HitPay’s Non‑Card Payment Volume Surged 124× Since 2020

Regulators Tighten the Screws

In June 2025, the Monetary Authority of Singapore levied S$960,000 in penalties across five major payment firms for inadequate customer screening, weak ownership oversight, and insufficient AML procedures. This marked the first public enforcement action under the Payment Services Act. The action crystallizes a regional shift: regulators in Singapore are tightening oversight and AI security rules, forcing Southeast Asian fintechs to accelerate AI-driven transaction monitoring and partner with specialized compliance vendors to close a widening talent gap.

The compliance burden didn't arrive as a single mandate. It landed in layers. Each regulator moved on its own timeline, each rule tightening a different screw, until fintechs found themselves facing a wall of requirements that legacy systems simply cannot clear.

Singapore moved first and hardest. MAS spent years absorbing blows from the 1MDB scandal, the Wirecard collapse, and a $2 billion money-laundering operation uncovered in August 2023 that shook confidence in the city-state's financial integrity. Parliament responded with the Anti-Money Laundering and Other Matters Act 2024, enacted in August 2024, which rewrites the prosecution playbook: prosecutors no longer need to prove a direct link between criminal activity and laundered money, authorities can sell seized assets if suspects abscond for more than six months, and casino operators must now run customer due diligence on cash transactions above S$4,000. The law also expands data sharing between government agencies and the Suspicious Transaction Reporting Office.

MAS didn't stop at legislation. Its 2024 national risk assessment flagged an escalation in cyber-enabled scams, organised crime networks, misuse of corporate structures, and cross-border digital payment risks. In April 2025, the regulator proposed updates requiring institutions to incorporate proliferation financing risk into AML programmes. MAS Notice PSN01 already sets expectations around customer due diligence, transaction monitoring, suspicious transaction reporting, and enterprise-wide risk assessments. The regulator has signalled forthcoming guidance will focus heavily on immediate risk detection rather than delayed, batch-based processes.

Malaysia's Bank Negara Malaysia has been quieter in public pronouncements, but the regional pattern is clear: FATF alignment is the baseline, and every major ASEAN regulator is converging on real-time monitoring, dynamic risk scoring, and AI-assisted screening as the new standard. The consultation paper on AI risk management published by MAS in November 2025 covers AI oversight, life-cycle controls, and capability requirements. It signals that the regulatory gaze is extending to the models themselves.

Fintechs licensed as Major Payment Institutions in Singapore, and their counterparts across the region, now face a compliance stack that requires screening both senders and beneficiaries in milliseconds, updating customer and transaction profiles continuously, and generating investigation-ready cases automatically. Legacy tools producing 90% false-positive rates are no longer defensible. The regulatory message is unambiguous: the controls must move at the speed of the payments.

Hiring Notices Reveal the Premium

Recruitment firms tracking Southeast Asian financial-services hiring say AI and AML expertise now appears in nearly every senior search brief. A 2026 review of trend reports from Morgan Kingley, Bartley, Simpson and Deoid found the question "what experience do you have with data or AI in AML" surfacing across the board. Hiring managers no longer look only for model-builders. They expect candidates to name specific screening, case-management, or dashboarding tools. Examples include ComplyAdvantage, Flagright, and Sumsub. Candidates must also articulate how those platforms cut false positives or prioritize alerts.

The demand signal lines up with the expansion of regulated fintechs across the three markets driving the regulatory push. HitPay, a Y Combinator alumnus that pivoted from e-wallet to full-stack SME payments infrastructure in 2018, now operates in Singapore, Malaysia, and the Philippines. The company raised a $15.75 million Series A led by Tiger Global in 2022 and reported that transaction volume doubled year-on-year in 2025 while non-card payment volumes surged 124 times since 2020. Its Borderless QR product, launched in January, lets merchants accept tourist wallets from across ASEAN and settle next-day in local currency. This cross-border flow triggers enhanced KYC and transaction-monitoring obligations under the new MAS rules.

HitPay's careers page lists it among "the world's and Singapore's leading fintech companies 2026," and its Series A proceeds were earmarked for building payments infrastructure from the ground up in each regulated market. That infrastructure build requires engineers who can embed screening, monitoring, and reporting logic directly into the payment rail. This is not a bolt-on compliance layer. The company's no-code platform for SMEs, activatable by software update without new hardware, further expands the surface area that needs automated risk controls.

First-party job-board data from a global payments player illustrates the compensation ceiling for this profile. Stripe posted a Machine Learning Engineer role in South San Francisco at $212,000–$318,000 in the past week, part of a batch of 56 new listings. While that posting is U.S.-based, it reflects the premium attached to ML talent that can operate inside a regulated money-movement stack. The same profile is what Southeast Asian fintechs are now competing for.

The talent pool is thin. Firms don't expect research-grade engineers; they want practitioners who have shipped monitoring rules, tuned thresholds, and worked with vendor APIs. That practical filter eliminates most academic ML candidates and pushes hiring toward engineers who have already sat inside a compliance operations team. The result is a poaching cycle: established banks and regional unicorns absorb the few engineers with live AML/AI experience, forcing growth-stage fintechs to partner with specialized vendors such as Flagright, which builds an "AI operating system for financial crime compliance," or ComplyAdvantage, recognized for name and transaction screening. Vendors become de facto talent incubators. Engineers who cut their teeth integrating Flagright or ComplyAdvantage into a core banking stack become the most recruitable profiles in the market. Fintechs that cannot hire directly buy the vendor's roadmap and the embedded expertise while they build internal capability. The hiring notices themselves reflect this dynamic: job descriptions increasingly list vendor integration experience as a required qualification, turning vendor adoption into a hiring signal.

The Stack Shifts to Real Time

Fintechs across Southeast Asia are replacing batch-oriented compliance stacks with cloud-native platforms that evaluate every transaction in milliseconds. Flagright's AI-native system processes billions of events at millisecond latency, using advanced anomaly detection and heuristic-based matching to flag suspicious patterns before funds settle. The platform automates more than 10,000 alerts and writes 10,000-plus case narratives each month, cutting manual review workloads that used to consume entire analyst teams. Integration averages one week. This is compared with the industry norm of three months. A new risk rule can be deployed in four minutes.

"AI from a deep level changes how software operates… you're not just getting a system, the software also does the work for you… we're not just selling a compliance solution anymore we're selling your compliance right the system comes with AI agents that take away some of the tasks some of the redundant repetitive tasks that a human analyst would do."

That shift is reshaping the engineering profile fintechs now hunt for. Legacy vendors built siloed modules for screening, monitoring, and case management. Flagright's architecture centralizes data first, then layers AI services on top. Madhu G. Nadig, co-founder and CTO, said the company started from a "bird's-eye view" after seeing fragmented systems cripple effectiveness at Palantir, AWS, and Forto. His team engineered a globally distributed, 24/7 infrastructure that delivers real-time risk scores for every transaction and user. This capability is now expected by regulators in Singapore.

Meanwhile, cross-border flows are accelerating. HitPay's Borderless QR processes US$50 million monthly across ASEAN wallets. Each new corridor adds on-chain and fiat monitoring requirements that legacy rule engines cannot cover.

Engineers who thrive in this stack combine three hard-to-find competencies. First, low-latency distributed systems design, the same discipline Nadig honed running mission-critical services at AWS. Second, applied machine-learning for anomaly detection, heuristic matching, and narrative generation at 10,000-plus inferences per month. Third, compliance-domain fluency, understanding KYB/KYC data models, adverse-media taxonomy, and the regulatory logic behind real-time risk cards.

Sciopay completed Flagright's API integration in seven days and gained a sandbox for continuous rule tuning. B4B Payments went live in two weeks with dedicated training and a migrated rule library. Both cases illustrate the new procurement calculus: fintechs buy compliance-as-a-service, then staff the integration, model governance, and ongoing feature work with engineers who speak both infrastructure and financial-crime language. The talent gap sits exactly at that intersection.

Salaries, Upskilling, and the Poaching Cycle

Singapore's fintech talent pool expanded 61% in 2024, and 54% of firms there plan significant hiring this year, dominated by full-time roles. The numbers reflect a regional scramble: financial crime roles in fintech grew 52% globally as firms bolster anti-money laundering defenses, while risk and compliance vacancies surged 26% even as banks cut hiring by 30%. That divergence is reshaping where engineers apply and what they demand.

Banks are feeding the fire. AI-specific roles in banking grew 13% in six months as headcount rose from 60,000 to nearly 80,000, intensifying competition with fintech for the same Python, TensorFlow, and MLOps talent. In Singapore, MAS's consultation paper on AI risk has made model governance a hiring criterion, not a nice-to-have. Engineers who can articulate how a transaction-monitoring model satisfies regulator explainability requirements command the top of the band.

Upskilling is no longer optional. CAMS certification boosts AML compliance hiring prospects by 45% over non-certified candidates. AI/ML skills appear in 78% of fintech job postings requiring Python, TensorFlow, and MLOps expertise. RegTech compliance skills show up in 72% of postings amid GDPR, PSD2, and AML pressures. Data analytics expertise, including SQL, Tableau, and real-time financial modeling, is required by 68% of roles. HitPay, operating across those three markets, kept its compliance team lean by embedding ComplyAdvantage's screening APIs and investing in internal tagging systems that let analysts focus on genuine risk rather than noise. Their technical team's deep API familiarity cut integration lead times for new market entries.

The freelance bench is deepening. Fintech contract jobs represent 1.35% of all UK IT contracts, with 356 daily rate postings. In Southeast Asia, 87% of fintech job seekers prefer hybrid or remote roles over fully on-site positions, and remote listings attract twice as many applicants. Firms that mandate four office days weekly, now 34% of hybrid roles, up from prior years, watch candidates walk to competitors offering three.

Talent flows one way: from banks cutting compliance headcount to fintechs building AI-driven monitoring from scratch. The engineers who stay in banking are the ones writing the model validation frameworks regulators now demand. Everyone else is building the models.

Three Constituencies, One Pressure

The regulatory tightening in Singapore, Malaysia, and the Philippines is not just a compliance exercise. It is restructuring the talent market. Three constituencies absorb the pressure differently, and their responses will shape the region's AI labor pool for years.

Engineers: Upskill or Displace

DBS offers the clearest signal of how incumbents treat the talent gap. The bank has deployed over 1,500 AI/ML models across 370 use cases, backed by 5.3 petabytes of data and an annual technology budget of roughly USD 1 billion. Its ADA and ALAN platforms compressed model time-to-market from 15–18 months to under three. But the infrastructure only works if people can use it. Over 18,000 employees have been trained in data management; 8,000 completed the "Data Heroes" program. The DBS Future Tech Academy, launched in 2021, targets the bank's nearly 5,000-strong technology workforce across six domains including AI/ML and cybersecurity. The iGrow platform, an AI-driven career adviser, reached 77% adoption in India within a year of its 2023 launch.

The same bank projects that AI integration will eliminate approximately 4,000 temporary and contract roles over the next three years. It has identified 13,000 employees for future-ready skills training, with over 10,000 already on learning roadmaps. The message is explicit: the floor for technical competence is rising. The middle, roles built on manual review or rules-based screening, is thinning.

At the fintech end, HitPay illustrates the lean alternative. The Singapore-headquartered PSP serves over 20,000 merchants across 12 Asia-Pacific markets. Its compliance manager, Paula Vitan, defines success as "proactively mitigate risk while ensuring all regulatory obligations are fulfilled and doing so in the most efficient and scalable manner possible." HitPay cut screening match rates from over 10% to 2–4% through configuration work with ComplyAdvantage, absorbing transaction-volume growth without proportional headcount increases. The trade-off is clear: engineers who can tune models and integrate vendor APIs replace analysts who cleared alerts manually.

Regulators: Setting the Pace

That paper, unveiled to secure AI agents in financial services, signals that model governance is becoming a regulatory artifact, not an internal checklist. The common thread: regulators now expect explainability, audit trails, and human-in-the-loop controls for any automated decision that touches customer funds or sanctions exposure. DBS's PURE framework, Purposeful, Unsurprising, Respectful, Explainable, and its Celent Model Risk Manager Award for AI and GenAI in 2025 show what "good" looks like to supervisors. Fintechs that cannot produce comparable documentation will face slower approvals or restricted licenses.

Cross-border payments amplify the burden. HitPay's expansion into Thailand, Indonesia, and the Philippines requires licensing in each market before building local payment rails. Sanctions screening of both originators and beneficiaries becomes mandatory the moment a corridor opens. The compliance stack must be portable, auditable, and multilingual. This specification favors vendors with regional coverage over homegrown point solutions.

Investors: Funding the Platform Layer

Capital is flowing to the infrastructure layer. Flagright raised a Series A to define what it calls the "AI operating system category for financial crime compliance." ComplyAdvantage is migrating customers like HitPay onto its unified Mesh platform with smarter alert prioritization. UOB's April 2025 strategic partnership with Accenture to accelerate GenAI and agentic AI adoption follows a "buy/partner" playbook that DBS's in-house strategy deliberately avoided. Both approaches create demand: one for platform engineers who can embed third-party risk engines, the other for researchers who can push model boundaries internally.

DBS's own outlook suggests a third revenue stream: packaging its AI-driven risk management, fraud detection, and personalization engines as Platform-as-a-Service for smaller financial institutions, credit unions, and non-financial companies. If that materializes, the bank becomes a competitor to the very vendors it once bought from and a new employer of the engineers who build those packaged services.

The Longer-Term Labor Market

Deloitte's 2024 Global Human Capital Trends survey found 86% of workers link organizational transparency to trust. The same research showed only 18% of financial-services executives are implementing generative AI in the talent function, versus 47% in marketing, sales, and customer service. The gap is a hiring signal: firms that apply AI to internal mobility, skill inference, and bias reduction will retain the engineers everyone else is poaching.

Southeast Asia's tourism sector, projected at $39.5 billion in 2026, fuels transaction growth that compounds the compliance load. HitPay expects ASEAN wallets to remain the core of Borderless QR volume through 2028, with non-ASEAN Asian wallets driving higher-value lifestyle and wellness transactions. Profitability is shifting from transaction fees to deeper merchant-operations integration. Every step of that shift, onboarding, KYB, ongoing monitoring, dispute resolution, will run on models that need training, monitoring, and regulatory sign-off.

The net effect: demand for ML engineers with financial-crime domain knowledge outstrips supply. Salaries in the region have not been publicly benchmarked at the granularity this shift demands, but the competition is visible in the training budgets. DBS spends USD 1 billion annually on technology; a slice of that funds the Future Tech Academy. HitPay keeps its compliance team lean by buying screening intelligence and investing in configuration expertise. UOB buys acceleration from Accenture. The winners will be engineers who can move between vendor platforms, in-house factories, and regulatory sandboxes without losing fluency in any of them.

When MAS levied those first penalties in June 2025, the message was clear: controls must move at payment speed. The engineers who can make that happen, whether they sit at Flagright, HitPay, or DBS, are now the scarcest resource in Southeast Asian finance.

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