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Industry: Data

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

Amy

Amy Sariego

Written By

Senior Content Manager, Provenir

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Datos Financial Crime and Cybersecurity Forum

Datos Financial Crime and Cybersecurity Forum

Join Provenir at the Datos Financial Crime and Cybersecurity Forum in Charlotte
  • September 15-16
  • Charlotte, NC
  • Booth #24

We’re excited to sponsor the Datos Financial Crime and Cybersecurity Forum this September in Charlotte, and we invite you to stop by Booth #24 to meet the team.

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.

See you in Charlotte!

Attend our Breakout Session

“When Seeing Isn’t Believing: How to Combat Deepfake Driven Fraud”

jason abbott headshot

Jason Abbott

Product Director, Fraud

Provenir
Book a Meeting with Our Experts

Reserve dedicated 1:1 time with the Provenir team to take a test drive of our platform and explore how we can support your specific initiatives.

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Jason Abbott

Product Director, Fraud, Provenir

Renee Fritton

Renee Fritton

Senior Sales Executive, Provenir

Sam Rohde

Sam Rohde

VP Solutions Consulting, Provenir

Meet with us onsite!

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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.

eyeDP provides advanced AI-powered document intelligence to help organisations reduce fraud risk, improve data integrity, and accelerate onboarding.

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JessWhitehouse

Jess Whitehouse

Written By

Director of Customer Operations, eyeDP

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Practical Guide to Reviewing Your Data Providers

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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.

The question isn’t whether you need more data. It’s whether your current decision stack is holding you back.

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Latam Compliance Challenge for Decisioning

LATAM Next Compliance Challenge: Decisioning Accountability

From Data Protection to Decisioning Accountability:
What LATAM Regulation Means for Enterprise Lenders

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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What Does a Good Data Provider Review Actually Look Like?

What Does a Good Data Provider Review Actually Look Like?

Most financial services providers know they should review their third-party data providers more often. Fewer know what a good review actually involves. Without a clear framework, it tends to collapse into one of two things: a commercial negotiation exercise (price renegotiation dressed up as strategic review), or a sprawling project that stalls before producing any real change.

This article sets out what a meaningful data provider review looks like in practice: who needs to be involved, what to measure, and how to turn findings into decisions.

Who Needs to Be Involved

A data review is not a procurement exercise. It touches risk strategy, compliance, technology, and customer experience, and the stakeholder group should reflect that. The right team typically spans risk and analytics (to assess predictive performance and model impact), compliance and legal (to review regulatory obligations and contractual terms), technology and engineering (to evaluate integration performance and flexibility), product and operations (to surface friction points in the customer journey), and procurement (to manage commercial outcomes once the strategic decisions are already made).

Getting these stakeholders aligned on objectives before the review starts saves significant time later. A review driven by a single function tends to optimize for that function’s priorities at the expense of the others.

What to Measure

The starting question for any performance review is: what is each data source actually contributing to decisions?

  • Predictive contribution

    Does the dataset improve model performance? What is the measured uplift in fraud detection, credit risk separation, or identity confidence when this data is present versus absent? If uplift can’t be demonstrated, the dataset warrants a challenge.
  • Decision impact

    How many decisions does this data influence per month? Is it in a critical path or a fallback? Some providers carry significant volume but marginal incremental value — a trap that’s easy to miss when reviewing providers in isolation.
  • Coverage and freshness

    What is the hit rate across your application population? Is coverage consistent across geographies, customer segments, and channels? Stale or patchy data creates silent failure modes: decisions that appear normal but are running on degraded inputs.
  • Integration performance

    What is the API response time, and how does it affect overall decision latency? What is the uptime record? Are there constraints that limit your ability to test, orchestrate, or swap out providers quickly?
  • Cost-per-decision

    What is the fully loaded cost of this provider, including integration and maintenance overhead, relative to the decisions it influences and the value it delivers?

A Practical Scoring Framework

A scoring matrix across these dimensions — predictive contribution, coverage, integration performance, cost efficiency, and strategic fit — makes comparison possible across providers and surfaces rationalization opportunities clearly.

Weight each dimension according to your organization’s current priorities. For those under margin pressure, cost-per-decision becomes a stronger forcing function. Score each provider, aggregate, and plot against contract renewal dates. That becomes your prioritized action plan.

For institutions running Provenir’s Data Marketplace, testing new providers can happen through the library of pre-built API connections without committing your own engineering resource to integration first — compressing the evaluation phase significantly.

What Good Looks Like at the End

A completed review should produce three things: a rationalized provider set with a clear rationale for each retained provider, a plan for exiting or renegotiating underperformers, and any overlapping providers consolidated; a tested shortlist of new providers, validated against your own data rather than vendor benchmarks; and an integration roadmap, with any legacy connections flagged for modernization and a timeline for changes.

The goal is a data stack that performs better than it did at the start of the review, and a process you can run again in 12 months without it becoming a major project.

Matthew Nutt

Matthew Nutt

Written By

Senior Product Manager, Provenir

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When Did You Review Your Third-Party Data Providers?

When Did You Last Review Your Third-Party Data Providers?

When Did You Last Review
Your Third-Party Data Providers?

Third-party data sits at the heart of financial services decisioning. Institutions rely on it to manage fraud, verify identity, meet compliance obligations, and price risk accurately. Yet despite its strategic importance, many organisations treat their data providers as fixed infrastructure, reviewed on contract renewal cycles rather than against current performance. 

That gap has consequences. Fraud patterns change continuously. Regulatory requirements evolve. Consumer behaviour shifts. And the data ecosystem itself keeps expanding, with new providers, richer signals, and alternative datasets entering the market. An unrevisited data stack is almost certainly leaving performance on the table. 

The Hidden Cost of Standing Still

Without regular review, data portfolios tend to accumulate inefficiency. Overlapping providers go unchallenged. Newer, higher-performing signals go untested. Models optimised for last year’s risk environment carry on running. Customer friction creeps up as legacy integrations slow decisioning down. 

A periodic review is a performance lever, and often a significant one. 

How to Review Existing Providers

A meaningful review goes beyond commercial renegotiation. It starts with measurable value and decision impact. 

Start by asking whether the data is still predictive. Look at how each dataset contributes to outcomes: fraud detection uplift, approval rates, false positive reduction, customer journey friction. If a dataset isn’t materially improving decisioning, it warrants a challenge. 

Then look for duplication. It’s common to see multiple providers offering similar signals — identity verification, device intelligence, email risk. Mapping providers against capability areas (identity, fraud signals, credit risk, AML/KYC) makes the overlap visible and the rationalisation case clear. 

Finally, assess whether integrations are still fit for purpose. Legacy connections can become bottlenecks in API performance, orchestration flexibility, and the ability to test new configurations quickly. Modern decisioning requires agility. Integrations that constrain iteration are a liability. 

This is where Provenir’s Data Marketplace changes the calculus. With 225+ pre-integrated global data sources across credit, fraud, identity, and compliance, connected via a single API, teams can consolidate, swap, or extend their data stack without the integration overhead that typically makes these decisions slow and expensive. 

How to Evaluate New Data Partners

Exploring new providers shouldn’t be resource-heavy. The most effective organisations treat it as an ongoing test-and-learn process rather than a formal procurement exercise. 

The starting point is always the use case: what problem are you solving? Reducing first-party fraud, improving thin-file approvals, strengthening identity confidence, enhancing AML screening — a clear use case sharpens evaluation criteria and prevents capability drift. 

From there, the best way to assess a new provider is through real data and measurable outcomes. Run parallel testing alongside existing providers where possible. Use historical and live traffic. Measure incremental uplift, not just standalone performance. And track both risk and customer experience metrics. A provider that reduces fraud while increasing friction may not represent a net gain. 

Look beyond the data itself, too. The strongest partners bring transparency in how signals are generated, consistent coverage across your key markets, and a clear roadmap for how their signals will evolve. 

Provenir Marketplace is built around this test-and-learn model. Pre-built integrations mean new providers can be connected and running in your decisioning workflows in days, with sandbox simulation available before any change goes live. 

How to Know Whether You’re Collecting the Right Data

More data isn’t the goal. The right data, aligned to specific decision points, is. 

Every dataset should serve a clear purpose in your decisioning workflow: onboarding, authentication, fraud prevention, customer management, collections. If you can’t map a data source to a decision outcome, it’s worth questioning whether it belongs in the stack. 

Marginal value analysis makes this concrete. What happens if you remove a dataset? What uplift does it deliver against alternatives? This kind of scrutiny helps prioritise spend and reduce noise. 

The right data also balances risk and experience. Better data should enable smarter decisions: higher approval rates, lower drop-off, faster time to decision, without simply adding weight to the process. 

And the right mix changes over time. Fraud patterns shift. New sources emerge. Business strategy evolves. Leading organisations treat their data ecosystem as a living system, revisiting it continuously rather than managing it on a fixed cycle. 

Building a Smarter Data Strategy

The question isn’t whether your current data providers are good enough in isolation. It’s whether they represent the best available fit for your current risk landscape, your customer experience goals, and your decisioning strategy. 

For most organisations, an honest review surfaces both savings and performance improvements. The barrier has historically been the integration overhead required to make changes, which is exactly the problem Provenir’s Data Marketplace is designed to solve. 

Matthew Nutt

Matthew Nutt

Written By

Senior Product Manager, Provenir

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Beyond Traditional Credit Scores

Beyond Traditional Credit Scores:
How Alternative Data is Revolutionizing Financial Inclusion

In financial services, the question isn’t whether you can lend responsibly, but whether you can identify creditworthy customers that traditional methods miss entirely. For millions of potential borrowers worldwide, thin credit files or complete absence from traditional credit bureaus creates an insurmountable barrier to financial services. AI-powered alternative data underwriting is changing that reality, one data point at a time.

The Hidden Market of the Credit Invisible

Nearly 26 million Americans are “credit invisible”, they have no credit history with nationwide credit reporting agencies. Globally, that number swells to over 1.7 billion adults who remain unbanked or underbanked. These aren’t necessarily high-risk borrowers; they’re simply invisible to traditional scoring methods that rely heavily on credit bureau data.

This represents both a massive untapped market and a profound opportunity for financial inclusion. The challenge lies in assessing creditworthiness without traditional markers and this is precisely where alternative data shines.

The AI Advantage in Alternative Underwriting

Alternative data underwriting leverages AI to analyze non-traditional data sources that reveal creditworthiness patterns invisible to conventional scoring. These data sources include:
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    • Telco and utility payment histories demonstrating consistent payment behavior
    • Gig economy income flows that traditional employment verification might miss
    • Open banking transaction data providing comprehensive financial activity insights
  • Behavioral and psychometric data

    including mobile usage patterns and psychometric assessments that indicate financial responsibility
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    that can identify fraud rings while respecting privacy
Machine learning algorithms identify subtle patterns like consistent utility payments paired with stable mobile usage that strongly correlate with loan repayment likelihood. AI combines these diverse data streams into coherent risk profiles that traditional scoring cannot achieve.

The Real-World Impact

Financial institutions implementing AI-driven alternative data strategies report significant outcomes:
  • 15-54%

    Increased addressable market by 15-40% as previously “unscoreable” applicants become viable
  • 60%

    Reduced manual review processes by up to 60% through automated decision-making
  • Inclusion

    More responsible inclusion with default rates remaining stable or improving compared to traditional methods
For borrowers, alternative data underwriting means access to credit for education, business development, and financial emergencies that would otherwise remain out of reach.

The Data Integration Challenge

Successfully implementing alternative data underwriting requires intelligent synthesis across multiple data sources. The most effective approaches combine traditional bureau data (when available) with alternative sources to create comprehensive risk profiles.

AI excels at this integration challenge. Unlike rules-based systems that struggle with data inconsistencies, machine learning models can weight different data sources dynamically based on their predictive value for specific customer segments. A recent graduate with limited credit history featuring strong educational credentials and consistent digital payment patterns might receive favorable consideration that traditional scoring would miss.

Emerging Markets: The Ultimate Testing Ground

Alternative data underwriting finds its most dramatic applications in emerging markets, where traditional credit infrastructure remains underdeveloped. In these environments, AI models might analyze:
  • Mobile money transaction patterns indicating cash flow stability
  • Agricultural data for farmers seeking seasonal credit
  • Educational completion rates and professional certifications
  • Social community involvement and local reputation indicators
Financial institutions operating in these markets report that AI-powered alternative data models often outperform traditional credit scoring, even when both are available, because they capture more nuanced, real-time behavioral patterns.

Regulatory Considerations and Ethical AI

As alternative data adoption accelerates, regulatory frameworks are evolving to address fair lending concerns. Alternative data must enhance rather than undermine financial inclusion goals. This requires:
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    that can explain decision factors
  • Bias monitoring

    to prevent discriminatory outcomes
  • Data privacy compliance

    that respects consumer information rights
  • Continuous model validation

    to ensure predictive accuracy across demographic groups

The Strategic Implementation Path

For financial institutions considering alternative data underwriting, the most successful approaches follow a structured progression:
  • Start with data partnerships that provide reliable, compliant alternative data sources
  • Pilot with specific segments where traditional scoring shows limitations
  • Implement robust model governance from day one to ensure regulatory compliance
  • Scale gradually while monitoring outcomes across customer cohorts
  • Continuously refine data sources and model performance based on results

Looking Forward: The Future of Inclusive Lending

Alternative data underwriting represents a fundamental shift toward more inclusive, accurate risk assessment. As AI capabilities continue advancing and data sources become richer, we can expect even more sophisticated approaches that combine traditional and alternative data streams seamlessly.

The institutions that master this integration will expand their addressable markets while creating competitive advantages in customer acquisition, risk management, and regulatory compliance. More importantly, they’ll contribute to a more inclusive financial system that serves previously underserved populations effectively.

The future of lending augments traditional methods with AI-powered insights that reveal creditworthiness in all its forms. For the millions of credit-invisible consumers worldwide, that future can’t arrive soon enough.

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Atom Bank Selects Provenir for Risk Decisioning and Data Orchestration

Atom Bank Selects Provenir for Risk Decisioning and Data Orchestration

The UK’s first app-based bank to deploy Provenir’s award-winning AI Decisioning Platform to support multiple consumer and business banking products

Parsippany, NJ – April 28, 2025 – Provenir, a global leader in AI risk decisioning software, today announced Atom Bank has selected the Provenir AI Decisioning Platform to streamline and modernize credit risk decisioning and data orchestration.

Atom Bank launched operations in April 2016 as the UK’s first app-based bank, offering mortgages and savings through its app, as well as secured business lending for small and medium-sized enterprises. It is currently the highest rated UK bank, savings bank and mortgage lender on Trustpilot.

With Provenir’s AI Decisioning Platform, Atom Bank is streamlining its data orchestration and decisioning in the areas of credit, fraud, and identity, across its residential mortgage, business banking secured lending, consumer savings, and Buy-to-let mortgages offerings.

“Atom Bank provides simple, well-designed mortgages and savings products that deliver ease, speed and value right to your device. As our customer base and operations continue to grow, our adoption of Provenir’s AI Decisioning Platform will reduce the complexities of managing multiple risk decisioning platforms while supporting our commitment to exceptional customer experience…Provenir demonstrated a deep understanding of what we were looking for in a modern, all-in-one decisioning and data solution that could scale to meet our growth plans.”

Chris Storey, Chief Commercial Officer

“We’re proud to partner with Atom Bank, which has quickly become one of the most innovative and successful challenger banks in the U.K.,” said Mark Collingwood, Vice President Sales Europe at Provenir. “Our AI-Decisioning Platform will help Atom Bank achieve its business objectives and customer experience aspirations to support its goal of being ‘the most customer-centric bank on the planet.’”

Provenir’s AI Decisioning Platform brings together the power of decisioning, data, and decision intelligence to drive smarter decisions. This unique offering gives organizations the ability to power decisioning innovation across the full customer lifecycle, driving improvements in customer experience, best-in-class fraud prevention, access to financial services, business agility, and more.


atom

About Atom Bank

Atom Bank is the UK’s first app-based bank, on a mission to make the experience of borrowing and saving faster, simpler and better value than anyone else.

The bank launched operations in April 2016, and offers award-winning mortgages and savings through its app, alongside secured business lending for small and medium-sized enterprises.

Based in the North East of England with a team of over 500 people, Atom is here to change banking for the good, for the better, and for everyone. This means focusing on customers’ needs, delivering better value than the incumbents, providing an exceptional app-based experience and offering award-winning customer support via phone, chat, email and social channels. The bank has some of the best customer service credentials in the UK, having achieved 5-star ratings on both the iOS and Android App Stores, and on Trustpilot, whilst consistently delivering Net Promoter Scores (NPS) in the high 80s.

Based in Durham, Atom is an engaged and active member of the North East Community. In 2022 Atom signed a five-year Memorandum of Understanding with Durham University to progress key research and diversity initiatives. The region has one of the highest levels of youth unemployment in the UK and Atom is passionate about addressing the critical digital skills gap and helping develop young people and other groups that are under-represented within the industry.

As of November 2021, all employees enjoy a four-day working week, after Atom became the largest company – and only bank – in Britain to introduce the policy for all employees, with no reduction in salary.

The Atom executive team are highly experienced, having built and run some of the most well-respected banks in the UK. CEO Mark Mullen has 30 years’ experience in the sector and was previously CEO at the multi- award-winning telephone and internet bank first direct. The team is supported by a strong non-exec board, chaired by Lee Rochford.

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