The Complete Guide to Using AI in the Financial Services Industry in Papua New Guinea in 2025

Papua New Guinea's financial sector is primed for practical gains from AI - faster loan decisions, sharper fraud detection, and personalised services that reach remote communities - but those benefits come with real risks like algorithmic bias, evolving AI scams, and rising regulatory scrutiny (the EU AI Act is already shaping expectations).

Global experience shows AI can halve fraud losses and automate document-heavy processes, while PNG-specific ideas - such as using airtime and top-up histories for thin-file credit scoring - can expand lending to rural customers who lack formal records (Thin-file mobile-data credit scoring for PNG financial inclusion).

Closing the skills gap matters: practical training like the AI Essentials for Work bootcamp - Nucamp 15-week practical AI training teaches prompt-writing and tool use so PNG banks and insurers can deploy responsible AI that boosts inclusion without sacrificing security (AI and Financial Services: Balancing Innovation and Security - Fintech Strategy).

Papua New Guinea's AI opportunity is strikingly practical: local innovators and national institutions are already turning theory into services that matter to people - NiuPay's homegrown team has rolled out an AI-powered visa processing platform that can issue real-time decisions in minutes and scale to roughly 1,000 applications a day, while also modernising land tax collection and training graduates with the Papua New Guinea University of Technology (NiuPay AI visa processing and land tax collection case study); at the same time the Bank of Papua New Guinea's “digital kina” proof-of-concept with Soramitsu and Japanese partners demonstrated secure, instant mobile payments, QR-pay use and fund‑recovery tests that point to tangible gains in inclusion and cash-cost reduction (Bank of Papua New Guinea Digital Kina CBDC pilot report).

These pilots create immediate levers for banks and insurers: faster onboarding, lower cash-handling costs, better fraud signals and new rails for remittances - while regional support such as AUSTRAC's TAIPAN system boosts financial-intelligence capabilities to detect illicit flows.

The “so what” is clear: combining locally-built AI services with modern payments infrastructure can bring banking to remote communities and recover lost revenue, provided institutions pair deployment with strong governance and skills development.

“The CBDC proof of concept is an important step to improve the cost and efficiency of Papua New Guinea's financial system.” - Elizabeth Genia, Governor of the Bank of Papua New Guinea

For Papua New Guinea financial firms, regulation is not a distant EU problem - the EU AI Act expressly reaches beyond Europe and can apply where an AI system is placed on the EU market or its “output” is used in the EU, so PNG banks, insurers and fintechs must first map whether they are a provider or a deployer and trace any cross‑border outputs (Analysis of the EU AI Act's extraterritorial reach and provider versus deployer roles (Morgan Lewis)).

If a PNG firm's models are made available to or used by EU customers it may need an EU authorised representative, face strict obligations for high‑risk use cases (risk‑management systems, data quality, logging, technical documentation, human oversight and post‑market monitoring) and meet transparency duties for GPAI models and chatbots (Guide to EU AI Act authorised representatives and compliance obligations (William Fry)).

Preparation matters now: compliance is being phased in 2025–2027, and failures carry sharp consequences - think fines up to €35 million or 7% of global turnover - so PNG institutions should prioritise an AI inventory, role‑based governance, staff AI literacy and clear contractual clauses with overseas suppliers before scaling pilots into production.

Top AI use cases for Papua New Guinea's financial services in 2025 are intensely practical and locally relevant: thin‑file credit scoring that turns airtime and top‑up histories into lending signals to reach rural customers (see thin‑file credit scoring with mobile data), intelligent document processing to speed loan origination and insurance claims, and unified FRAML systems that merge fraud prevention with AML for cleaner, faster remittance rails; real‑time transaction monitoring and device‑fingerprint checks for P2P, e‑wallet and gateway security; biometric and automated KYC to shrink onboarding friction; and ML‑driven typology detection that spots smurfing, structuring or unusual wire patterns before loss accumulates.

Each use case reduces cost and risk in a different way - think of a reliable pattern in phone top‑ups standing in as a credit signal where no formal file exists, or an AI model flagging a suspicious round‑figure wire before it completes - and together they unlock inclusion while hardening compliance.

Prioritise pilots that pair these tools with clear governance, human oversight and role‑based controls so PNG firms can scale responsibly. Learn more about real‑time AML for remittances and risks in cross‑border flows, the practical fraud controls used in payments and wallets via SEON, and apply device‑fingerprinting best practices from payments specialists.

“SEON significantly enhanced our fraud prevention efficiency, freeing up time and resources for better policies, procedures and rules.”

Data decisions will shape whether AI lifts inclusion or creates new exposure in Papua New Guinea: the DICT has flagged the importance of distinguishing data localisation (where data is stored) from data sovereignty (which laws apply), so PNG banks and insurers should map which datasets must stay under national jurisdiction and which can be processed regionally (PNG DICT Data Governance Awareness: data localisation vs data sovereignty); note that PNG currently lacks a comprehensive data protection law, so prudent institutions should treat sovereignty as a practical control rather than a legal checkbox and plan for local copies or private‑cloud zones as a default (Papua New Guinea data protection overview - DataGuidance).

On the technical side, client‑side encryption and local key management let organisations use global cloud services while keeping effective control - protecting sensitive KYC, thin‑file credit and claims data so a customer in a remote village retains authority over who can decrypt their records (End-to-end encryption and client-side key management - Virtru).

Practical deployment choices include hybrid on‑prem/private‑cloud architectures, region‑based processing for latency‑sensitive scoring, strict access controls and audited logging for any cross‑border flows; these measures reduce regulatory friction, harden resilience and keep AI models useful where connectivity and trust matter most.

We have migrated approximately 270,000 users to regionalized data processing while maintaining the same functionality Workspace has always offered, with no reported impact to end-users, so we are able to be more productive and innovative.

Strong governance, clear ethics and active risk management are the bridge between AI's promise and real, trustworthy services in Papua New Guinea's financial sector: boards must own AI strategy, not delegate it, and institutions should build living AI governance frameworks that require transparency, human oversight, cross‑functional review and continuous staff training so models don't silently bake in bias or privacy harms (ungoverned AI “can raise data privacy issues, intellectual property concerns, and the potential for biased outputs that send wrong messages and land the business in a reputational nightmare” - a practical warning from local guidance on generative AI); pairing that guidance with the government's upcoming National AI Adoption Framework and advances like SevisPass means PNG banks and insurers can align ethical controls with national infrastructure and reduce friction for identity‑based services (IDAPNG guidance: How to govern generative AI in Papua New Guinea, PNG ICT Ministry: National AI Adoption Framework and Digital ID announcement).

Prioritise an AI inventory, role‑based accountability, approved‑project gates, regular audits and literacy programmes so pilots scale without creating surprise liabilities or community backlash; this is governance as competitive advantage, not mere compliance.

“And compliance officers should take note. When our prosecutors assess a company's compliance program - as they do in all corporate resolutions - they consider how well the program mitigates the company's most significant risks. And for a growing number of businesses, that now includes the risk of misusing AI. That's why, going forward and wherever applicable, our prosecutors will assess a company's ability to manage AI-related risks as part of its overall compliance efforts.”

Start small, build to scale: Papua New Guinea financial institutions should sequence AI adoption around clear, locally‑anchored milestones - first, map an AI inventory and data flows tied to the soon‑to‑be published National AI Adoption Framework and the SevisPass rollout so identity, consent and logging are solved before models touch customer accounts (DICT National AI Adoption Framework and SevisPass announcement); next, prioritise high‑impact pilots that reinforce financial integrity (thin‑file credit scoring and intelligent document processing) while shoring up AML and transaction monitoring to reduce FATF‑related exposure and meet regulator expectations (PNG plans for AML, AI and safer digital transactions).

Leverage the Bank of Papua New Guinea's CBDC proof‑of‑concept learnings - QR‑pay and fund‑recovery tests show how payments pilots can be staged in controlled groups before broader rollout - and pair those pilots with hybrid data architectures and local key management to keep sensitive KYC under practical national control (Papua New Guinea digital kina CBDC proof-of-concept).

Make funding realistic (note SevisPass faced a K7m shortfall), assign board‑level accountability, embed human‑in‑the‑loop gates for high‑risk uses, and commit to skills pipelines so a village shopkeeper can go from QR‑pay acceptance to a verified bank account without friction - small, governed steps that turn national digital infrastructure into measurable inclusion and resilience.

“SevisPass will serve as a Digital Public Infrastructure, enabling secure authentication across banking, telecommunications, and government systems,” said Minister Masiu.

Choosing AI vendors in Papua New Guinea is about matching capability to context: start by defining the use case and total cost of ownership, then shortlist platforms that score well on lifecycle tooling, MLOps/LLMOps and governance (ISG's AI Platforms Buyers Guide names Oracle, AWS and IBM among the Product Experience leaders and highlights the need for end‑to‑end lifecycle tooling); demand clear documentation from any GPAI provider because the EU AI Act rules require technical records of model training, testing and evaluation for integrated GPAIs, which can affect PNG suppliers that export services to the EU (ISG AI Platforms Buyers Guide, Norton Rose Fulbright AI regulation overview).

Use procurement best practices - weighted scoring models, TCO analysis, supplier segmentation and risk‑based evaluation - and favour vendors with strong audit trails, hybrid deployment options and proven tooling for fraud, AML and document processing; practical templates and checklists can speed decisions and reduce vendor lock‑in (GEP guide to procurement evaluation with AI).

The right shortlist lets PNG banks and fintechs pair local digital public infrastructure (Digital ID, e‑portals) with responsible AI platforms that keep sensitive KYC and thin‑file scoring usable at the village level - so a phone top‑up pattern can become a reliable lending signal, not a regulatory headache.

“When someone submits an application, it automatically uses all the supporting documents... What used to take days or weeks happens now in minutes.” - James Inglis, NiuPay (on ICSA's AI visa platform)

People and change management will determine whether PNG's AI investments actually widen access or simply automate the same exclusion: with just over 10 million people, roughly 80% still unbanked and 87% living in rural areas, closing the digital and financial‑literacy gap is urgent (PNG national strategy for financial inclusion and the gender gap).

Practical steps matter - province‑level partnership models and the Centre for Excellence in Financial Inclusion's Provincial Government Engagement Program show how advocacy, agent onboarding and local training can be sequenced to reach districts that national programmes miss (Provincial engagement model to scale PNG NFIS financial inclusion goals).

Invest in targeted reskilling (fraud analysts, field agents, branch staff and underwriters), blended digital literacy for customers, and talent pipelines that prioritise women - PNG has the largest regional gender gap, with women 29% less likely to access formal services - and youth in the informal economy.

Pair classroom and bootcamp learning with on‑the‑job mentoring and simple KPIs so pilots convert to durable change: when a village shopkeeper can go from QR‑pay acceptance to a verified bank account, that's the moment skills, trust and systems all click (Thin-file credit scoring and practical AI use cases for PNG financial services).

Practical next steps for Papua New Guinea financial services in 2025 start with aligning pilots to the soon‑to‑be finalised National AI Adoption Framework and the SevisPass digital ID rollout so identity, consent and logging are solved before models touch customer money - the ICT Ministry warns SevisPass can unlock millions of new customers but has faced a K7m shortfall, so funding and phased delivery matter (PNG DICT National AI Adoption Framework and SevisPass announcement); second, favour locally‑anchored pilots that prove value in controlled cohorts - NiuPay's cloud platform now issues visa decisions in minutes and can handle up to 1,000 applications daily, a reminder that practical pilots can scale fast when paired with local control and skills-building (NiuPay AI visa processing and land tax case study (APNGBC)); third, invest in workforce readiness and simple governance: technical literacy for frontline staff, role‑based oversight, human‑in‑the‑loop gates for high‑risk flows and prompt‑writing skills that reduce vendor dependency - training such as the 15‑week AI Essentials for Work bootcamp helps staff apply AI safely across business functions (Nucamp AI Essentials for Work bootcamp (15-week AI training)).

Pair these steps with hybrid data architectures, local key management and clear procurement checklists so thin‑file credit scoring, intelligent document processing and AML monitoring deliver inclusion without regulatory surprise; sequence measurable KPIs, board accountability and staged funding so each pilot converts into durable access and resilience for urban and rural Papua New Guineans alike.

“SevisPass will serve as a Digital Public Infrastructure, enabling secure authentication across banking, telecommunications, and government systems.”