Lim
Total investment raised: US$35 million
Investor: Radian Capital.

Shiok Meats

A manufacturer and supplier of lab-cultured meat products. It offers lab-cultured and cell-based meat and seafood. The company claims that its meats are animal-friendly, health-friendly, and environment-friendly.

Also Read: Shiok Meats CEO Sandhya Sriram to step down after merger with Umami Bioworks

Founding year: 2018
Female co-founder: Sandhya Sriram
Total investment raised: US$30.7 million
Investors: Woowa Bros, CJ CheilJedang, Vinh Hoan, Toyo Seikan Kaisha, Twynam, Monde Nissin, Big Idea Ventures, Boom Capital Ventures, Beyond Impact Advisors, Aqua Spark, METI,
Realtech Fund, VegInvest, Makana Ventures, AiiM Partners, Irongrey, Ilshin, Yellowdog, SEEDS Capital, Agronomics, Impact Venture Capital, Mindshift Capital, Y Combinator, BOOM CAPITAL GROUP, Aera VC, Entrepreneur First, Future Food Asia, CPT Capital, Success Accelerator, Innovate 360.

DocDoc

DocDoc is an online platform that uses AI to connect users to doctors. The platform uses HOPE, an AI-powered doctor discovery engine, to find doctors based on users’ medical needs. The platform lists information about clinics and doctors, along with their locations, clinical interests, subspecialties, procedures available, and so on, to enable users to compare and book appointments.

Founding year: 2012
Female co-founder: Grace Park
Total investment raised: US$29.6 million
Investors:  Sumitomo Corporation, Adamas Finance Asia, Cyberport, SparkLabs Global Ventures, Vectr Ventures, Hong Leong Financial Group, KCP Capital,
Jungle Ventures, 500 Global, Hong Leong, Apis Partners, RVP Group, Gaingels, Bells Ventures,
Plug and Play APAC.

Parcel Perform

A cloud-based software startup for e-commerce store operators to compare and book carriers for sending packages. Businesses can compare prices from multiple carriers and view KPIs such as geography coverage, transit times, etc. It also provides a solution for the courier service provider to manage their deliveries, track customer queries, and creating own dashboards to track performance.

Founding year: 2016
Female co-founder: Dana von der Heide
Total investment raised: US$21 million
Investors: Cambridge Capital, Wavemaker Partners, Investible, SBVA, Investigate, 500 Global, Bansea, Silicon Straits Saigon, Acequia Capital, RTL Group Investments, MobilityFund,
Endeavour Ventures, Investigate, ReadyVentures, True Growth Capital, Artiel Ventures

Raena

An app-based reselling and dropshipping marketplace for beauty businesses, it allows users to discover and buy products to sell multi-category beauty products across various brands and earn profits.

Founding year: 2019
Female co-founder: Sreejita Deb
Total investment raised: US$20.8 million
Investors: AC Ventures, Alpha Wave Global, Alfamart, Alto Partners, Alpha JWC Ventures,
Beenext, Beenos, STRIVE, Orient Growth Ventures

Tookitaki

A startup providing of anti-money laundering solutions. The platform features include AML transaction monitoring, customer risk scoring, customer screening, regulatory compliance, case management, and customer due diligence. It also offers financial crime detection and prevention solutions for banks and fintech companies.

Founding year: 2012
Female co-founder: Jeeta Bandopadhyay
Total investment raised: US$20.4 million
Investors: Illuminate Financial, Nomura, Viola Group, Jungle Ventures, SIG Venture Capital, SEEDS Capital, Enterprise Singapore, Supply Chain Angels, T-Hub, RevTech Labs, The FinLab,
Rebright Partners, Blume Ventures, India Internet Fund, IIMA Ventures, Srijan Capital, Tempus Capital, Microsoft Accelerator, Queen City FinTech, Innoven Capital, Aditya Birla Bizlabs, Faktory Ventures, Nomura Capital Partners, CFV Ventures, voyager.nomura.co.in, Somdutta Singh, peercheque

Morph

A consumer-centric blockchain platform. It offers developers solutions for blockchain explorer, bridge, node management, faucets, and more. It also offers blockchain sequence, rollups, and blockchain layer-2 solutions.

Founding year: 2023
Female co-founder: Cecilia Hsueh
Total investment raised: U$20.3 million
Investors: DRAGONFLY, Pantera Capital, Foresight Ventures, Spartan Group, Symbolic Capital, Publicworks, MH Ventures, Every Realm, Bitget.

Klub

An online marketplace for revenue-based financing, Klub offers business financing options based on data-driven analytics, financial innovation, and community engagement. It also provides financing in return for a fixed percentage of revenue generated. It also offers private market investors to invest in consumer-focused industries including cafes, bars, lifestyle, and more.

Founding year: 2019
Female co-founder: Harshita Sanganeria
Total investment raised: US$12 million
Investors: Northern Arc Capital, Trifecta Capital, Surge, Alter, GMO Venture Partners, 100Unicorns, EMVC, Tracxn Labs, Venture Catalysts, Better Capital, Earlsfield Capital, Astir Ventures, Techmind, Gurukul Venture Partners, Groundupp Ventures, FairAngels, Aperio Partners, FBC, CapFort Ventures.

Nalagenetics

The startup develops genetic test kits for precision medicine and offers a range of genetic tests and assays. The genetic tests are used to analyse drug reactions along with information from information management systems. Nalagenetics has also developed a clinical decision support system that uses the data and provides clinical recommendations. The information enables doctors to provide prescriptions or treatments. The company also offers patients an app for information on medication side effects.

Founding year: 2016
Female co-founder: Levana Sani
Total investment raised: US$13.6 million
Investors:  Intudo Ventures, Vulcan, dxdhub.sg, A*STAR, Integra Partners, Diagnos Laboratorium Utama, East Ventures, Founders Fund, Vulcan Capital, Brama One Ventures, Plug and Play APAC.

NSG BioLabs

A provider of level-2 biosafety co-working laboratory and office space. It offers various services and facilities, including a laboratory facility for molecular, cellular, and microbiological research, HEPA-filtered HVAC systems, and room for tissue culture and microbial work.

Also Read: How NSG BioLabs aims to nurture biotech innovation in Singapore and beyond

Founding year: 2019
Female co-founder: Daphne Teo
Total investment raised: US$14.5 million
Investors: Clavystbio, Celadon Partners, NSG Ventures.

Zimplistic

A manufacturer of automatic roti makers. It uses patented AI technology to measure and mix the correct ratio of flour and water in real-time. Its product can also be used for making paranthas, pooris, wraps, quesadillas, and other food items.

Founding year: 2008
Female co-founder: Pranoti Nagarkar
Total investment raised: US$16 million
Investors: Openspace Ventures, Robert Bosch Venture Capital.

Vaniday

An online platform offering salon booking services. It provides beauty, wellness, and fitness services and products. The platform allows users to browse salons and spas in the local area and book appointments for various services such as hair removal, massage, spa, and makeup. It also provides beauty products such as cosmetics, eye care, hair care, nail care products, and more.

Founding year: 2015
Female co-founder: Ruth Teo
Total investment raised: US$16.6 million
Investors: Rocket Internet, The Asia Pacific Internet Group, Vorwerk Ventures, HV Capital.

LionsBot International

The startup develops cleaning robots for commercial applications. Its characteristics include the ability to convey its emotions through its eyes and voice. Some of the other features include obstacle avoidance, auto-docking capabilities, AI-enabled batteries, and multiple cleaning modes. It comes with soft bumpers, an emergency stop button, and clear lights and sounds to avoid collisions with people.

Founding year: 2018
Female co-founder: Michelle Seow
Total investment raised: US$17 million
Investors: TransLink Capital, Supersteam.

Miya Health

A digital medical cost management solution for payors and employers. It offers a navigation service for patients that helps manage chronic illness and a platform that assists payers and corporates in reducing medical and administrative costs.

Founding year: 2018
Female co-founder: Shirley Ah-Hee
Total investment raised: US$17 million
Investors: Fondation Botnar, ST Engineering, Elev8, HealthXCapital, Central Capital Ventura,
SEEDS Capital.

Geniebook

An AI and app-based platform offering adaptive learning solutions for students. The platform can identify a child’s weaknesses and generate targeted questions. It enables users to improve their learning speed by practising questions at their own pace. Additionally, it provides worksheets, live and recorded classes, and more. Its app is available for Android and iOS devices.

Also Read: From brick-and-mortar to AI-powered learning: The journey of Geniebook

Founding year: 2015
Female co-founder: Alicia Cheong
Total investment raised: US$18 million
Investors: Titan Capital, East Ventures, Lightspeed Venture Partners, Apricot Capital

Us2.ai

An AI-powered tool for the detection of heart risk. The company’s flagship product, Echo Copilot, provides fully automated, real-time echo reports and disease detection, supporting healthcare professionals in interpreting echocardiograms.

Founding year: 2017
Female co-founder: Dr Carolyn Lam
Total investment raised: US$19 million
Investors: IHH Healthcare Berhad, HEAL Partners, Peak XV Partners, Pappas Capital, EDBI,
Partech Partners, Sequoia Capital, SGInnovate, StartUp Health, Startup SG, A*STAR, Fabrice Grinda, EPRV, Startup Creasphere, XNode.

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Data credit: Tracxn
Image Credit: 123RF

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Validus, a Singapore-headquartered digital SME lending platform, has partnered with global banking giant HSBC to raise up to US$50 million in debt facility.

The capital will be deployed through Validus’s Indonesian subsidiary, Batumbu, to support local MSMEs and address the country’s financing gap.

Also Read: 01Fintech invests US$20M in SME supply-chain financing platform Validus

Batumbu claims to have seen growing profits over two years and consistently achieved EBITDA margins exceeding 50 per cent.

According to a press release from Indonesia’s Ministry For Economic Affairs, there are currently 64.2 million MSMEs that contribute 61 per cent of the country’s GDP, absorbing 97 per cent of the total workforce in the country.

A World Bank report highlights that Indonesian MSMEs face major challenges in securing financing due to the stringent requirements imposed by banks. Despite various government initiatives, MSME loans account for only about 20 per cent of total bank loans.

The International Finance Corporation estimates that MSMEs’ financing gap is approximately US$234 billion.

Validus co-founder and Group CEO Nikhilesh Goel, said: “This long-term partnership with HSBC builds on our ongoing efforts to bridge the financing gap for MSMEs in Indonesia. We will continue to pioneer innovations and drive advancements in the lending space.”

Harish Venkatesan, Head of Corporate and Business Banking at HSBC Singapore, added: “MSMEs play a key role contributing to the long-term economic success in the ASEAN region and beyond. We look forward to supporting Validus in its mission to drive regional growth through the HSBC ASEAN growth fund.”

Founded in 2015, Validus uses data analytics and AI to drive growth financing for the underserved SME sector via funds from individual and institutional investors. It holds a Capital Markets Services Licence from the Monetary Authority of Singapore (MAS) and has also received regulatory approval in Indonesia (OJK) and Thailand (SEC and BOT).

Also Read: Validus, TTC Group, Do Ventures form JV to boost SME lending in Vietnam

Validus also has a presence in Vietnam (Validus Vietnam) and Thailand (Siam Validus)

Since 2021, Validus claims to have quintupled its total funds disbursed, reaching S$5.17 billion. The company is backed by investors, including Vertex Ventures Southeast Asia and India, Vertex Growth, FMO, 01Fintech, NongHyup Financial Group, Norinchukin Bank, Aizawa Asset Management, Lotte F&L, AddVentures by SCG, VinaCapital Ventures, SEA Frontier Fund, K3 Ventures, and Openspace Ventures.

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Image Credit: Validus.

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Digital financial services in Southeast Asia are at an inflexion point, expected to generate revenues of US$38 billion by 2025 and account for 11 per cent of the total financial services industry. Banks and financial services providers are increasingly seeking advanced solutions by leveraging machine learning and AI to tap into this potential.

However, business leaders face two key concerns:

• Making AI operational with reusability, scalability and adaptability with quality data
• Making data privacy business-critical with new techniques of risk management

Solving for dual problems of quality data and data privacy 

A recent survey of 600 data leaders shows that “quality of data” is the top data-related obstacle (42 per cent of respondents) to the adoption of generative AI and large language models. Data privacy and protection (40 per cent) is the second challenge cited by participants. Additionally, researchers also predict that if current Large Language Model (LLM) development trends for training AI models continue, we may run out of available datasets between 2026 and 2032.

Big industry challenges are unlikely to be solved by a single company, working with its proprietary data. When multiple industry players pool their data and collaborate, the collective intelligence generated can solve complex problems such as money laundering, cyber resilience, supply chain management, drug discovery can be tackled more effectively. It’s a win-win situation for individual companies, the industry and customers. Among emerging technologies, Federated Learning (FL) stands out as a revolutionary approach that addresses both concerns: the growing need for data privacy while enabling banks to extract value from distributed data.

Understanding Federated Learning 

Federated Learning is a machine learning paradigm where multiple institutions (e.g., banks) can collaborate to train a shared model while keeping their data decentralised. Unlike traditional machine learning, where data is aggregated into a central location for processing, Federated Learning trains algorithms across decentralised devices or servers holding local data samples, without exchanging them. This approach is particularly appealing to industries like banking, where data privacy and security are paramount. 

Benefits of Federated Learning for operations in banking and financial services 

The benefits of Federated Learning for the banking sector include: 

• Data privacy and security preservation 
  • Protection of sensitive information, as customer information remains within each bank’s secure environment, reducing the risk of data breaches. 
  • Compliance with regulations – such as GDPR, which mandate strict control over personal data and its cross-border transfer.
• Improved model accuracy and robustness
  • Access to diverse data: By leveraging data intelligence from multiple banks, Federated Learning can create more accurate and robust risk management models. This is because the combined data set represents a wider range of scenarios and customer behaviours, leading to better generalisation and prediction capabilities. 
  • Enhanced fraud detection: With access to a broader set of transaction patterns and fraud cases, Federated Learning can improve the detection of fraudulent activities, reducing financial losses.
    
• Efficient resource utilisation 
  • Cost reduction: The Federated Learning approach allows banks to pool their computational resources, reducing the overall cost of model training. This collaborative approach can lead to significant savings in infrastructure and operational expenses. 
  • Accelerated model development: By sharing insights and developments, banks can accelerate the process of model refinement and deployment, leading to quicker implementation of risk management strategies.
    
• Real time risk assessment 
  • Dynamic risk modelling: Federated Learning facilitates the development of models that can be updated in real-time as new data becomes available. This is crucial for identifying emerging risks and adapting to changing market conditions promptly. 
  • Distributed decision making: By enabling localised model updates, more responsive and context-specific decision-making processes within different branches or regions of a bank are supported.
    
• Enhanced collaboration 
  • Cross-institutional collaboration: Banks can collaborate on risk management initiatives without compromising proprietary data, fostering a culture of shared knowledge and best practices within the industry. 
  • Benchmarking and standardisation: Federated Learning enables the creation of industry-wide benchmarks for risk management practices, helping banks to standardise their approaches and improve overall industry resilience.
    
• Regulatory compliance and reporting 
  • Automated reporting: Federated Learning models can be designed to automatically generate compliance reports, ensuring that banks meet regulatory requirements efficiently. 
  • Regulatory sandboxes: Regulators can use Federated Learning to test new policies and regulations on anonymised data sets from multiple banks, assessing their impact without exposing sensitive information. 

Also Read: How Web3 will revolutionise borderless banking in Southeast Asia

 Why Federated Learning is relevant for banking and financial services 

Banks handle vast amounts of sensitive data, including financial transactions, customer information, and behavioural data. This data is not only valuable for making business decisions and improving customer services, but also a prime target for cybercriminals. Moreover, banks operate under strict regulatory frameworks, which impose severe penalties for data breaches or misuse. Federated Learning can enable banks to personalise customer experiences in the following ways: 

• Risk assessment: The Federated Learning collaboration can improve various scoring models by incorporating diverse data from multiple institutions, leading to more accurate assessments of borrowers’ risk profile. When multiple banks shares anonymised and privacy-protected use cases on fraud, threat, risk behaviour, the entire industry benefits from the generated collective intelligence. This sharing of tribal knowledge from each bank, provides insights into industry benchmarks and best practices for local and regional applications to all participants. This further enables banks to understand customer risk profiles and offer relevant products.

• Fraud and money laundering management: Federated Learning intelligence can teach individual bank predictive models, far deeper correlation identifiers for bad actors and bad actions based on private data. This can help identify potential vulnerabilities and mitigating them proactively, so that the customer journey remains free of incident. 

Collective intelligence: The Human Managed architecture for Federated Learning  

In 2018, Human Managed was established in Singapore, to build “collective intelligence” of the crowd – made of humans and machines. Our goal has always been to operate a multi-sided ecosystem-driven platform that gets smarter with more data, more learning and more real world use cases. 

To translate our vision into reality, we created the I.DE.A. (Intelligence Decision Action) platform that builds AI-native solutions for cyber, digital and risk problems for enterprises. This platform is a modular collection of 14 functions and 92 micro-services abstracted into infrastructure, software, data, and AI stacks.  It integrates data from any source, and develops AI models for business context and specific use cases. For individual banks, the platform enables intelligence for smarter decisions and faster actions for better cyber, digital and risk outcomes.  

Integrating data from diverse external sources and generating intelligence in real time, as in the case of risk management for multiple banks, requires privacy preserving technologies. Through Federated Learning and AI-powered apps, the HM collective intelligence platform can build a threat intelligence sharing system for banks that will ensure that:

• Relevant data with the appropriate business context is processed for each bank, via context models 
• Data structures are organised for efficient interoperability or real-time exchange of information across different systems, via STIX v2.1 standards 
• Threat detection for individual banks is agile with cyber incidents detected faster via nano models 
• Privacy across multiple banks is preserved, as a result of collaborative training of machine learning models without actual sharing of raw data via distributed Federated Learning

How it works 

Each participating bank preprocesses its data to ensure consistency and quality before entering the Federated Learning framework. A common initial model is shared among the banks, which will be locally trained on their respective datasets. Each bank trains the model locally on its own data, generating model updates (e.g., gradients).

Also Read: Gen AI in banking: How to ensure a successful transformation for an age-old industry

The local updates are securely aggregated using techniques like secure aggregation protocols or homomorphic encryption. The aggregated updates are used to refine the global model, which is then redistributed to the banks for further training. These steps are repeated iteratively until the model converges to an optimal state. 

Conclusion: The future of intelligence is collective 

The future of effective, real-time intelligence will need to be based on collaborative efforts. Federated Learning can be leveraged in banking to enhance services, improve decision-making, and ensure compliance with stringent data protection regulations.

Overtime, we believe that Federated Learning will drive digital transformation in banking and level the playing field for banks of all sizes. It will foster innovation and create new business models. It will allow for greater financial inclusion, with a greater number of people, especially the rural unbanked access services for personal and business needs.

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