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MKTplace-region: APAC

Claire Hartley - APAC Compliance Challenge

APAC’s New Compliance Challenge

APAC’s New Compliance Challenge:
Managing Innovation Across a Fragmented Regulatory Region

Asia-Pacific is entering a more demanding phase of compliance, regulatory and data protection oversight.

The challenge is no longer simply keeping pace with new legislation. Organisations must now manage an increasingly complex combination of national privacy laws, data localisation requirements, international transfer restrictions, artificial intelligence governance and sector-specific regulation.

Unlike the European Union, APAC does not operate under one harmonised regulatory framework. Each jurisdiction has its own legal requirements, regulatory priorities and enforcement approach. A control that is appropriate in Singapore may not be sufficient in Australia, China, India, Indonesia or New Zealand.

This fragmentation creates a significant challenge for organisations operating across multiple APAC markets.

AI and Automated Decision-Making

Artificial intelligence is becoming inseparable from data protection and regulatory compliance.

In July 2026, Singapore’s Personal Data Protection Commission published guidance addressing how personal data should be collected and used throughout the generative AI lifecycle. This includes web scraping, reusing existing customer data, allocating responsibilities between AI providers and users, managing retention and responding to individual rights requests.

Australia is also introducing new transparency requirements for automated decision-making. From 10 December 2026, regulated organisations will need to disclose certain uses of personal information in computer-generated decisions that could significantly affect an individual’s rights or interests.

For financial services organisations, these developments create particular challenges. AI and automated models may influence credit, fraud, identity, affordability and customer-management decisions. Organisations must therefore be able to explain not only what a model does, but also how data is selected, how risk is assessed and where human oversight is applied.

Biometrics and Identity Data

Biometric information is also receiving greater regulatory attention.

New Zealand’s Biometric Processing Privacy Code introduces specific rules governing the collection and use of facial, fingerprint, voice, behavioural and other biometric information. Organisations already using biometric processing must transition to the new requirements by 3 August 2026.

This reflects a wider regional trend. Identity verification and fraud-prevention technology can deliver substantial benefits, but regulators increasingly expect organisations to demonstrate necessity, proportionality, transparency, security and appropriate retention.

Data Transfers and Local Requirements

Cross-border data transfers remain another major area of complexity.

APAC organisations frequently rely on global cloud infrastructure, regional service providers and international data sources. However, the legal mechanisms for transferring personal data differ significantly between jurisdictions.

Some countries require contractual safeguards. Others may require regulatory assessments, certifications, local storage or additional controls for particular categories of information. Organisations therefore cannot rely on one global transfer mechanism without first assessing the law, data and processing activity in each relevant market.

The practical challenge is knowing where information is located, how it moves, which providers can access it and which organisation is accountable at every stage.

How Provenir Meets These Demands

Provenir addresses these challenges through a global Compliance, Regulatory and Data Protection, or CRDP, framework that combines central governance with jurisdiction-specific regulatory analysis.

CRDP provides independent oversight and challenge across Provenir’s platform and works alongside Information Security, Product, Engineering, Technology and Legal. This enables regulatory requirements to be considered throughout product development, contracting, implementation and ongoing customer support rather than only at the final compliance review stage.

Provenir’s approach includes:

  • – privacy and data protection assessments for new technologies, products and processing activities;
  • – governance of international transfers, subprocessors and regional data flows;
  • – due diligence and risk assessment for vendors and data providers;
  • – defined incident identification, escalation, investigation and notification processes;
  • – data-minimisation, retention and access-control requirements;
  • – regulatory monitoring across the countries in which Provenir and its customers operate;
  • – documented accountability for AI, model governance and automated decision-making.

Provenir also uses a structured lines-of-defence model. Operational teams own and manage their controls, CRDP provides policy, advice, monitoring and challenge, and independent certification and assurance activity provides further scrutiny.

Privacy governance is embedded within how Provenir designs, deploys and operates its platform. This includes clear controller and processor role allocation, data-processing agreements, privacy impact assessments, international transfer safeguards, subprocessor oversight and breach-management procedures.

AI governance is similarly integrated across CRDP, Product, Engineering and Information Security. Provenir’s framework addresses purpose, accountability, data governance, fairness, transparency, human oversight, security and continuing monitoring, with reference to ISO/IEC 42001 and emerging regulatory requirements.

The platform itself supports this governance approach by bringing data, models and decisioning into a controlled environment. This gives customers greater visibility over decision strategies, testing, deployment and performance, while allowing them to apply their own regulatory policies and risk controls.

From Compliance Obligation to Market Confidence

The most successful organisations in APAC will not be those that attempt to apply one policy everywhere.

They will be those that establish consistent global governance while retaining the flexibility to respond to local laws, regulatory expectations and customer requirements.

For Provenir, strong CRDP governance is not separate from innovation or commercial growth. It provides the structure required to deploy data and AI responsibly, support customers across different regulatory environments and enter new markets with greater confidence.

In a region defined by rapid technological development and regulatory diversity, this ability to combine innovation with demonstrable control is becoming a significant competitive advantage.

Claire Hartley, Chief Compliance Officer Group DPO, Provenir

Claire Hartley

Written By

Chief Compliance Officer Group DPO, Provenir

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AI governance in financial services

AI governance in financial services:
What “governed” means in practice

Artificial intelligence has reached the point where its presence is assumed. Every software platform is adding AI capabilities, established technology providers are layering intelligence onto products that have existed for decades, and entirely new companies are emerging with AI at their core. For financial institutions, however, this technological acceleration creates a different conversation. The question is no longer whether AI can be adopted; it is whether it can be adopted without losing control of the decisions that matter most.

Banking has always been built on trust. Every lending decision, fraud investigation, affordability assessment or customer interaction carries consequences that extend far beyond technology. They affect customers’ financial lives, an institution’s reputation, and confidence in the financial system itself. AI undoubtedly has the potential to improve these outcomes, but the same capability that creates value can just as easily amplify poor decisions if it is introduced without the discipline to govern it properly.

What Lies Beneath the Surface

At Provenir, we often think of AI adoption as an iceberg. Above the surface sit the capabilities that attract the headlines: automation, personalisation, natural language interfaces and dramatically faster decision-making. Beneath the surface lies the work that determines whether those capabilities create sustainable value or introduce new operational risks: bias that only emerges at scale, model drift, over-reliance on AI recommendations, and systems that perform well in pilots but struggle under the complexity of production. These are rarely failures of AI itself; they are failures of governance.

For Tier 1 financial institutions, this matters because they are not trying to become AI companies. They have spent decades building resilient decisioning infrastructure capable of supporting millions of customers under demanding regulatory standards. Their challenge is to strengthen that foundation with AI, not replace it. The objective is to improve customer outcomes without compromising the control, resilience and accountability that already exist.

Regulation as an Enabler

Regulation, including the EU AI Act, can be an enabler rather than a barrier. Good regulation defines the conditions under which innovation can scale responsibly. By requiring traceability, human oversight, documentation and accountability, it gives organisations a framework for deploying AI safely in the processes that matter most.

Ultimately, the conversation should never begin with the technology. It should begin with the outcome. Customers never experience a large language model. They experience whether a loan was approved fairly, whether fraud was detected quickly, whether a complaint was handled appropriately, or whether they were treated with empathy during financial difficulty. The technology only has value if those customer outcomes improve.

What “Governed AI” Really Means

Governance is not simply connecting an LLM into a workflow. It encompasses everything that surrounds it: how the solution was designed, how it was tested, how fairness was assessed, how performance is monitored, how drift is detected, whether every decision can be traced, explained and audited, and how long the supporting evidence is retained. These are the questions that compliance officers, auditors and regulators increasingly need institutions to answer with confidence.

Perhaps the most important principle is that accountability never transfers to the technology. AI may generate recommendations and automate increasingly sophisticated tasks, but it is never accountable for the outcomes it produces. That responsibility always remains with people. As AI becomes more capable, human accountability becomes more important, not less.

Balancing Value and Risk

Every AI decision is ultimately a balance between value and risk. Governance is what allows organisations to shift that balance, reducing risk while increasing the value that AI can safely deliver. It provides the confidence to introduce AI where it creates meaningful improvements while recognising that, in some situations, a more traditional approach may still be the better choice.

This is why it’s important to think of governance as a conscious series of design choices rather than a checklist of controls. There are situations where a human should remain directly involved in every decision, such as managing vulnerable customers, because judgement and accountability extend beyond what AI should provide independently. There are others, such as transaction monitoring, where automation can safely operate at scale, provided robust monitoring, alerting and escalation mechanisms remain in place.

The objective should never be to maximise automation for its own sake. It should be to maximise customer outcomes while managing risk appropriately. In some situations, AI should support a person in making the final decision, keeping a human firmly in the loop. In others, it can safely automate routine decisions, provided robust monitoring and governance remain in place. And there will always be situations where AI is not the right technology at all. The decision should always reflect the balance between value, risk and the controls available to manage that risk.

The Next Stage of AI Literacy

In many respects, this is simply the next stage of a journey that financial services has travelled before. Banks first developed risk literacy, then data literacy, and now AI literacy. The institutions that succeed will not necessarily be those deploying the most AI, but those that understand where it creates genuine value, where traditional approaches remain more appropriate, and how to combine both within a governance framework that customers, regulators and boards can trust.

Mike Holmes

Mike Holmes

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Head of Data Science, Provenir

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Enabling Responsible Growth and Customer Trust in Malaysia’s Digital Lending Landscape through Data-Driven Insights

ProvenirNext Roundtable

Enabling Responsible Growth and Customer Trust in Malaysia’s Digital Lending Landscape through Data-Driven Insights

Bringing together visionary minds from the financial sector to spark dialogue and innovation

  • 06th Aug 2025
  • 4:30 PM – 7:30 PM MYT
  • Sofitel Kuala Lumpur Damansara | Malaysia

As digital transformation accelerates across Southeast Asia, Malaysia stands at a pivotal point driven by innovation, increasing regulatory expectations, and evolving customer demands.

This executive roundtable will convene thought leaders from banks, fintechs, digital lenders, and regulators to explore how financial institutions can meet growing expectations for responsible lending, deliver exceptional digital experiences, and build enduring customer trust.

We’ll dive into how intelligent decisioning, low-code orchestration, and real-time data are transforming onboarding and credit decisioning, empowering institutions to drive sustainable growth, reduce risk exposure, and enhance the overall customer lifecycle—from acquisition to long-term retention.

We’re also hosting a special fireside chat featuring Kavines Karthigasan, Head of APAC, Provenir and Shawn Loh, Director, LifeTech Group. They’ll dive into how data security, collaboration, and customer experience intersect to drive trust at scale in modern finance.

Discussion Highlights

  • Responsible Lending in a Regulated Landscape How Malaysian lenders can align with BNM’s expectations using real-time affordability insights and intelligent decisioning.
  • From Acquisition to Customer Lifetime ValueStrategies to foster long-term loyalty and profitability through lifecycle decisioning.
  • Trust Through TransparencyUsing consent-driven data and explainable decisioning to enhance digital trust.
  • Frictionless Digital Journeys – Delivering seamless onboarding and lending experiences with low-code, intelligent orchestration.
  • Malaysia’s Financial Outlook – Navigating fraud risks, credit growth, and rising consumer expectations in a dynamic digital lending environment.
Why Attend?
  • Learn from local and regional success stories

  • Engage in off-the-record dialogue with industry peers
  • Grow your professional network over a curated three-course meal
  • Walk away with fresh insights to drive trust and customer growth

Who Should Attend?

This session is curated for senior executives and decision-makers across:

  • Retail & Digital Banking
  • Risk, Fraud & Compliance
  • Customer Experience & Product Strategy
  • Data Science & Decisioning
  • Fintech, BNPL & Lending Platforms

ProvenirNEXT

Register your interest here

  • Kavinesswaran Karthigasan

    Head of APAC, Provenir

    Kavines is the Principal Consultant driving business-value propositions for Provenir across the APAC Region. With a decade of experience in credit risk management solutions in the financial services sector, Kavines has worked with major banks and lenders in Southeast Asia, assisting them in optimising their business processes, automating their workflows, and complying with regulatory standards through the use of risk and decision management applications. Prior to joining Provenir, he spent nearly a decade as a key member of Experian’s Southeast Asia Decision Analytics group. Throughout his career, he has held a variety of positions, beginning as an implementation analyst, progressing to consultant, then pre-sales consultant, and finally customer success manager for the Southeast Asia region. He was born in Malaysia and graduated from Monash Australia. Kavines’ aim is to help his clients achieve customer-level consistency, digital transformation, and data-driven decision making across their products and channels.

  • Shawn Loh

    Director of Business Development, LifeTech Group

    Shawn leads the business division at LifeTech Group, a leading provider of Managed Security Services delivering next generation AI-Powered Security Operations Centre (SOC) solutions and global cybersecurity services. With years of expertise in cybersecurity and fintech, Shawn plays an instrumental role in supporting national critical industries – such as financial, healthcare, utilities and manufacturing – enhance their digital resilience, adopt proactive security operations, and strengthen their brand reputation. His mission is to ensure that organizations are safeguarded with precision and agility, so that they can focus on what matters most – growing their business.

The Provenir Thought Leadership Roundtable Series brings together industry visionaries, C-level executives, and thought leaders for insightful discussions on redefining risk decisioning strategies. The series fosters a collaborative environment for sharing forward-thinking perspectives, exploring innovative approaches, and shaping the future of fraud prevention in an era of rapid technological evolution and increasing digital risk.

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Simplify Compliance, Amplify Success

AdviceRobo mission is to responsibly promote financial inclusion for the next generation, globally. Younger generations possess unique attributes such as digital fluency, strong entrepreneurial spirit, and a commitment to sustainability, often overlooked by traditional financial methods. To bridge this gap, we’ve crafted an advanced data collection tool tailored for next-gen traits and behaviors.

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