Thailand’s banking sector crossed a threshold in June 2025 that few believed would arrive so quickly. The Bank of Thailand approved three virtual banking licenses (commonly referred to as digital banks), authorising consortiums led by SCB X, Krungthai Bank and Ascend Money to establish digital-only banks targeting the nation’s substantial underbanked population. With operations required to commence by June 2026, this sets the stage for the most significant competitive disruption Thailand’s financial services industry has witnessed in decades.

The approvals represent more than regulatory housekeeping. They signal Thailand’s calculated bet that technology-native banks can reach segments traditional institutions have struggled to serve profitably, whilst simultaneously forcing incumbents to accelerate digital transformation they might otherwise have delayed for years.

The Winners and What They Bring

The three approved consortiums reveal careful regulatory thinking about which combinations of capital, technology and distribution could succeed in Thailand’s competitive market.

SCB X, parent company of Siam Commercial Bank, partnered with South Korea’s KakaoBank and China’s WeBank – two of Asia’s most successful digital banking operators. The consortium brings established banking infrastructure combined with proven digital banking expertise from markets that have already navigated this transition.

Krungthai Bank’s consortium includes Advanced Info Service (AIS), Thailand’s leading telecommunications provider with 46.3 million mobile subscribers, and PTT Oil and Retail Business, which operates extensive convenience store networks. The combination offers immediate distribution reach to millions of potential customers through existing touchpoints.

Ascend Money, operator of the TrueMoney e-wallet with over 32 million users, secured approval alongside backing from Ant Group. The CP Group connection provides access to Thailand’s most extensive retail network, including over 15,000 7-Eleven stores – physical touchpoints that could differentiate their digital offering.

The Market Opportunity

Thailand’s financial services landscape combines high formal banking penetration with persistent underbanked segments – a paradox these digital banks are designed to address. A substantial portion of Thailand’s adult population remains either unbanked or underbanked, representing the target market digital banks aim to capture through digital-first propositions tailored to demographics traditional banks have found difficult to serve profitably.

Digital payment infrastructure has developed rapidly. The PromptPay system, launched in 2017, enables instant peer-to-peer transfers and has achieved widespread adoption. Mobile and internet banking reached 144.3 million accounts by October 2024, processing more than 6.28 billion transactions, according to Bank of Thailand data.

This infrastructure provides the foundation digital banks need. Rather than building payment rails, they can focus on layering lending, savings and wealth management products atop existing real-time payment systems.

 How Incumbents Are Responding

Thailand’s established banks haven’t waited passively. Major institutions have accelerated digital transformation investments, recognising that defensive positioning requires more than incremental improvement.

Kasikornbank announced a $2.7 billion strategic programme in July 2022 aimed specifically at boosting access among Thailand’s unbanked and underbanked populations, small businesses and self-employed individuals. The bank’s partnership with LINE to offer social banking services through LINE BK leverages LINE’s 56 million monthly active users in Thailand – providing distribution reach that matches or exceeds what digital banks can immediately achieve.

CEO Kattiya Indaravijaya characterised KBank’s initiative as combining the strengths of a heavyweight traditional lender with the DNA of a challenger bank to empower a whole new generation. “We’re taking a bold step and, through technological leadership, aim to transform banking in Thailand in ways that can help more people enter the banking system and benefit from banking products and services,” she said.

Siam Commercial Bank announced a major core system overhaul in 2024, partnering with Sunline to replace decades-old infrastructure. The four-year integration project positions SCB as what executives describe as a “full-fledged digital banking” leader by completion. “This is the first IT architecture revamp in over a decade. The partnership will enable SCB to quickly offer tailored financial solutions and seamless experiences to individual customers and corporate clients, setting a new benchmark for the finance industry on both the domestic and global levels,” said CEO Kris Chantanotoke.

David Becker, managing director for Asia Pacific at Mambu, captured the competitive dynamic: “A lot of us think that the virtual banks coming in 2026 will be cloud-first, cloud-native. That’s a given. They will be agile, customer-centric from the start, and will set a new bar for speed and innovation. The opportunity for the incumbents is still there, if they can execute in the right way.”

The Profitability Question

Digital banking’s economic viability remains unproven in Southeast Asia. Across Asia-Pacific, regulators have licensed numerous digital-first banks yet few have reached profitability quickly. The transition typically requires three to eight years, according to industry analysis.

Hong Kong’s eight digital banks, licensed since 2019, have made significant market inroads but most remain distant from reporting net yearly profit. Singapore’s digital banks face similar timelines.

Thailand’s approved digital banks hold one advantage: only three licenses were granted, compared to eight in Hong Kong and multiple approvals in Singapore. Reduced competition could accelerate the path to profitability by allowing each entrant to build meaningful market share before fragmentation occurs.

The consortiums’ structures suggest awareness of this challenge. By combining established customer bases – telecommunications subscribers, e-wallet users, retail touchpoints – with digital banking expertise, they’re attempting to shortcut the customer acquisition costs that have burdened purely digital entrants in other markets.

Regional Implications

Thailand’s approach differs meaningfully from other Southeast Asian markets. Singapore licensed digital banks with explicit mandates to serve SMEs and underserved segments. Malaysia’s digital banking licenses emphasised financial inclusion for underbanked populations. Indonesia’s digital bank approvals focused on extending services to the archipelago’s geographically dispersed population.

Thailand’s framework sits somewhere between. The consortiums approved combine technology sophistication with established distribution networks – a hybrid model that could prove more sustainable than purely digital approaches elsewhere in the region.

If successful, Thailand’s model could influence regulatory thinking across Southeast Asia, potentially leading to similar hybrid licensing approaches in Vietnam, Philippines and other markets considering digital banking frameworks.

What Determines Success

Customer acquisition costs in competitive markets have plagued digital banks globally. The approved consortiums’ access to existing customer bases through telecommunications, e-wallets and retail networks could mitigate this challenge; or prove insufficient if traditional banks successfully defend relationships through improved digital offerings.

Technology execution will separate winners from also-rans. Cloud-native architectures, AI-driven underwriting and seamless user experiences aren’t merely nice-to-haves but competitive necessities. Digital banks that launch with clunky interfaces or limited product sets will struggle regardless of capital backing.

Regulatory evolution will shape the playing field. If the Bank of Thailand concludes initial approvals succeeded in expanding financial inclusion, additional licenses could follow. If concerns emerge about stability or consumer protection, regulatory constraints could tighten.

For Thailand’s financial services industry, the introduction of digital banks represents an inflection point. Whether they ultimately reshape the competitive landscape or become niche players absorbed by incumbents depends on execution over the next two to three years.

What’s certain is that Thailand’s banking sector in 2028 will look markedly different from 2024, and the transformation began with those three approvals in June 2025.

Malaysian banks are deploying Artificial Intelligence (AI) at breakneck speed. But ask them to quantify the risk exposure from unexplainable algorithmic decisions, and you’ll uncover the industry’s next major challenge.

When AI denies business loans to viable SMEs or flags legitimate transactions as suspicious – and banks can’t articulate why – the risk cascades: regulatory penalties, discrimination lawsuits, reputational damage and customer attrition. Yet most institutions are measuring AI performance without measuring AI explainability risk.

Malaysian banks are accelerating AI adoption at remarkable speed. According to the Asian Institute of Chartered Bankers, 57% of financial institutions are already in early-stage AI implementation. Bank Negara Malaysia (BNM) released its “Discussion Paper on Artificial Intelligence” in August 2025 and Oracle’s multi-agent AI investigators are transforming compliance workflows across institutions.

However, when AI denies a business loan or flags a transaction as suspicious, can the bank document the decision-making process well enough in the face of regulatory scrutiny? Not with vague references to “insufficient creditworthiness.” Can they provide specific, defensible reasoning that satisfies regulators, courts and increasingly sophisticated customers?

The answer, more often than anyone wants to admit, is no.

Quantifying the Explainability Risk Exposure

Hong Leong Bank’s partnership with DCAP Digital illustrates both promise and risk. The collaboration uses AI-powered credit scoring to assess underbanked SME borrowers, particularly in motorcycle financing where over 61,000 units were registered in May 2025 alone.

Without explainability infrastructure, banks could possibly face three compounding risks:

1. Regulatory risk when BNM demands justification for algorithmic decisions
2. Legal risk when rejected applicants claim discrimination
3. Reputational risk when customers migrate to competitors offering transparent decision-making

These AI systems analyse hundreds of data points to generate credit scores. When the algorithm says no, explaining which specific factors drove that decision becomes exponentially more complex than traditional credit assessments. More critically, without systematic documentation, banks can’t defend those decisions when challenged by regulators, courts or customers.

The Regulatory Compliance Challenge

Regulators globally are converging on explainability requirements. Singapore’s Monetary Authority (MAS) emphasises transparency and explainability in AI governance frameworks. The European Union (EU) AI Act mandates clear explanations for algorithmic credit decisions. Even as US federal oversight shifts, state regulators are affirming that “the algorithm decided” is no longer legally defensible.

Bank Negara’s AI governance discussion paper emphasizes fairness, transparency and accountability. The AICB’s AI Governance Framework includes explainability as a core principle. But principles and practical implementation are very different.

Consider the risk exposure: A pattern of AI-driven loan rejections disproportionately affecting specific sectors could trigger BNM investigations. Legal discovery in discrimination cases would force banks to produce documentation they don’t have. Reputational damage compounds when media coverage frames it as “algorithms discriminating against people.”

The Risk Management Gap

Explainability techniques like SHAP and LIME allow data scientists to reverse-engineer AI decisions. Financial institutions globally are integrating these tools into workflows.

But deploying explainability tools requires different skillsets than deploying AI models. Banks need internal teams capable of interrogating models, documenting their logic and translating technical explanations into language that risk officers, compliance teams and regulators understand.

The AICB’s Future Skills Framework notes that 40,000+ banking employees will see roles evolve due to automation. That’s a massive skills transformation while AI deployment accelerates and risk exposure accumulates.

Alternative Data: Expanding Credit Access While Multiplying Risk

Malaysia’s push toward alternative credit scoring adds risk complexity. Bank Negara’s Financial Sector Blueprint encourages “forward-looking and alternative data” for credit assessment – utility payments, e-commerce transactions, digital platform engagement.

Malaysia has a RM90 billion MSME funding gap partly because traditional assessments exclude businesses without conventional lending histories. Alternative data bridges that gap.

But it multiplies explainability risk. When banks deny credit based on “atypical digital payment patterns,” how do legal teams defend it when regulators investigate discrimination or plaintiff attorneys pursue class actions?

Building Risk-Resilient Explainability Infrastructure

Bank Negara’s discussion paper on AI addresses explainability, noting existing policies are “adequate for the time being” but may require enhancement as AI complexity increases.

Risk-mature institutions are treating explainability as first-line defence, investing in:

Explainability-by-design: Embedding SHAP, LIME or similar tools into AI workflows from the start, reducing regulatory scrutiny and legal discovery exposure.

Cross-functional risk teams: Pairing data scientists with compliance officers and legal counsel who can translate technical outputs into plain language, ensuring risk functions can defend decisions when challenged.

Documentation standards: Creating systematic records of how AI models make decisions. When regulators or courts ask “why did this happen?” two years from now, banks need retrievable, defensible answers.

Scenario and discrimination testing: Stress-testing AI systems for explainability and fairness. Identifying patterns that could be interpreted as discriminatory before they become regulatory issues.

The Risk Management Imperative

Banks that master AI explainability won’t just avoid regulatory penalties. They’ll gain competitive advantage in risk management and customer trust.

In a market where 57% of institutions are deploying similar AI technologies, differentiation won’t come from having AI. It’ll come from managing AI risks better than competitors.

Gartner forecasts that ‘death by AI’ legal claims will surge to over 2,000 cases by late 2026, driven largely by inadequate risk controls around opaque algorithmic systems. Banks can build explainability infrastructure now or scramble when the first regulatory investigation forces the issue.

Malaysia’s AI governance framework provides solid foundations. Bank Negara is asking the right questions. The industry is moving with appropriate urgency. But frameworks don’t manage risk. Implementation does.

The banks investing in explainability infrastructure now aren’t just preparing for compliance. They’re managing existential risks: litigation exposure from unexplainable decisions, regulatory penalties from inadequate governance and customer attrition from eroded trust.

The question isn’t whether Malaysian banks can master AI explainability. It’s whether they can afford not to, before the first discrimination lawsuit, regulatory investigation or reputational crisis forces the issue. Right now, most institutions are accumulating risk faster than they’re building defences.

Closing that gap isn’t a 2026 priority. It’s a 2026 survival requirement.