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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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Beyond Detection: Closing the Fraud Prevention Gap

Beyond Detection:
Closing the Fraud Prevention Gap

Financial services providers have more data, more models, and more fraud technology than at any point in the industry’s history. Fraud losses are still climbing. In 2024, consumers reported losing $12.5 billion to fraud, a 25% jump from the year before, according to the FTC’s Consumer Sentinel Network Data Book. Imposter scams alone accounted for over 847,000 reports.

It’s worth asking why more technology alone hasn’t closed the gap.

Fragmentation, not detection, is the real problem

Most fraud programs aren’t short on tools. They’re running point solutions for identity verification, device risk, watchlist screening, and behavioral analytics, each bolted onto the last, each with its own data feed and its own blind spots. Teams end up reacting to threats one system at a time instead of seeing the full picture in real time, while fraudsters move across channels and products, testing whatever gap opens first. A fragmented defense is always a step behind an adaptive attacker.

The biggest red flag: data that isn’t fit for purpose

The teams making real progress on fraud have a narrower data footprint, not a bigger one. What they have is matched precisely to the decisioning problem in front of them, and it plugs in without a six-month integration project.

The pattern shows up constantly: companies without a strong data foundation try to stitch one together after the fact, pulling in sources that weren’t built to work together and weren’t vetted for the decision at hand. The result creates the appearance of coverage while functioning as noise.

Fraud prevention runs on the same principle as any other decision: the data matters more than the volume of data collected. Access to the right source, at the right moment, beats access to more sources.

What a governed decision layer changes

Provenir’s Application Fraud solution brings identity signals, device intelligence, behavioral data, and watchlists into a single, governed decisioning environment instead of a stack of disconnected tools.

  • Behavioral and identity AI detects synthetic identity, first-party fraud, and emerging patterns through intelligent profiling instead of static rules alone.
  • Real-time threat blocking scores and stops threats as they happen, not after a batch review.
  • Smarter case management prioritizes queues so investigators spend time on the cases that matter, not chasing false positives.
  • A governed data marketplace gives fraud teams on-demand access to 120+ data partners across identity, device, and credit signals, with pre-built integrations instead of a new project every time a new source is needed.

The result is a single, governed view of risk that gets sharper with every decision it processes.

Proof in practice

MTN put this to work and stopped 135% more high-risk transactions, while increasing pre-approvals by 130%. Fraud control and growth moved together because the data and the decisioning were finally working from the same source of truth.

Own the decision layer

Fraud won’t stop evolving, and the providers who win will be the ones whose decisioning is governed, connected, and built to adapt as fast as the threats do.

Book a meeting to see Provenir’s fraud decisioning in action

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Senior Content Manager, Provenir

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PowerSports Finance Summit 2026

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Join Provenir at the PowerSports Finance Summit in Atlanta
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We’re excited to sponsor the PowerSports Finance Summit this September in Atlanta, and we invite you to stop by Booth #1 to meet the team.

At Provenir, we help lenders make smarter, faster decisions across the customer lifecycle with AI-powered risk decisioning. From credit underwriting and fraud prevention to customer acquisition and portfolio management, our Decision Intelligence Platform enables organizations to automate decisions, optimize risk strategies, and deliver seamless customer experiences.

Whether you’re focused on improving application approvals, streamlining dealer financing, reducing fraud losses, or adapting to changing credit conditions, we’d love to show you how Provenir can help.

See you in Atlanta!

Book a Meeting with Our Experts

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At Provenir, we’re helping financial institutions stay ahead of evolving threats with AI-powered decisioning across fraud, identity, and risk. Book a meeting with us onsite to discover how our Decision Intelligence Platform enables financial institutions and organizations to make faster, smarter decisions throughout the entire customer lifecycle.

Whether you’re focused on streamlining onboarding, managing risk more effectively, or creating personalized customer experiences, let’s talk about what’s possible when you combine intelligence with execution.

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Beyond Data: Why Decisioning Needs Document Intelligence

Beyond Data:
Why Decisioning Needs Document Intelligence

Financial services providers now have access to more data than at any point in the industry’s history. Identity verification, fraud intelligence, credit data, sanctions screening, behavioral analytics and open banking have all become standard components of a modern decisioning strategy. Platforms like Provenir have made it possible to bring these sources together into a single, governed decisioning environment, replacing fragmented tools with consistent, auditable execution across the customer lifecycle.

One challenge has remained constant through all of this progress: many of the decisions that matter most still depend on documents.

Onboarding a new customer, verifying affordability, assessing source of funds, satisfying a regulatory obligation — each of these still relies on bank statements, payslips, proof of address, tax records, company filings and dozens of other forms of evidence. These documents carry essential information, and they remain one of the hardest sources of data to validate accurately at scale.

That gap points to a broader shift underway across the industry. Effective decisioning is no longer only a question of what data an institution can access. It is a question of how much confidence that institution can place in the evidence behind every decision.

Structured data provides insight. Documents provide evidence.

Modern decisioning platforms are built to aggregate structured information well. They retrieve identity records, calculate risk scores, assess affordability and combine multiple data providers into a single workflow, giving institutions the orchestration needed to automate complex decisions while keeping governance intact.

Documents present a different kind of problem. They vary in format, quality and completeness, and much of what they contain cannot simply be extracted into predefined fields. Determining whether a document is internally consistent, whether it aligns with other evidence the customer has provided, whether it shows signs of alteration, and whether external public records support what it claims all requires interpretation, not extraction. That interpretation is where document intelligence becomes necessary.

The missing layer in decisioning

Document intelligence is not a replacement for decision orchestration. It strengthens it, by improving the quality of the evidence entering the decision-making process in the first place. Rather than simply pulling data out of a document, AI-powered document processing analyzes its content, flags inconsistencies, detects signs of manipulation and validates the information against trusted external sources.

This shifts the work from document collection to document understanding, giving institutions a more complete view of customer identity and risk. The outcome is stronger fraud detection, tighter compliance and greater confidence in automated decisions, without adding friction for genuine customers.

Why complementary technologies matter

As institutions modernize their technology stacks, the strongest strategies are built on connected ecosystems rather than standalone tools. A governed decisioning environment provides the framework for bringing multiple sources of intelligence together into one consistent workflow. Specialist technologies then contribute depth in their own domain, whether that domain is identity verification, fraud prevention, credit intelligence or document analysis.

This is what allows institutions to adopt best-in-class capabilities in each area without giving up flexibility or control. Instead of asking a single technology to solve every problem, an institution can assemble an ecosystem in which each solution adds distinct value while reinforcing the overall decision.

Bringing document intelligence into the Provenir ecosystem

The addition of eyeDP to the Provenir Data Marketplace is a direct example of this approach in practice. Through the Marketplace, institutions can now access eyeDP’s document intelligence capabilities directly within their existing Provenir workflows, putting document verification alongside identity, fraud and credit data as another governed input into the decision.

eyeDP analyzes documents across common formats, including PDFs, JPEGs and screenshots. Beyond extraction, it identifies discrepancies, flags potential fraud and cross-references document content against trusted public data sources, giving institutions greater confidence in the evidence behind every customer decision.

For institutions operating in regulated environments, the practical impact is direct: faster and more consistent onboarding, earlier identification of fraud risk, stronger compliance processes and less manual document review, all without giving up oversight.

The evidence behind every decision

The next stage of digital decisioning will not be defined by a single technology. It will be shaped by connected ecosystems in which specialist capabilities work together to improve both efficiency and confidence.

Decision orchestration provides the structure. Data providers contribute insight. Document intelligence strengthens the evidence underneath the decision itself. Together, they move institutions beyond simply automating decisions and toward decisions they can stand behind.

As the demands placed on regulated businesses continue to grow, confidence will matter as much as speed. Combining governed decision orchestration with document intelligence gives institutions a stronger foundation for every decision that follows, and a clearer path to reducing fraud, strengthening compliance and delivering the kind of customer experience that regulated growth depends on.

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Provenir Experts Answer Frequently Asked Questions

1. Regarding the 1st party fraud problem, there are fraud losses in the bad debt P&L, usage is monitored by the FMS, applications are handled by Credit risk, and nobody generally owns first-party fraud. Concretely, what type of solution can fix an organizational problem? Software doesn’t change reporting lines…
it is right that software alone doesn’t fix org charts, but the silo persists because fraud and credit run on separate systems with separate data. When application fraud scoring, credit decisioning, and intent-to-pay signals run in one orchestrated decision flow, you can finally tag an account as first-party fraud risk at origination and pass that flag downstream into collections strategy. The output, in addition to delivering a decline/approve, can be a shared risk label both teams act on. Several of our clients started exactly there: a joint fraud-credit decision workflow, before touching any reporting line.
2. By the time we detect a fraud, the handset is often already shipped. What new signal do you suggest to use, and at what latency?
The honest answer is: probably no single signal you don’t have yet. The gap is combining them in real time at the decision point : device fingerprint change, behavioral velocity, network signals via CAMARA/Open Gateway APIs… most operators have access to these, but they sit in different tools and never converge before the order is approved. We believe orchestrating those calls in a waterfall within the transaction, milliseconds, not batch, is the right approach.
3. We see fraud-as-a-service kits iterating daily, but my rule team takes up to 3 weeks to deploy a change through IT or requires a change request to our vendor. Every vendor says “AI fights AI”, but can you concretely explain how to better manage time-to-deploy for a new model or rule, and who needs to be in charge?
Fair challenge, and it’s the right metric to buy on. In our platform the fraud team owns the decision logic: low-code, so a rule change or a champion/challenger test is deployed by your analysts in hours, outside usual IT release cycles. On the model side, you can drop in a retrained model alongside the live one, route 10% of traffic, and compare before switching. The point isn’t that our AI is smarter than theirs, but very design of the solution makes the iteration loop become shorter, which is the only durable advantage.
4. We get blamed for fraud losses as well as for abandoned carts. Can you share real numbers that back the frictionless security headline? Referral rate, false positive reduction… and how do you also measure the revenue side?
We’d never quote a universal number, because is primarily depends on the baseline referral rate. What matters is the mechanism. A risk-based waterfall, where low-risk applicants pass on cheap passive checks; data costs and friction are only escalated for the ambiguous middle. That alone typically collapses the manual review queue, because most referrals today are caused by blunt, one-size-fits-all rules. And because the decisioning and the data are in one place, we build the conversion dashboard next to the fraud dashboard. And our simulation capability makes it easy to assess instantly the impacts on both conversion and loss. That dual KPI view is, frankly, how fraud managers can better defend their budget.
5. We’ve got a 15-year-old FMS that’s excellent at IRSF, but useless at onboarding, plus a credit engine, plus a KYC vendor, plus device intelligence. You’re proposing to be tool number five. That’s yet another integration project in our IT backlog. All that for just orchestration. Realistically, when could we expect decisions in Production?
Your 5th tool objection would be legitimate if we were another point solution. Although we explained how powerful our proprietary Fraud block is, our platform sits as the decision layer that calls your existing four vendors through pre-built integrations from our data marketplace, so the business case is consolidating the decision, not replacing the tools. Typical first use case: say, online subscription with device financing. It goes live in weeks because there’s no rip-and-replace: your FMS keeps doing IRSF, we handle the onboarding decision it was never designed for. And over time you get leverage on those four contracts, because you can swap a data provider in the waterfall without touching the journey.
6. What innovations are you seeing across the eco system to combat this?

The biggest innovation is what could be called Open Telco: GSMA Open Gateway and CAMARA APIs turning network intelligence into usable fraud signals. APIs such as SIM Swap, Number Verification, Device Swap, KYC Match or Number Recycling help detect account takeover, fake onboarding, mule activity and social-engineering risks in real time.

But these signals are not a standalone answer. They become powerful when combined with device intelligence, behavioural analytics, graph analysis, AI-assisted investigation and dynamic step-up controls. The real innovation is orchestration: using better ecosystem signals to make faster, proportionate and explainable fraud decisions.

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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

Written By

Head of Data Science, Provenir

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What’s Missing from Your Decision Stack? Smarter decisions start with the right data. Are you seeing the full picture?

Most organizations aren’t lacking data—they’re lacking the right data, at the right time, from the right sources. Relying on limited inputs creates costly blind spots: missed revenue from thin-file customers, increased fraud exposure, slower decisions, and delayed innovation.

The challenge isn’t volume. It’s access, integration, and flexibility.

Provenir’s Data Marketplace solves this by giving you instant access to a global ecosystem of 130+ trusted data providers—all through a single API and low-code integration. From identity and fraud signals to credit, alternative data, and real-time financial insights, you can quickly plug in, test, and scale the data sources that power better decisions across the entire customer lifecycle. The result?

Faster decisions. Lower risk. Higher approvals. Greater agility.

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The days of treating compliance, regulatory oversight and data protection as separate functions are coming to an end. 

Latin America is one of the most consequential growth regions in digital financial services right now. Banks, fintechs and lenders are using real-time data, alternative data and AI-enabled decisioning to expand credit access, strengthen fraud detection and make faster decisions at scale. 

But the compliance question has shifted. 

It is no longer sufficient to demonstrate that personal data was collected lawfully and protected securely. Organisations now need to explain how data influenced a decision, whether that data was reliable, and whether the outcome was fair, proportionate and properly governed. That is the transition underway across the region: from data protection compliance to decisioning accountability. 

The regulatory landscape makes this more complex. LATAM is not one market. Brazil, Mexico, Chile, Colombia and Peru are each developing their approaches to privacy, AI regulation, international data transfers and automated decision-making on different timelines and with different emphases.

A decisioning strategy developed for one country cannot be deployed unchanged across the region. Local legal requirements, regulatory expectations, data quality and market conditions all need to be reflected in how decisions are built, governed and explained.

This is where the architecture of a decisioning platform matters. 

Provenir provides a configurable decisioning environment that allows customers to maintain global consistency while adapting rules, workflows and strategies to the requirements of each market. Customers retain control over their legal grounds, data sources, risk policies and decisioning objectives, supported by visibility, access controls and auditability that regulators increasingly expect to see. 

Provenir’s broader compliance framework reflects this. ISO/IEC 27001 certification, contractual data protection controls, international transfer arrangements and subprocessor oversight are established elements. Provenir is also implementing an AI management framework aligned with ISO/IEC 42001 and emerging legal requirements across the markets we serve. That work strengthens governance around AI risk, accountability, transparency and lifecycle management — the areas where regulatory scrutiny is growing fastest. 

The opportunity in LATAM is real, but it is defined by more than speed. Organisations that can enter new markets, support financial inclusion and scale operations with demonstrably governed decisioning will be in a stronger position than those treating compliance as a retrospective concern. The next phase of LATAM regulation will test whether organisations can show that their data-driven decisions are lawful, secure, fair and explainable. That capability needs to be built into decisioning architecture from the start, not added to it later. 

Compliance is not a constraint on innovation in this region. It is the condition that makes sustained innovation possible. 

Claire Hartley, Chief Compliance Officer Group DPO, Provenir

Claire Hartley

Written By

Chief Compliance Officer Group DPO, Provenir


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