Unveiling the landscape of Fintech in ASEAN: assessing development, regulations, and economic implications by decision-making approach

ASEAN (Brunei Darussalam, Cambodia, Indonesia, Lao PDR, Malaysia, Myanmar, the Philippines, Singapore, Thailand, Vietnam) plays a major role in regional and global economic dynamics, contributing over 10% to East Asia and Pacific GDP. Fintech has transformed financial services across payments, lending, investment, and compliance, driving inclusion and efficiency via technologies such as AI, blockchain, and data analytics. Despite growth, Fintech development and adoption are uneven across ASEAN due to diverse economic performance and regulatory landscapes. This study addresses the research gap by evaluating Fintech’s influence and potential across ASEAN along three aspects: (1) financial activities’ influence on economies, (2) technology infrastructure, and (3) Fintech‑enabling regulatory environments. It proposes an integrated multiple‑criteria decision-making framework combining DCRITIC and EDAS for quantitative indicators and F‑EDAS for regulatory (linguistic) assessments, enabling simultaneous analysis of numerical and qualitative data to provide a comprehensive regional comparison and guidance for policymakers.

The review covers key fintech themes: adoption behavior (trust, usefulness, ease of use, social influence; demographic effects), financial inclusion for underserved populations via mobile banking and digital wallets, impacts on literacy and SME growth, and blockchain/cryptocurrencies (transparency, security, efficiency; market dynamics; regulatory challenges). It also examines fintech’s disruption of incumbent banks/insurers and collaboration/competition strategies, along with regulatory topics including consumer protection, data privacy, cybersecurity, and innovation sandboxes. Recent studies link fintech with sustainability, green leadership, corporate sustainability performance (via access to finance), crypto risk management and needed regulatory frameworks, and CSR’s influence on firm performance, noting a gap in applying these insights to ASEAN’s contexts. The review then surveys Multiple Criteria Decision-Making (MCDM): AHP, TOPSIS, MAUT, PROMETHEE/ELECTRE within MCDA, hybrid approaches, and fuzzy theory for uncertainty handling. Specific emphasis is placed on CRITIC (considering inter-criteria correlation to derive objective weights) and EDAS (distance from average solution for ranking alternatives), including fuzzy extensions that allow linguistic assessments. Combining fintech research and MCDM provides robust tools for structured, transparent assessment of fintech development across heterogeneous contexts such as ASEAN.

The study proposes an assessment framework to jointly evaluate quantitative indicators of financial activities and technology infrastructure (FA&TI) and qualitative/linguistic information on fintech‑enabling regulations (FERs). Data sources include open databases from the World Bank, United Nations, and Asian Development Bank for quantitative indicators, and the World Bank Global Fintech‑enabling Regulations database for regulatory status. - Fuzzy sets: Triangular fuzzy numbers (TFNs) are used to quantify linguistic regulatory statuses. A TFN is defined by (s, m, l) representing the smallest, most likely, and largest values. Arithmetic operations on TFNs are defined, and defuzzification uses a graded mean (s + 2m + l)/4 to obtain crisp values. - DCRITIC (Distance-based CRITIC): For quantitative indicators, the procedure is: (1) build decision matrix X of I alternatives by J criteria; (2) normalize by range to obtain Y (benefit/cost-sensitive); (3) compute standard deviation per criterion; (4) construct Euclidean distance matrices per criterion between all pairs of alternatives; (5) compute row, column, and grand means; (6) double-center each distance matrix; (7) compute distance covariance (dCOV) between criteria via Hadamard product of double-centered matrices and averaging; (8) distance variance (dVAR) on diagonals; (9) distance correlation (dCOR) from dCOV and dVAR; (10) information content ICj = sum over criteria of (1 − dCORij); (11) objective weights wj = ICj / sum(IC). - EDAS: Using normalized quantitative data and DCRITIC weights, compute the average solution per criterion, then positive and negative distances from average for each alternative. Aggregate weighted positive and negative distances to obtain an appraisal score in [0,1]; higher is better. - Fuzzy EDAS (F‑EDAS): For qualitative FER statuses, assign TFNs to each status level, construct a fuzzy decision matrix, compute the fuzzy average solution, determine fuzzy positive/negative distances, and defuzzify to crisp values to obtain appraisal scores. Due to lack of evidence on relative importance of regulations, equal weights are applied across FER criteria. The combined framework yields comparable FA&TI and FER appraisals, enabling positioning and group analysis across ASEAN.

- Data and indicators: Six FA&TI indicators were used: (1) GDP by financial and insurance activities (converted to USD), (2) Money supply (% of GDP), (3) Secure internet servers per million people, (4) Telecommunication Infrastructure Index (UN), (5) Online Service Index (UN), (6) Individuals using the Internet (% population). - Indicator variance and correlations (DCRITIC): Telecommunication Infrastructure Index (14) had the lowest variance across ASEAN, while GDP by financial and insurance activities (11) had high variance, indicating heterogeneous financial sector size. Correlation between indicator 1 (GDP finance/insurance) and indicator 5 (Online Service Index) was high (r = 0.849), suggesting co-movement of financial sector scale and online service maturity. - Objective weights (DCRITIC): Indicator weights prioritized: GDP finance/insurance (22%), Secure internet servers (22%), Money supply (17%), Individuals using Internet (15%), Telecommunication Infrastructure Index (12%), Online Service Index (12%). Indicators 1 and 3 provided the highest information content. - FA&TI rankings (EDAS): Singapore led ASEAN with the highest FA&TI appraisal score (leader group). Rapidly growing group: Vietnam and Thailand. Intermediate: Cambodia and Malaysia. Next tier: Indonesia, the Philippines, and Brunei Darussalam. Lowest: Myanmar and Lao PDR, reflecting constraints in infrastructure and financial sector development. - FER status (F‑EDAS): Regulatory statuses were mapped to TFNs: No legislation (1,1,3), Sandbox (1,3,5), Guided by other laws (3,5,7), Draft law (5,7,9), Law (7,9,9). Appraisal results (examples): Singapore 0.8934 (leader); Philippines 0.8027; Thailand 0.7619; Malaysia 0.6379; Indonesia 0.5357; Lao PDR 0.5079; Brunei Darussalam 0.4084; Vietnam 0.2797; Cambodia 0.1544; Myanmar 0.0025. - Joint positioning (FA&TI vs FER): Singapore attained FA&TI 1.0000 and FER 0.8934. Malaysia showed relatively stronger FA&TI (e.g., 0.5167 cited) with moderate FER. The Philippines had strong FER progress (0.8027) with moderate FA&TI (e.g., 0.3100 cited). Vietnam and Lao PDR showed some FER progress but need FA&TI improvements. Myanmar lagged on both dimensions. - Policy implication: Countries should concurrently strengthen technology infrastructure, financial sector depth, and enabling regulations; high performers offer models, while low performers require targeted reforms to catalyze fintech adoption and inclusive growth.

The study’s central question—how fintech development differs across ASEAN and how FA&TI and FERs jointly shape readiness—was addressed by a unified quantitative–linguistic framework. Results show that countries with advanced infrastructure and sizable financial sectors tend to also develop comprehensive regulatory regimes (e.g., Singapore), supporting innovation while managing risks. Conversely, countries with emerging regulatory frameworks but weaker FA&TI (e.g., Philippines, Lao PDR, Vietnam) face constraints translating policy into widespread digital finance usage. The high correlation between financial sector activity and online service maturity suggests complementary investments in digital public infrastructure and financial sector capabilities. The distinct weighting of indicators via DCRITIC emphasizes the importance of secure digital infrastructure (secure servers) along with sectoral economic contribution. By mapping both FA&TI and FERs, the positioning analysis identifies tailored policy pathways: strengthening regulatory clarity and consumer/data protections where FA&TI is advanced; and prioritizing foundational infrastructure, connectivity, and financial sector deepening where FERs are progressing but usage lags. Overall, the results provide actionable insights for sequencing reforms and targeting cross-border collaboration to reduce regional disparities.

The paper introduces a robust decision-making framework combining DCRITIC with EDAS and its fuzzy extension to assess fintech development across ASEAN using both numerical indicators and linguistic regulatory data. Empirically, it reveals wide heterogeneity: Singapore leads on both FA&TI and FERs; Thailand, Malaysia, Indonesia, and the Philippines show meaningful regulatory or infrastructural advances with varying gaps; Vietnam, Lao PDR, and Cambodia exhibit partial progress; and Myanmar significantly lags. The contributions are twofold: (1) methodological—demonstrating a unified approach for integrating objective weighting and fuzzy linguistic evaluation; and (2) practical—providing a comparative benchmark and policy guidance for enhancing infrastructure, financial sector depth, and enabling regulation to spur fintech-driven growth and inclusion. Future research should refine regulatory weighting, broaden indicators (e.g., consumer adoption, digital financial service quality), integrate primary data, and adopt longitudinal and cross-regional comparisons to capture the sector’s rapid evolution.

- Regulatory weights were not investigated or differentiated; FER criteria were equally weighted due to data limitations. - Rapidly changing fintech landscapes may outdate data inputs; results reflect a time-bound snapshot. - Indicator selection, while broad, may omit important facets (e.g., adoption intensity, service quality). - Reliance on secondary data can introduce measurement biases and may not capture latest regulatory changes or on-the-ground realities. - Future work should employ longitudinal designs, include primary data (expert interviews/surveys), expand indicators, and compare with other regions.