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Author: Amy Sariego

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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:

  • 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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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.

EBOOK Survey2026

Download the full 2026 Global Decisioning Survey:

Download Survey

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How Digital Banks in APAC Can Turn AI Governance Into Competitive Advantage

Written by Amy Sariego on . Posted in Blog.

How Digital Banks in APAC Can Turn AI Governance Into Competitive Advantage

From Risk to Reward: AI Governance in APAC Banking

If you’re leading digital transformation at a bank in Singapore, Malaysia, Thailand, or across APAC, you’re facing a critical tension:

On one hand, your customers expect instant approvals, personalized offers, and frictionless experiences. AI is the key to delivering this at scale.

On the other hand, regulators are classifying AI use cases like credit scoring, fraud detection, AML/KYC monitoring, customer targeting, and compliance automation as “high-risk” — demanding explainability, bias testing, and robust audit trails.

So what do you do? Slow down innovation to stay compliant? Or move fast and hope for the best?

The best digital banks are doing neither.

Instead, they’re treating AI governance as a strategic advantage — using it to build customer trust, reduce risk, and move faster than competitors still stuck on legacy systems.

Here are five AI use cases where getting governance right unlocks measurable business value.

Credit Scoring & Lending:
Say Yes to More Customers — Safely

  • Why This Matters:

    Traditional credit scoring leaves millions of customers underserved. Thin-file applicants, gig workers, and new-to-credit customers often get rejected — not because they’re risky, but because legacy models can’t assess them fairly. 

    AI changes this. By analyzing alternative data, behavioral patterns, and real-time signals, digital banks can approve more customers while actually reducing default rates. 

  • The Governance Reality:

    Credit scoring is now classified as high-risk AI because biased or opaque models can lead to unfair lending, regulatory fines, and brand damage. MAS, BNM, and BOT are all increasing scrutiny on how banks make credit decisions. 

  • How to Do It Right:

    Leading digital banks are deploying explainable AI models with: 

  • Built-in bias testing to ensure fair treatment across demographics 
  • Continuous monitoring to catch model drift before it becomes a problem 
  • Human oversight workflows for edge cases 
  • Complete audit trails that satisfy regulators 

The result? They approve more customers, with confidence. 

Real Impact:

  • 95%

    of applications processed automatically without manual review

  • 25%

    faster underwriting while maintaining risk standards 

  • 135%

    increase in conversion rates through personalized credit decisions

The Bottom Line:

When you can explain why you approved or declined someone — and prove there’s no bias in the decision — you can safely expand your lending reach while building customer trust. 

Fraud Detection:
Stop More Fraud Without Frustrating Customers

  • Why This Matters:

    Mobile-first banking in APAC is booming — but so is fraud. Synthetic identity fraud, account takeovers, and first-party fraud are costing banks millions while eroding customer trust. 

    The problem with traditional fraud systems? They’re either too aggressive (blocking good customers) or too lenient (letting fraud through). You can’t win. 

  • The Governance Reality:

    Fraud detection models face increasing regulatory scrutiny on accuracy, robustness, and explainability. False positives damage customer experience. False negatives cost you money and regulatory credibility. 

  • How to Do It Right:

    The most effective approach combines: 

  • Behavioral profiling that learns normal vs. suspicious patterns over time 
  • Identity AI that detects synthetic IDs and stolen credentials 
  • Adaptive models that evolve as fraud tactics change 
  • Explainable alerts so investigators understand why a transaction was flagged 

This isn’t about blocking more transactions — it’s about blocking the right transactions while letting good customers through. 

Real Impact:

  • 135%

    increase in high-risk fraud stopped

  • 130%

    increase in legitimate approvals (fewer false positives) 

  • Faster

    investigation times with explainable, prioritized alerts 

The Bottom Line:

When your fraud models are transparent, adaptive, and accurate, you protect revenue and customer experience — without choosing between them. 

AML / KYC Monitoring:
Move From Reactive to Proactive Compliance

  • Why This Matters:

    Manual AML and KYC processes are expensive, error-prone, and slow. They also create compliance risk: missed suspicious activity can lead to massive fines, license threats, and reputational damage. 

    Automated monitoring solves this — but only if it’s done right. 

  • The Governance Reality:

    Regulators across APAC are demanding robust documentation, clear alert logic, and evidence that your AML systems actually work. “We have a system” isn’t enough anymore — you need to prove effectiveness. 

  • How to Do It Right:

    Smart digital banks are implementing: 

  • Continuous monitoring that flags suspicious patterns in real-time 
  • Automated alerts with clear, explainable logic 
  • Complete audit trails that document every decision 
  • Risk-based approaches that focus resources on the highest-risk cases 

The goal isn’t just compliance — it’s confident compliance that doesn’t drain resources. 

Real Impact:

  • Automated

    alert generation with explainable logic 

  • Reduced

    false positives and investigator workload 

  • Audit-ready

    Audit-ready documentation that satisfies regulators across multiple markets 

The Bottom Line:

When your AML/KYC systems are transparent, well-documented, and continuously monitored, compliance becomes a strength — not a burden. 

Customer Personalization:
Build Loyalty Without Breaking Trust

  • Why This Matters:

    Generic offers don’t work anymore. Customers expect you to know them — to offer the right product, at the right time, through the right channel. 

    AI-driven personalization makes this possible at scale. But get it wrong, and you risk privacy breaches, customer backlash, and regulatory penalties. 

  • The Governance Reality:

    Using customer data for targeting and personalization requires explicit consent, transparent logic, and fair treatment. PDPA regulations across APAC are tightening, and customers are increasingly aware of how their data is used. 

  • How to Do It Right:

    The most successful digital banks approach personalization with: 

  • Consent-first data practices that respect customer privacy 
  • Explainable recommendations so customers understand why they’re seeing certain offers 
  • Fairness testing to ensure no demographic groups are disadvantaged 
  • Real-time engagement that feels helpful, not intrusive 

Done right, personalization doesn’t feel creepy — it feels helpful. 

Real Impact:

  • 550%

    increase in accepted product offers 

  • 2.5x

    faster approvals for credit line increases 

  • 20%

    reduction in defaults through proactive risk management 

The Bottom Line:

When personalization is transparent, consent-based, and fair, it builds loyalty instead of eroding trust. 

Compliance Automation:
Launch Products in Weeks, Not Months

  • Why This Matters:

    The most frustrating bottleneck in digital banking? Waiting months for IT to implement new products or adapt to regulatory changes. 

    Meanwhile, competitors move faster, customers get impatient, and opportunities slip away. 

  • The Governance Reality:

    New regulations like MAS guidelines, BNM frameworks, and BOT standards require rapid adaptation. But most banks’ compliance systems are rigid, manual, and dependent on IT resources. 

  • How to Do It Right:

    Leading digital banks are adopting: 

  • Low-code compliance workflows that business users can configure 
  • Real-time validation against regulatory rules 
  • Scenario testing to identify issues before going live 
  • Multi-market support for banks operating across APAC 

This isn’t about cutting corners — it’s about making compliance more agile. 

Real Impact:

  • 4-month

     average time from concept to live product 

  • Changes to processes

     made in minutes, not weeks 

  • Successful expansion

     across multiple APAC markets with different regulatory requirements 

The Bottom Line:

When compliance is automated and business-user-friendly, it accelerates innovation instead of blocking it. 

The Pattern:
Governance Unlocks Growth

Notice the pattern across all five use cases?

The digital banks winning in APAC aren’t treating governance as a checkbox exercise. They’re using it to:

  • Build customer trust through fairness and transparency 
  • Reduce operational risk with continuous monitoring and audit trails 
  • Move faster by removing IT bottlenecks and vendor dependencies 
  • Scale confidently across products, markets, and customer segments 

The difference between treating governance as a burden vs. an advantage often comes down to infrastructure. 

  • Legacy systems make governance hard: they’re rigid, opaque, and require heavy IT lift for every change. 
  • Point solutions create governance gaps: fraud in one system, credit in another, compliance somewhere else — with no unified view. 
  • Modern AI decisioning platforms make governance natural: explainability built in, audit trails automatic, changes fast, and everything connected. 

What to Look For in an AI Decisioning Platform

If you’re evaluating solutions to power AI decisioning across your digital bank, here’s what matters: 

  • Unified Lifecycle Coverage

    Can it handle credit, fraud, customer management, and collections — or will you need to stitch together multiple systems?

  • Built-in Governance

    Does it offer explainability, bias testing, audit trails, and monitoring out of the box — or is governance an afterthought?

  • Decision Intelligence

    Can you simulate strategies, optimize performance, and continuously improve — or are you locked into static rules?

  • Business User Agility

    Can your risk and compliance teams make changes independently — or do you need IT for every adjustment?

  • Real-Time Data Orchestration

    Can you access the data you need, when you need it, through a single API — or are you managing dozens of integrations?

Final Thoughts:
The Future Belongs to Governed Innovation

The digital banks that will dominate APAC in 2025 and beyond won’t be the ones that move fastest or the ones that are most compliant. 

They’ll be the ones that do both — using governance as the foundation for sustainable, scalable, customer-centric growth. 

Because here’s the truth: customers don’t choose banks based on AI capabilities or compliance certifications. They choose banks they trust — banks that make smart decisions quickly, treat them fairly, and keep their data safe. 

Governance isn’t the obstacle to delivering that experience. When done right, it’s what makes it possible. 

Ready to shape the future of your decisioning with AI?

Contact Us

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europe mortgages

Top Mortgage Lending Trends in the UK and Europe

Written by Amy Sariego on . Posted in Blog. No Comments on Top Mortgage Lending Trends in the UK and Europe

Top Mortgage Lending Trends in the UK and Europe: Smarter Decisioning for a Changing Market

Navigating evolving market conditions, affordability challenges, and AI-driven risk management

The UK mortgage market is poised for a notable rebound in the coming year, with mortgage lending growth projected to double compared to 2023, according to EY. While this signals renewed optimism, lenders are still navigating complex challenges — rising interest rates, affordability constraints, evolving regulatory pressures, and shifting borrower expectations.

Across Europe, mortgage markets are experiencing varying levels of volatility. Some countries, like Germany and the Netherlands, are facing demand fluctuations due to interest rate adjustments, while others, such as France and Spain, are seeing pockets of resilience amid broader economic uncertainty.

So, how can lenders capitalize on growth while managing risk? By embracing advanced credit and fraud risk decisioning, leveraging alternative data, and integrating AI-driven automation, mortgage providers can ensure they remain competitive in a rapidly changing landscape. Here’s what you need to know.

1. Mortgage Market Rebound: Will Growth Be Sustainable?

After recent turbulence, the UK mortgage market is showing early signs of recovery. The latest data from EY forecasts that net mortgage lending will grow from £11bn in 2023 to £22bn — a significant shift fueled by economic stabilization and a potential slowdown in interest rate hikes. However, growth comes with some challenges:

  • Interest rates remain high compared to pre-pandemic levels, affecting affordability.
  • Consumer confidence is still fragile, with borrowers cautious about long-term financial commitments.
  • Regulatory scrutiny is increasing, with the Financial Conduct Authority (FCA) pushing for fair lending practices and enhanced risk oversight.
Across Europe, trends vary widely:
  • Germany is experiencing weaker housing demand due to tightening credit conditions.
  • France is navigating a slowdown in new mortgage approvals amid regulatory adjustments.
  • Spain and Portugal are seeing a rise in international buyers, stabilizing demand despite domestic affordability challenges.
What do you need to do? To thrive in this landscape, mortgage providers must improve risk assessment capabilities and adopt more dynamic credit and fraud risk decisioning frameworks that can adjust to market shifts in real time.
2. The Affordability Dilemma: Why Traditional Credit Scoring Isn’t Enough
Affordability remains one of the biggest challenges in the UK mortgage market. While lending volumes are set to increase, many borrowers are still struggling with:
  • High living costs and wage stagnation, which impact disposable income.
  • Stringent mortgage stress tests, making it harder for first-time buyers to qualify.
  • Variable rate mortgages, which are exposing homeowners to fluctuating monthly payments.
Traditional credit scoring models (which are heavily reliant on credit history and debt-to-income ratios) often fail to provide a full picture of a borrower’s financial health. That’s why leading lenders are increasingly turning to alternative data like the following to refine their risk assessments:
  • Open banking data: Real-time income and spending patterns can help assess affordability more accurately.
  • Rental payment history: Demonstrates financial discipline, especially for first-time buyers.
  • Utility and telecom payments: Provides additional insights into payment behaviors and financial stability.

By integrating AI-powered risk decisioning, you can analyze alternative data at scale, leading to more inclusive lending decisions and better default risk prediction.

What do you need to do? Move beyond traditional credit scores by adopting AI-driven analytics and alternative data sources to expand lending opportunities without increasing risk.

3. AI and Automation: The Future of Mortgage Decisioning

With mortgage competition increasing and regulatory expectations rising, you can no longer afford slow, manual credit decisioning processes. AI and automation are becoming essential tools for enhancing speed, accuracy, and compliance.

AI is transforming mortgage lending with:

  • Instant Decisioning – AI models process vast amounts of data in real time, reducing approval times from weeks to minutes.
  • Advanced Fraud Detection – AI-powered anomaly detection helps identify fraudulent applications before loans are approved.
  • Improved Regulatory Compliance – AI ensures fair lending practices by providing explainable decisioning frameworks and reducing bias.

But what’s the competitive advantage to AI Decisioning?

  • Higher Approval Rates: More borrowers qualify for mortgages through personalized risk assessment.
  • Reduced Risk Exposure: Predictive analytics detect high-risk applicants before issues arise.
  • Operational Efficiency: Automating credit checks and underwriting reduces costs and processing times.
What do you need to do? Future-proof your mortgage operations by implementing AI-driven decisioning platforms that enhance efficiency while maintaining compliance with FCA and EU regulatory guidelines.
Building a Smarter Mortgage Lending Strategy

With UK mortgage lending growth set to double and European markets shifting, mortgage providers must evolve their decisioning strategies to remain competitive.

By embracing AI, alternative data, and automated decisioning, you can:

  • Expand access to credit while minimizing default risk.
  • Deliver faster, more seamless customer experiences.
  • Ensure compliance with evolving regulatory standards.

As the mortgage landscape continues to change, the lenders that invest in innovation today will be the market leaders of tomorrow.

Ready to future-proof your mortgage lending strategy? Discover how AI-driven decisioning can help you boost approvals, manage risk, and streamline compliance.

Shape the future of your mortgage strategy with AI.

Learn More

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EVENT: Provenir Customer Advisory Board Meeting

Written by Amy Sariego on . Posted in Uncategorized. No Comments on EVENT: Provenir Customer Advisory Board Meeting

Event

Provenir Customer Advisory Board Meeting

May 13-14, 2025
Kansas City, MO – USA

Join us for an exclusive gathering of industry leaders and valued customers to collaborate, share insights, and shape the future of our partnership. Stay tuned—more details and a formal invitation to follow soon!

We look forward to seeing you in Kansas City!

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NEWS: AI Risk Decisioning Leader Provenir to Sponsor Financial Services Events

Written by Amy Sariego on . Posted in News. No Comments on NEWS: AI Risk Decisioning Leader Provenir to Sponsor Financial Services Events

AI Risk Decisioning Leader Provenir to Sponsor Upcoming Banking and Financial Services Events

Parsippany, NJ – February 19, 2025 – Provenir, a global leader in AI risk decisioning software, today announced its participation and sponsorship of three upcoming banking and fintech events focusing on key topics, including digital banking, banking trends, and policy and regulatory issues.

The events provide Provenir an opportunity to meet with financial services leaders to better understand the challenges they face amidst rising consumer debt, evolving digital banking platforms, and fraud mitigation. According to a recent survey by Provenir, nearly half of all financial services executives are struggling with managing credit risk and detecting and preventing fraud.

Details of the events include:

future digital finance connectFuture Digital Finance Connect 2025

Future Digital Finance Connect 2025
(Feb. 24-25, New Orleans)
The inaugural Future Digital Finance Connect is an exclusive, invitation-only gathering for senior digital and innovation leaders from top big banks, community banks, credit unions, credit cards and insurers. Provenir is a sponsor.

fintech meetupFintech Meetup

Fintech Meetup
(March 10-13, Las Vegas)
Fintech Meetup is the largest and most productive event for networking in the industry, bringing together fintech leaders to network, collaborate, and discuss industry issues. Provenir is a bronze sponsor and will be located at stand #2326.

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CBA Live 2025
(March 17-19, Orlando)
At CBA LIVE, retail banking professionals come to explore regulatory and policy issues, learn new trends, and share ideas that will improve their business strategies and better serve their customers. Provenir is a silver sponsor.

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 the customer experience, best-in-class fraud prevention, access to financial services, business agility, and more.
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charter logo

Customer Story: Charter

Written by Amy Sariego on . Posted in Customer Story.

charter logo

Charter Communications is a leading broadband connectivity company and cable operator, headquartered in Stamford, Connecticut. With an annual revenue of $55 billion, Charter provides high-speed internet, video, mobile, and voice services to millions of customers across 41 U.S. states.

As a trusted provider, Charter serves 57 million homes and connects 500 million IP devices to its robust network. The company also powers businesses with 300,000 fiber-lit commercial office buildings, ensuring seamless connectivity and innovation. Recognized for excellence, Charter has been ranked #1 in customer satisfaction by JD Power within its peer group, reflecting its commitment to delivering high-quality service and superior customer experience.

  • Industry
  • Region
  • Countries

    United States

  • Line of Business
  • Solution
  • Module
  • Infrastructure
  • ROI
  • Competition
Customer Timeline
Land MRR: $62K
Land PS: $462K
Expand MRR: $100K
Expand PS: $250K
  • Opportunity Created
    June 28, 2024
  • Opportunity Won
    January 21, 2025
  • Go-Live
    Estimated July 2025
  • Customer Expansion
    • Collections/Delinquency Mitigation
    • Portfolio Management (upsell/cross sell)
    • TRMA Sponsorship
    • Case Study
Initial Opportunity Details

  • Customer Challenge

    • Charter has seen application fraud rates spike significantly over the past three years.
    • Antiquated systems prevented Charter from effectively mitigating application fraud
    • Experian FraudNet Solution cost over $1M a year to support and was ineffective.
    • New senior executive team hired to rebuild Charter fraud onboarding infrastructure
    • Charter Data Science team was handcuffed by poor analytics, testing capabilities, and decentralized workflow tools.

  • Provenir Approach

    Profiling Engine

    Aggregation of specific values over a time period.

    • “Grouping of Activity” / “Buckets of Behavior”
    Examples:
    • IP Address 168.192.1.1 has been on 10 transactions over the past 6 hours
    • Location 123 has had a median order amount of $5,222 over the past 180 days
    Python Model Deployment

    Provenir provides the Charter Data Science team a platform to deploy, execute, test, monitor models they build to detect Fraud and Risk.

  • Provenir Impact

    • Reduced customer friction and losses, while optimizing operations through a stable, reliable, and scalable platform to support analytics and reporting needs.
    • Fraud and credit abuse controls prior to order submission will enable more accurate real-time decisioning.
    • $1M immediate annual cost reduction with the elimination of the Experian FraudNet tool.
    • The platform will enable risk assessment functionalities like testing rule performance and fraud decisioning through advanced ML models
    • Centralized Rule and Model Governance

  • Competitors

    Experian (incumbent), FICO, DataVisor, Socure, Visa (risk product) and Pega

  • Why We Won

    • Provenir Solution: Provenir Profiling Engine provided the most compelling/complete solution for Charter
    • Our Team: Fraud Expertise + Implementation Certainty
    • Decision Intelligence and Advanced AI/ML
      Centralized Rules and Model Governance

  • Pain Points

    • Decentralized fraud controls
    • Poor Analytics and Reporting
    • Infrastructure Downtime
    • Inability to leverage AI and Advanced Learning models
Customer Growth

Growth Opportunities

Organic Volume Growth – Charter’s expecting significant geographic expansion over next 3-5 years.

Expansion

  • Portfolio Management/Account Management
  • Collections – Charter has seen a rise in delinquencies and customer churn
Example Decisioning Flows
  • New Application

    Decisioning

    Orders received for two channels:

    1.Ship to Home
    or
    2.In Store

  • Internal/external Data Calls

    Decisioning

    Data Vendors

    • Ekata
    • SentiLink
    • Datafiniti
    • Nuance
    • RevSprings
    • UPS/FedEx
    • Citrix
    • Authentic ID
  • Real-Time Fraud Checks

    Decisioning

    Rules and Lookups

    • Negative List
    • Velocity Checks
    • Email, Billing, Device, Attempts, etc.
    • Feature Aggregation
    • Blacklist
    • Valid/Deceased SSN
    • Fraud Prevention Scenarios
    • SMB Orders
    • Positive Lists
  • Scoring and Risk Models

    Decisioning

    Analytical Models

    • Models built by Charter Data Scientists in KC
    • Champion / Challenge
    • Ongoing Feature Engineering
  • Manual Review Exceptions

    Decisioning

    Alert Review

    • Red / Yellow / Green Risk Assignment
    • Fraud, Credit, Sanctions, Affordability Analyst and Underwriter Reviews
OTHER CUSTOMER STORIES

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Tonic Seafood & Steak, Wilmington, DE

Exclusive Event: Smarter Strategies for Card Issuers

Written by Amy Sariego on . Posted in Event.

Exclusive Event

Smarter Strategies for Card Issuers:
How to Navigate Risk, Fraud, and Portfolio Performance with Advanced Analytics

Join us live in Wilmington for cocktails and conversation

March 26th, 4:30 – 6:30pm
Tonic Seafood & Steak, Wilmington, DE

Join us for an exclusive Cocktail Hour & Discussion on March 26th in Wilmington, designed for credit card issuers and financial services providers in the area. This intimate networking event offers a unique opportunity to connect with industry peers, exchange insights, and explore innovative strategies to navigate today’s evolving risk landscape.

Amid shifting market conditions—including decreasing mortgage rates and the challenge of managing high-interest receivables—card issuers must continuously refine their approach to fraud prevention, portfolio management, and collections. But it’s not always easy to do – in our recent survey of nearly 200 key financial services decision makers, nearly 60% of respondents said it was difficult to deploy and maintain their risk decisioning models and over half said being able to easily integrate data sources into decisioning processes is their biggest data challenge.

In a short presentation followed by an interactive discussion, Provenir will highlight how advanced analytics, data orchestration, and AI-driven decisioning can empower issuers to:

  • Enhance fraud detection and prevention through better data integration and real-time decisioning (nearly 50% of our respondents said that managing credit risk and detecting/preventing fraud are their biggest challenges)
  • Optimize portfolio management by balancing performance ratios and mitigating balance attrition
  • Get ahead of delinquencies with predictive insights and proactive risk strategies

Whether you’re looking to strengthen fraud defenses, improve customer lifecycle management, or maximize portfolio profitability, this discussion will offer actionable takeaways to help future-proof your credit card business and provide key guidance on how to deploy advanced analytics in your business.

Enjoy curated cocktails, thought-provoking conversation, and an evening of valuable industry connections. Space is limited—reserve your spot today!

Register your interest here

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DATA SHEET: Fraud for Telco

Written by Amy Sariego on . Posted in Resource. No Comments on DATA SHEET: Fraud for Telco

Provenir: Application Fraud for Telcos

Holistic Fraud Detection with Comprehensive Subscriber View
Telcos globally are struggling with increasingly sophisticated fraud attempts fuelled in part by rapid digital transformation and exacting subscriber demands. But how can you stay ahead of evolving fraud tactics while ensuring a seamless customer experience for legitimate subscribers? Discover how Provenir’s Application Fraud solution enables you to detect fraud risk more accurately with a holistic, comprehensive view of your subscribers.
Discover how Provenir’s AI Decisioning can transform your telco business.

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