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The Fraud-AI Double Bind

Written by Amy Sariego on . Posted in Blog. No Comments on The Fraud-AI Double Bind

The Fraud-AI Double Bind: New Survey Reveals the Fraud-AI Paradox Facing Financial Institutions

Financial institutions face a critical tension. They need AI to combat increasingly sophisticated fraud. Yet 77% are concerned about AI-enabled fraud threats.

Our 2026 Global Decisioning Survey, conducted by The Harris Poll across 203 senior decision-makers in 22 countries, reveals the scope of this challenge and the strategies organizations are using to address it.

The Numbers

  • The adoption of AI for fraud prevention is strong:

    • 75% use AI-driven adaptive fraud prevention
    • 74% deploy real-time anomaly detection
    • 87% trust AI decisioning outcomes

  • Yet the concern is equally widespread:

    • 77% worry about AI-enabled fraud threats
    • 50% struggle to detect and react quickly to new fraud trends
One Chief Risk Officer we surveyed described the compliance challenge as “trying to get certified for a standard that hasn’t been written yet.”

The Speed Problem

When we asked about their biggest application fraud challenge, 50% identified detecting and reacting quickly to new fraud trends.

Bad actors use AI to evolve their tactics in real-time, testing thousands of attack vectors simultaneously. Traditional monthly or quarterly model updates can’t keep pace. Organizations need real-time, adaptive AI systems to combat fraud, but deploying those systems runs directly into implementation barriers around governance and explainability.

What Comprehensive Fraud Strategy Requires

When we asked what’s most important for delivering comprehensive fraud prevention, organizations prioritized four capabilities:

33%

rank as most important

Comprehensive fraud risk review of customer data:
Organizations need complete visibility across customer interactions and behavior patterns. Siloed views by channel or product line leave blind spots.

23%

Reducing friction in customer experience

Security cannot come at the cost of customer experience. Organizations that create too much friction lose legitimate customers to competitors.

22%

Aligning data at customer level vs. by channel

Breaking down silos to create unified customer views enables better fraud detection without false positives that frustrate good customers.

19%

Breaking down data silos between fraud and credit teams

Traditional organizational separation between fraud and credit creates blind spots. Integrated views improve both fraud prevention and credit decisioning.

How Organizations Measure Success

The variety in primary metrics reflects different organizational priorities:

  • 54%

    track enhancing operational efficiency and automation (16% say this is their primary metric)

  • 54%

    track improving accuracy of AI and ML models (25% say this is their primary metric)

  • 52%

    track reducing fraud loss (15% say this is their primary metric)
Operational efficiency and model accuracy rank equally with fraud loss reduction. Organizations recognize that sustainable fraud prevention requires systematic operational excellence beyond just loss minimization.

  • Breaking Free from the Double Bind:

    Organizations successfully navigating this tension balance aggressive AI adoption with comprehensive risk management.

  • Deploy explainable AI architectures from the start:

    They don’t sacrifice interpretability for performance. Modern approaches enable both.

  • Maintain human-in-the-loop oversight for high-risk decisions:

    AI handles volume and speed, but humans make final calls on edge cases and high-stakes scenarios.

  • Implement continuous monitoring for model drift and bias:

    They don’t deploy and forget. Models require ongoing governance.

  • Build governance as an ongoing product, not a one-time project:

    Governance evolves alongside AI capabilities and regulatory requirements.

The Implementation Reality

You can’t sacrifice governance for speed. But you also can’t sacrifice speed for governance. The most successful organizations find ways to achieve both.

They leverage platforms designed to orchestrate decisions across existing systems rather than requiring wholesale system replacement. This approach accelerates time-to-value and reduces technical risk.

Rather than ripping out decades of infrastructure, they deploy decisioning layers that orchestrate data from multiple sources and deliver decisions back to existing platforms in milliseconds.

Looking Ahead

The fraud landscape will continue to evolve faster than most organizations can currently respond. Organizations that successfully deploy explainable, governed AI at speed will protect revenue, preserve customer experience, and build sustainable advantages.

EBOOK Survey2026

Download the full 2026 Global Decisioning Survey:

Download Survey

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BLOG Survey01

Why 77% of Financial Institutions See Decision Intelligence as Their 2026 Priority

Written by Amy Sariego on . Posted in Blog.

Why 77% of Financial Institutions See Decision Intelligence as Their 2026 Priority

The financial services industry is experiencing a fundamental shift. Organizations have spent years automating decisions. Now they need those decisions to get smarter.

Our 2026 Global Decisioning Survey reveals the scope of this transition: 77% of senior decision-makers see Decision Intelligence as very valuable for their strategy over the next 2-3 years.

What Decision Intelligence Actually Means

Decision Intelligence represents the evolution from automated decisioning to continuously optimized, AI-driven decision-making that learns and improves.

THE DIFFERENCE:

  • The Traditional Approach:

    Deploy AI models, measure results periodically, update quarterly, manage explainability and governance separately

  • Decision Intelligence Approach:

    Execute decisions at scale, measure outcomes continuously, learn from performance, optimize in real-time within unified platforms that provide transparency, governance, and integration
Organizations are moving quickly:

  • 75%

    are already collaborating on AI-driven decision intelligence

  • 18%

    are exploring partnerships

  • 66%

    are very interested in using AI for strategy implementation and optimization

  • 60%

    plan to invest in AI or embedded intelligence for decisioning in 2026 (making it the top investment priority)

What Organizations Value Most

When we asked which AI features provide the most value, organizations prioritized capabilities that go beyond basic automation:

51%

Ability to leverage generative AI for natural language queries
The democratization of AI insights through conversational interfaces transforms who can access and act on decisioning data. Business users, executives, operations teams, and compliance staff can all interact directly with AI systems using natural language.

92%

of organizations find it important to interact with data quickly using natural language queries.
(62% find it very important, 30% moderately important).

  • 49%

    Real-time decisioning across customer touchpoints:
    Speed and consistency across channels create better customer experiences and reduce operational complexity.

  • 50%

    Transparency and explainability of AI models:
    Organizations need AI they can understand and defend to regulators and stakeholders.

  • 47%

    Integration with existing systems and data sources:
    AI must work with existing infrastructure rather than requiring complete replacement.

The Business Impact

Organizations cite four primary benefits from improved Decision Intelligence:

  • 62%

    cite operational efficiency:

    Automated decision-making reduces manual review, accelerates processes, and lowers costs while improving consistency.

  • 52%

    cite better customer experience:

    Faster decisions, reduced friction, and personalized interactions create superior customer journeys.

  • 58%

    cite improved accuracy of models and strategies:

    Continuous learning and optimization improve predictive performance and business outcomes over time.

  • 56%

    cite faster deployment of new decision strategies:

    Rapid testing and iteration enable organizations to adapt quickly to market changes and competitive pressure.
These benefits compound over time. Organizations that deploy Decision Intelligence don’t just get better decisions today. They build systems that continuously improve.

The Intelligence Loop in Practice

Decision Intelligence creates a continuous cycle:

  • chess

    Shape Strategy

    Design and evolve decision strategy by learning from how decisions actually perform. Strategy is measured through outcomes and continuously refined to balance risk exposure and revenue opportunity.

  • rocket

    Execute Decisions

    Make real-time, data-driven decisions at every customer touchpoint using deep customer understanding, data, context, and decision history.

  • dashboard

    Measure Outcomes

    Connect decisions to business outcomes to see what actually drives risk, revenue, and profitability.

  • learning

    Learn and Optimize

    Get specific recommendations to improve performance based on actual results. Learn from the results over time and continuously refine strategies.
This loop transforms decisioning from a periodic batch process into a continuous optimization system.

The Natural Language Revolution

92% of organizations find it important to interact with data quickly using natural language queries. This represents a fundamental shift.

When business users can interact directly with AI systems using conversation, they build intuition about how these systems work. That understanding improves their ability to provide governance oversight and makes the entire organization more comfortable with AI-driven decisioning.

Natural language querying enables:

  • Business users to explore decisioning data without SQL knowledge
  • Executives to get instant answers to strategic questions
  • Operations teams to investigate anomalies in real-time
  • Compliance teams to audit decisions conversationally
This democratization helps address one of the top implementation barriers: explainability. When more people in the organization can interact with and understand AI systems, those systems become more transparent by design.

Addressing Implementation Barriers

Decision Intelligence approaches help address the barriers preventing AI adoption:

  • Explainability

    Platforms provide visibility into what decisions were made, how they perform, and why. This makes it easier to explain outcomes to regulators and stakeholders.

  • Governance

    Connecting decisions to business outcomes (risk, revenue, customer experience) makes governance more manageable. You measure results and learn from performance rather than monitoring models in isolation.

  • Integration

    Decision Intelligence platforms orchestrate data and decisions across existing infrastructure without requiring wholesale system replacement.

  • Speed

    Organizations can learn from every decision and optimize continuously, addressing the speed challenge that 50% cite as their biggest fraud detection obstacle.

Looking Ahead

The survey reveals clear momentum:

  • 77%

    see Decision Intelligence as very valuable

  • 75%

    are already implementing it

  • 66%

    want AI for strategy optimization

  • 60%

    are investing in 2026 (top priority)
Traditional decisioning optimizes for speed. Decision Intelligence optimizes for outcomes. Organizations that build systems capable of continuous learning will create advantages that compound over time.

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Download Survey

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LewisGRP

Leading South African Furniture Retailer Lewis Group Partners with Provenir to Drive AI Credit Decisioning Transformation in the Cloud

Written by provenirmulti on . Posted in News. No Comments on Leading South African Furniture Retailer Lewis Group Partners with Provenir to Drive AI Credit Decisioning Transformation in the Cloud

Leading South African Furniture Retailer Lewis Group Partners with Provenir to Drive AI Credit Decisioning Transformation in the Cloud

The retailer will deploy Provenir’s AI Decisioning Platform in the cloud to improve agility and operational efficiencies, with the ability to capitalize on greater customer insights

Parsippany, NJ | January 21, 2026 – South African furniture retailer Lewis Group is migrating its credit decisioning to the cloud with Provenir, a global leader in AI risk decisioning, to streamline its onboarding processes and expand customer touchpoints.

Lewis Group is a leading retailer of furniture, home appliances, electronic goods and homeware in South Africa through its brands Lewis, Best Home & Electric, Beares, UFO, Bedzone, Real Beds and Monarch Insurance. The retailer has 813 stores across South Africa and 145 in southern Africa, including Namibia, Botswana, Lesotho and Eswatini.

A Provenir customer for 15 years, Lewis Group is embarking on a cloud-migration strategy on the AWS stack, designed to elevate the customer experience and further drive innovation in credit decisioning. The goal is to enable more personalized customer engagements, further improve the onboarding process, and unlock meaningful productivity gains.

By modernizing and enhancing decisioning capabilities via the cloud, the Provenir AI Decisioning Platform supports Lewis Group’s commitment to responsible and effective customer engagement.

“Our long-standing collaboration with Provenir underscores a shared focus on using technology to deliver better outcomes for our customers… By migrating to the cloud, we are able to realize enhanced speed and agility, scalability, improved security, and faster time-to-market for solutions and services to our valued customers.”

Lambert Fick, Lewis Group’s GM Credit Risk

“After more than 15 years of partnership, we’re proud to support Lewis Group’s move to a modern cloud platform with our AI Decisioning Platform to drive improved business outcomes,” said Ryan Morrison, executive vice president, Provenir. “This migration will give Lewis Group faster, more effective decisioning, a unified customer view across channels, and the ability to leverage advanced analytics to enhance onboarding, fraud prevention, and overall customer management.”

Provenir’s AI Decisioning Platform combines data, decisioning, and decision intelligence to enable smarter, faster decisions across the entire customer lifecycle – from onboarding and application fraud to credit risk, customer management, and collections.

Explore Our AI-Decisioning Platform

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HyperPersonalization

From Risk Manager to Revenue Generator

Written by Jose Vargas on . Posted in Blog.

From Risk Manager to Revenue Generator:
How CROs Are Becoming the New Growth Heroes

As a Chief Risk Officer or senior executive, you’ve likely defended your risk budget in countless board presentations. You’ve explained loss ratios, regulatory compliance costs, and the value of preventing defaults. But here’s a question that might change how you position your department forever:

What if your risk team doesn’t just protect profit, but creates it.

The most profitable financial institutions have already discovered this truth. While their competitors view risk management as a necessary cost center, these organizations have transformed their risk functions into revenue engines that optimize every customer decision for maximum profitability.

Consider the numbers: McKinsey research shows that true personalization can boost revenue by 10-15% while increasing customer satisfaction by 20%. Yet when we analyze how most institutions actually make decisions, we find that most organizations believe they’re hyper-personalizing customer experiences when in reality they haven’t moved past applying predictive analytics with human judgment overlays.

The gap between perception and reality represents the difference between incremental improvements and transformational competitive advantage.

Your risk department sits on the most valuable asset in your organization: the ability to make profit-optimizing decisions for every customer interaction. While commercial teams bring customers through the door, risk teams determine whether those relationships generate sustainable returns or catastrophic losses.

The fintech graveyard is littered with companies that prioritized customer acquisition over sophisticated risk decision-making. They built beautiful user experiences, raised hundreds of millions in venture capital, and acquired millions of customers. They also gave away billions in capital because they never understood that sustainable revenue generation requires prescriptive risk management, not just predictive analytics.

Smart CROs are recognizing this inflection point. When we present this revenue-generation paradigm to risk leaders, the response is immediate recognition: “We’ve been saying this for years, but nobody listened.”

The conversation is changing. The question for your organization is whether you’ll lead this transformation or follow competitors who recognize risk management’s true revenue potential.

The Hyper-personalization Myth

Industry buzzwords create dangerous illusions. The same pattern that affects AI adoption – where everyone claims advanced capabilities while few achieve true implementation – applies directly to hyper-personalization.

Many organizations describe their approach as hyper-personalized because they use customer data to inform product recommendations. The critical distinction lies in execution methodology. Traditional approaches use predictive analytics to calculate probabilities, then apply human judgment to make final decisions about customer treatment.

This approach falls short of true hyper-personalization, which requires algorithmic decision-making without human interpretation layers.

  • Collections:

    The Decision-Making Divide

    Traditional collections processes illustrate this distinction perfectly. Standard approaches predict customer payment probabilities and delinquency risks, then rely on human judgment to determine contact timing, communication channels, and messaging approaches.

    Collections teams decide when to contact customers, whether to use phone calls, texts, or emails, and what tone to employ. These represent the when, how, and what of collections strategy – all determined by human analysis of predictive data.

    True hyper-personalization eliminates human decision-making. Advanced algorithms determine optimal contact timing for each customer, identify the most effective communication channel based on individual success probabilities, and prescribe specific messaging approaches. The system drives strategy execution based on optimization algorithms, not human interpretation of predictive analytics.

  • Credit Line Management:

    From Standard to Optimal

    Credit card portfolio management demonstrates another critical application. Effective credit limit optimization drives transaction volume and revenue generation through both interest income and interchange fees.

    Traditional approaches apply standardized credit limit policies, often resulting in customers preferentially using competitors’ cards with more suitable limits. This creates revenue leakage and reduces share-of-wallet performance.

    Hyper-personalized credit line management determines optimal limits for individual customers, ensuring specific cards become primary payment methods. The algorithm optimizes for usage frequency while maintaining payment capacity, maximizing profitability for each customer relationship.

  • Product Recommendations:

    Machine vs. Human Decision Authority

    Standard cross-sell processes predict customer preferences and acceptance probabilities for various products. Human analysts interpret these predictions to select specific products and terms for individual customers.

    True hyper-personalization requires algorithmic product selection with specific terms. The optimization engine makes complete decisions by balancing multiple factors: profitability, conversion likelihood, and long-term customer loyalty. The machine prescribes the right product with optimal terms for each customer based on what will generate the best total relationship value over time.

Your Internal Data Goldmine

The best decisions come from understanding your customers deeply. You already have the information you need.

Your existing customers are your biggest advantage. You’ve seen how they bank with you: their spending patterns, how they manage credit, when they make payments, and which products they use. This history tells you what each customer actually needs.

Even more valuable is understanding how customers react to your decisions. When you increase a credit limit, does the customer use it or ignore it? When you offer a new product, do they engage or opt out? This reaction data helps you predict how individual customers will respond next time.

For customers you don’t know as well, smart analytics can help. By studying customers you understand deeply, you can identify patterns that apply to similar customers with less history. You learn from your best relationships to improve your newest ones.

Looking ahead:

Beyond your walls. Right now, most personalization uses data you already own. There’s a largely untapped opportunity in bringing together different types of information beyond credit scores: broader signals that reveal customer needs and behaviors.

Making the Transformation Real

Historical financial services decision-making relies heavily on human judgment. Even when institutions can accurately predict customer behaviors, final decisions about loan amounts, pricing, and terms often depend on subjective analysis and competitive market reactions.

Competitive positioning doesn’t necessarily optimize profitability for specific customer relationships. True optimization requires maximizing profitability for every decision rather than simply maintaining market-competitive offerings.

  • The Technology Foundation

    Prescriptive analytics platforms provide the technological infrastructure needed to optimize individual decisions at institutional scale. These systems integrate predictive capabilities with optimization algorithms, enabling profit-maximizing decisions for every customer interaction.

    Advanced platforms process multiple constraints simultaneously: regulatory requirements, risk appetite parameters, profitability targets, and customer experience objectives. The technology enables real-time optimization across thousands of decision variables.

  • Success Measurement Evolution

    Revenue-generating risk functions require new measurement frameworks that capture both traditional risk metrics and financial performance indicators. Organizations must develop comprehensive measurement approaches that evaluate revenue generation, profit optimization, and sustainable growth alongside risk management effectiveness.

    Key performance indicators should include revenue per customer, profit margins by customer segment, lifetime value optimization, and cross-sell success rates. These metrics demonstrate risk management’s direct contribution to organizational financial performance.

  • Organizational Alignment

    Effective optimization frameworks unite commercial and risk stakeholders around shared objectives, eliminating traditional conflicts between revenue growth and risk management. Properly implemented optimization serves both revenue goals and risk management requirements simultaneously.

The Strategic Imperative

Implementation separates leaders from followers. Organizations ready to begin this transformation should start with three concrete steps:
  • Audit current decision-making processes.
    Map where human judgment currently overrides data in credit decisions, collections strategies, and product recommendations. These are your optimization opportunities.
  • Establish baseline metrics.
    Measure current performance on revenue per customer, lifetime value, and cross-sell conversion rates. You need to quantify the improvement as you shift to algorithmic optimization.
  • Start with one high-impact use case.
    Don’t attempt a full transformation immediately. Choose credit line management or collections optimization where you can demonstrate results within quarters, not years. Success in one area builds organizational support for broader implementation.

The technology exists.
The data exists in your systems.
What’s required now is leadership commitment to move from predictive analytics to prescriptive action.

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2025 winner

Provenir Wins Credit Risk Solution Award at 2025 Credit & Collections Technology Awards 

Written by provenirmulti on . Posted in News.

Provenir Wins Credit Risk Solution Award at 2025 Credit & Collections Technology Awards

AI decisioning platform recognized for innovation in
credit risk management across consumer lending and banking

LONDON, UK – December 1, 2025 – Provenir, a global leader in AI decisioning platforms for financial services, won the Credit Risk Solution award at the 2025 Credit & Collections Technology Awards. The ninth annual ceremony took place November 20, 2025, at the Midland Hotel in Manchester.

The Credit & Collections Technology Awards celebrate companies driving innovation in credit risk management across the financial services industry. The awards recognize organizations that consistently advance the profession through technology and strategic innovation.

Provenir’s award reflects the company’s work helping financial institutions make smarter credit risk decisions across the customer lifecycle—from onboarding through collections. The platform processes over 4 billion decisions annually for 110+ enterprise customers across 60+ countries, combining real-time risk assessment with embedded AI to help banks, fintechs, and consumer lenders balance growth with portfolio health.

The platform enables risk teams to automate underwriting decisions, adapt credit strategies in real-time, and optimize portfolio performance across consumer lending, banking, and BNPL use cases. Recent customer results include 10% increases in approval rates, 30% decreases in delinquent accounts, and 2X growth in customer base while maintaining risk discipline.

Provenir has been recognized as a Strong Performer in Forrester’s Wave for AI Decisioning Platforms and a Category Leader by Chartis Research in Credit Portfolio Management, Credit Lending Operations, and Risk Tech Quadrant for Retail Credit Solutions.

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provenir and atom bank

Atom Bank Selects Provenir for Risk Decisioning and Data Orchestration

Written by provenirmulti on . Posted in News. No Comments on 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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Provenir Rooftop

The Summer Skyline (UK)

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Provenir Next

The Summer Skyline (UK)

Exclusive rooftop networking event hosted by Provenir

  • Tuesday, 1st July 2025
  • 5:00 pm – 8:00 pm
  • Provenir Roof Terrace
    Platform Building New Station Street, Leeds, LS1 4JB, UK

Join us this July for The Summer Skyline Event — a relaxed evening of wine tasting, light bites, and meaningful connections high above the heart of Leeds.

Set on Provenir’s private roof terrace, this event brings together professionals and peers from across the industry for an unforgettable summer evening of conversation and city views.

What to Expect

  • A curated wine tasting experience
  • Informal networking with professionals across the industry
  • Panoramic rooftop views of Leeds
  • Seasonal light bites and refreshments

Whether you’re reconnecting or making new connections, The Summer Skyline offers the perfect blend of atmosphere and opportunity.

We are hoping for a warm summer’s evening, but we have a backup plan to ensure that rain won’t dampen it!

ProvenirNEXT

RSVP Now
Spaces are limited—reserve your place to join us for this exclusive summer gathering.

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Leeds

Leeds Digital Charity Ball 2025

Written by provenirmulti on . Posted in Event.

Event

Leeds Digital Charity Ball 2025

An Evening to Celebrate, Support, and Give Back

  • Thursday, 12th June 2025
  • Pre-event Reception at 6:00 PM
    The Queens Bar & Restaurant, The Queens Hotel, Leeds

    Main Event at 7:00 PM
    Royal Armouries Museum, New Dock Hall, Leeds
  • Black Tie | Glamorous & Elegant
The Leeds Digital Charity Ball 2025 is the event of the year that you won’t want to miss. This incredible evening brings together Leeds’ thriving tech and digital community to raise funds for vital digital inclusion projects across the city. After the success of previous years—raising over £60,000 in 2022, £100,000 in 2023, and £65,000 in 2024—the organisers are aiming to make 2025 even bigger and better. With your support, we can continue to bridge the digital divide and ensure everyone in Leeds has the opportunity to thrive in the digital world.
What to Expect:
This is a night of celebration, philanthropy, and entertainment. The event will feature:
  • Welcome Drinks – Enjoy a drink and network with fellow guests as the evening begins.
  • Three-Course Dinner – Indulge in a delicious gourmet three-course meal, designed to delight.
  • Charity Auction – Bid on exclusive items and unique experiences, with all proceeds supporting digital inclusion efforts.
  • Live Music & Dancing – Dance the night away with a fantastic live band, all while raising funds for a great cause.
It’s an opportunity to connect with like-minded individuals, celebrate the tech community in Leeds, and contribute to a meaningful cause.
Why Attend?

The Leeds Digital Charity Ball is more than just a gala—it’s a chance to directly impact the lives of those in our city who are most in need of digital access and skills. The funds raised will support projects that provide essential resources to help bridge the digital divide, offering opportunities for education, skills development, and connectivity to those who need it most.

Let’s make 2025 the biggest year yet! Your participation will help fund digital inclusion projects that are changing lives across Leeds. We hope you’ll join us in supporting this worthy cause.

Register your interest here

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datos report

Datos

Written by lizupson on . Posted in Landing Pages. No Comments on Datos

Report

Beyond Point Solutions: Orchestrating the Future of Fraud Prevention

Fraud is evolving fast—and financial institutions need to move even faster.

This report from Datos Insights explores why traditional point solutions can no longer keep pace with today’s fraud landscape—and how modern fraud orchestration platforms are helping financial services organizations unify strategies, adapt in real time, and outpace fraudsters.

Provenir is proud to be featured as a leading provider, showcasing how our AI-powered decisioning platform helps orchestrate smarter, faster, and more flexible fraud prevention across the customer lifecycle.

What You’ll Learn

  • Why 95% of FIs say siloed data is their #1 fraud-fighting challenge
  • How orchestration platforms reduce integration pain and IT delays
  • The four categories of fraud orchestration solutions—and how to choose what fits best
  • What makes Provenir’s low-code, high-flexibility platform stand out
  • Key trends shaping the $3.6B fraud orchestration market by 2028

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NEWS: 2025 New Global Survey

Written by Amy Sariego on . Posted in News.

New Global Survey Shows Nearly Half of Financial Services Executives Struggling to Manage Credit Risk and Detect and Prevent Fraud

AI is playing a prominent role in the revamp of credit risk decisioning
and fraud prevention strategies in 2025

Parsippany, NJ – February 12, 2025 – A new survey shows nearly half of all financial services executives are struggling with managing credit risk and detecting and preventing fraud. The survey also shows many are revamping their credit risk decisioning and fraud prevention strategies in 2025, with AI playing a prominent role.

These are among the key findings from the survey of nearly 200 key decision makers at financial services providers globally to understand their risk decisioning and fraud challenges across the customer lifecycle, decisioning investment priorities, and AI opportunities. The survey was conducted by Provenir, a global leader in AI Decisioning solutions.

Over half of all respondents plan to invest in risk decisioning solutions and AI/embedded intelligence in 2025 and beyond. At present, nearly 60% of respondents say they find it difficult to deploy and maintain risk decisioning models. 55% of executives recognize the value of AI to make streamlined strategy decisions, and in its ability to provide AI-powered performance improvement recommendations, and 53% see the value in the ability to automatically tune models to make better, more accurate decisions.

Key priorities for customer and account management are real-time, event-driven decisioning (65%), eliminating friction across the customer lifecycle (44%), and increasing customer lifetime value (44%).

Over half of respondents agree the biggest data challenge they face is being able to easily integrate data sources into decisioning processes.

Survey insights also reveal the pitfalls of operating multiple decisioning systems across the customer lifecycle. 59% of respondents say this is causing a lack of seamless data flow and unified insights, while 52% say it creates operational inefficiencies. Additionally, 28% said it contributes to an inconsistent customer experience.

When asked about data and fraud, 37% say they struggle with effective data orchestration for application fraud prevention, specifically in not being able to easily ingest and integrate new data sources, while 36% are challenged in using AI and machine learning for fraud prevention. Nearly one-third of respondents agree that the most important aspect for comprehensive fraud strategies is the ability to break down data silos between fraud and credit risk teams.

“Financial institutions are keenly aware of today’s increasingly complex threat landscape and must adopt new approaches for improved risk decisioning and fraud prevention across the customer lifecycle while providing frictionless and personalized customer experiences,” said Carol Hamilton, Chief Product Officer, Provenir. “With an AI decisioning platform more closely aligning credit and fraud risk teams, financial services executives can ensure holistic, end-to-end decisioning with a complete view of customers across the entire lifecycle.”

The survey was conducted November-December 2024; respondents were based in North America, EMEA, Latin America and Asia Pacific, holding the titles of manager, director, vice president, or above.

The full report of the survey findings can be found here.

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