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CS-Industry: Financial Services

Brendan Deakin, Executive Vice President at Provenir

Confidence at Speed: What Risk Leaders Are Actually Missing

Confidence at Speed:
What Risk Leaders Are Actually Missing

Most risk organizations are facing a similar problem: they need to move faster. More decisions, more channels, more pressure to approve in seconds rather than days. So the instinct is to buy for speed. Faster engines, faster models, faster onboarding. 

However, speed is not the problem most of these organizations actually have.  

Visibility is. 

At Provenir, we’ve had risk leaders who can tell us exactly how fast their systems approve an application but aren’t clear on why a given decision was made, whether it would hold up under regulatory review, or how a policy change three systems ago is still affecting today’s approval rate. That is not a speed gap. That is a control gap, and it’s the one that gets expensive. 

How the Gap Forms

It rarely happens on purpose. A credit engine gets bought to solve credit. A separate fraud tool gets added when fraud becomes the priority. Collections gets its own system when recovery rates slip. Each decision made sense in isolation. None of them were made with the others in the room.

Years later, the risk organization is running on a stack of tools that were never designed to share a data model, a decision log, or a governance framework. Authority over decisioning becomes diffuse: spread across systems, vendors, and teams, with no single place to see the whole picture. Strategy gets constrained by what the architecture can support, instead of the architecture supporting the strategy the business actually wants to run.

This is what flying blind looks like in practice. Not a lack of data. Too much data, scattered across systems that do not talk to each other, with no consolidated view of how a decision was actually made.

Picture a single customer moving through that environment. Credit approves them based on the data available at onboarding. Weeks later, the fraud system flags unusual activity on the account, working from a different data set and a different scoring logic, with no visibility into the credit decision that already happened. By the time the account reaches collections, the treatment strategy is built without either of those earlier signals. Three systems, three partial views, and no single place where a risk leader can see the full picture of that one relationship. Multiply that by a portfolio of millions of customers, and the visibility gap stops being an inconvenience. It becomes the reason risk teams get surprised by numbers that a connected system would have surfaced months earlier.

Why This Is The Moment It Matters Most

Three forces are converging on risk organizations right now, and each one raises the cost of staying fragmented.

AI adoption is outpacing AI governance. Risk teams are under pressure to bring AI and machine learning into decisioning faster than ever, but a model added to one system in isolation is a model nobody can fully explain across the customer’s full journey. The more AI gets layered onto a fragmented stack, the harder it becomes to answer a simple question: what, exactly, made this decision, and can we defend it.

Regulators are asking harder questions. Explainability and auditability are no longer boxes to check once a year. Examiners want to see, on demand, why a specific decision was made for a specific customer, and fragmented systems make that request expensive to fulfill even when the answer is defensible. The cost is not just the audit itself. It is the time risk teams spend reconstructing decisions after the fact instead of having the answer ready.

Vendor sprawl has stopped paying for itself. Every additional point solution came with its own contract, its own integration cost, and its own maintenance burden. Individually, each one looked justified. Collectively, they have become expensive to run and slow to change, and that cost shows up twice: once on the budget, and again in how long it takes to respond when the business needs to move.

Risk organizations that consolidate onto a single, governed decisioning environment are the ones positioned to manage risk deliberately rather than reactively. That is a different posture than moving fast. It is moving with your eyes open.

What Visibility Actually Buys You

A governed decisioning environment does not trade control for speed. It gets you both, in the right order.

When every decision is traceable and explainable by design, risk teams can calibrate exposure on purpose instead of discovering problems in a post-mortem. When strategy changes can be simulated and tested against real production data before they go live, business teams can move quickly because they have already validated the outcome, not because they are hoping for the best. When the full customer lifecycle runs through one system instead of four, a policy change made in one place shows up consistently everywhere it should, and nowhere it shouldn’t.

This is the shift I would ask every risk leader to consider: the goal was never speed for its own sake. It was confidence at speed. Those only coexist when the organization can see, end to end, exactly how and why every decision gets made.

Where This Leaves Risk Leaders

The organizations that will separate from the pack over the next few years are not the ones with the fastest single decision engine. They are the ones that closed the visibility gap first, and can move fast because they have full control over the decision layer, not in spite of it.

Brendan Deakin, Executive Vice President - Provenir

Brendan Deakin

Written By

Executive Vice President, Provenir

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FAQ

Decision Management Implementation FAQs for Enterprise Banks

Decision Management Implementation FAQs for Enterprise Banks

Implementing a new Decision Management platform is a significant undertaking for any enterprise bank, involving cross-functional teams, careful planning, and a shared commitment to getting it right. The following FAQs address the questions banks most often raise as they prepare for a Provenir implementation, from initial scope and governance to testing, go-live, and future expansion. Whether you’re evaluating what the process involves or preparing your team for a project already underway, these answers offer a practical look at what to expect at each stage.

1. What does implementing a new Decision Management platform actually involve?

A Decision Management implementation goes beyond configuring decision rules. A typical Provenir implementation can include defining the initial decisioning scope and workflows, integrating and orchestrating data, configuring case management and reporting, supporting model design and deployment, completing user acceptance testing, and preparing the solution for production go-live.

The exact scope is defined around the bank’s initial objectives, with Provenir Professional Services supporting the journey from initial implementation through future expansion.

2. Who from our bank needs to be involved in the implementation?

Enterprise implementations typically require participation from business, technical, quality assurance, project management, and executive stakeholders.

Business users help define and maintain requirements, participate in workshops and design reviews, execute business testing, and provide sign-off on design and go-live readiness. Technical teams support architecture, integrations, networking, security, encryption, request/response structures, and non-functional requirements. QA resources develop and execute test plans, while executive sponsors and steering committees provide governance and escalation oversight.

3. How much responsibility does our internal team have during implementation?

Implementation is a joint delivery effort rather than a vendor working independently and handing over a finished system.

The bank owns its business requirements, provides subject-matter expertise, participates in solution and design reviews, supports integrations and technical architecture, develops or executes relevant testing, and ultimately signs off on readiness. Provenir works alongside those teams to manage delivery, configure the solution, support testing, identify dependencies, manage issues, and prepare for production deployment.

4. How is a large Decision Management implementation governed?

Provenir uses a structured governance framework covering onboarding, resource planning, communications, escalation, scope control, assumptions, risk mitigation, change requests, timelines, deliverables, and advisory requirements.

Governance can include daily scrum calls where needed, weekly project-management meetings and status reporting, and monthly executive or steering-committee reviews. A central project plan, milestone tracking, action logs, risk and issue registers, and formal change-management processes provide visibility throughout delivery.

5. How do you keep scope, dependencies, and timelines under control?

Implementation planning starts by aligning on the finalized scope, requirements, assumptions, dependencies, critical milestones, design components, resources, testing cycles, communication plans, and escalation processes.

Provenir maintains these through project-management tools such as the integrated project plan, requirements and project checklists, milestone tracking, action-item management, risk and issue logs, and a formal change-request process. This gives both teams a common source of truth as the project progresses.

6. What documentation and planning should we expect before development begins?

A structured enterprise implementation typically includes a Business Requirements Document, Technical Design Document, high-level project plan, and a jointly agreed sprint breakdown.

Before build activities progress, the teams review the finalized scope, requirements, assumptions and dependencies, critical milestones, design components, resource availability, testing and validation cycles, planned holidays or blackout periods, and communication and escalation plans.

This upfront alignment helps ensure that business, technology, and delivery teams are working against the same requirements and implementation plan.

7. How is the solution tested before it goes into production?

Testing is performed progressively throughout the implementation rather than being left until the end.

The approach described by Provenir includes unit testing, integration testing, end-to-end workflow testing, QA and user acceptance testing, regression testing, and performance stress testing. Individual components are tested before being integrated into the overall decision workflow, and regression testing is performed throughout delivery to identify issues earlier and reduce the effort required during final validation.

8. How do we know the new Decision Management platform will meet our performance requirements?

Performance testing is included as a specific validation activity.

Provenir can use performance-testing tools such as JMeter to simulate large volumes of applications and measure system performance against the bank’s required service levels. This provides evidence that processing performance is meeting agreed expectations before the solution moves into production.

9. What happens once the initial implementation goes live?

Go-live is not the end of the relationship.

Provenir offers ongoing support subscriptions that can include training, configuration reviews, performance assessments, expert consultations, data and integration assessments, model and analytics reviews, and decision-strategy reviews. Training is also available through live sessions and Provenir Academy so internal teams can continue building their platform knowledge after implementation.

The objective is to support both stable day-to-day operation and continued optimization of the platform over time.

10. Can we expand into additional products, markets, or use cases after the first implementation?

Yes. Provenir’s implementation approach is designed to support expansion after the initial launch.

Follow-on projects can introduce additional products, regions, integrations, decisioning capabilities, or analytics requirements. Provenir can help define the scope and timeline of each expansion, prepare systems and teams for regional or product launches, and extend existing integration frameworks and platform configurations rather than treating each new initiative as an entirely separate implementation.

This allows the initial Decision Management deployment to become a foundation for broader decisioning transformation across the bank.

Sam Rohde

Sam Rohde

Written By

Vice President, PreSales & Solutions, Provenir

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AI governance in financial services

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

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

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

What Lies Beneath the Surface

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

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

Regulation as an Enabler

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

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

What “Governed AI” Really Means

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

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

Balancing Value and Risk

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

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

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

The Next Stage of AI Literacy

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

Mike Holmes

Mike Holmes

Written By

Head of Data Science, Provenir

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Banks Architecture Gap - Provenir

The Architecture Gap: Banking’s Next Competitive Battleground

Decisioning Architecture
The Architecture Gap: Banking’s Next Competitive Battleground

Banks have spent the last decade chasing digital transformation. Mobile apps, cloud migration, digital onboarding, and a wave of AI pilots have dominated the agenda, and the investment has been real. Yet many institutions are still dealing with the problems they started with: slow product launches, fragmented customer journeys, rising operating costs, and pressure from leaner digital challengers.

What sits underneath all of this is architecture – the growing distance between what a bank wants to do commercially and what its underlying technology and data can actually support.

Call it the architecture gap. It rarely shows up in a strategy document or a transformation roadmap. It shows up in execution: a product that should take six weeks takes six months, a routine application needs manual review, an AI pilot can’t move past the single use case it was built for. Across African banking, and globally, this gap is widening.

How growth widens the gap

Much of this comes down to growth. Many banks have expanded through acquisition and regional diversification, and commercially, that strategy holds up. But every acquisition brings its own core banking system, its own data model, its own decisioning logic. A few rounds of this, and an institution isn’t running one platform but a loose federation of partially connected systems, each with its own rules and its own version of the customer.

None of this reflects bad strategy – it’s simply what happens to architecture at scale, and the gap widens with every deal.

Even the strongest banks aren’t immune

This isn’t limited to banks that grow by acquisition. Groups as established and well-run as Absa and Standard Bank have absorbed real costs tied to legacy modernisation, including technology impairments linked to platform replacement and strategic reprioritisation.

These aren’t isolated accounting items – they’re a signal that banking architecture has become a moving target, where legacy systems depreciate faster than the investment cycles built to support them.

Digital-native banks will meet it too

Digital-native banks sit on the other side of this. TymeBank, Salt Bank, BankZero and Revolut to name a few, were built with cloud-native infrastructure, API-first design, and real-time data from day one.

But as they scale, take on more regulatory complexity, and expand their product range, they’ll meet their own version of the same constraint. The architecture gap isn’t a legacy problem – it’s a scale problem, and every bank eventually runs into it.

Why the gap matters now

Historically, banks could treat business strategy and technology execution as separate conversations. That’s no longer realistic. A bank’s ability to compete now comes down to how fast it can launch new products, how well it can orchestrate decisions in real time, how effectively it can move AI from pilot to production, how quickly it can adapt to new regulation, and how consistent the experience is across channels.

All of it depends on architecture, not on digital capability in the traditional sense.

AI will amplify the gap

Most banks are now investing heavily in AI across fraud, credit risk, collections, and customer engagement. But AI doesn’t operate independently of the systems around it. It depends on clean, connected, real-time data, a single view of the customer, decisioning embedded into the business rather than bolted on afterward, and orchestration that works across the enterprise.

Where the underlying architecture is fragmented, AI inherits that fragmentation too: effective in a pilot, hard to scale anywhere else.

Closing the banking architecture gap

The most important question facing banking leaders is no longer whether to modernise, digitise, or adopt AI.

It is whether their current architecture can support the institution they are trying to become. Because the next generation of winners in banking will not be defined purely by scale or speed.

They will be defined by how effectively they close the Banking Architecture Gap – combining trust, capital strength, and regulatory sophistication with architectural agility and intelligence.

In the age of intelligent banking, architecture is no longer an implementation detail.

It is the strategy itself.

Giovanni Hofmayer

Giovanni Hofmayer

Written By

Senior Sales Executive, Provenir

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christian-ball

Christian Ball

Enterprise Account Executive

Why the smartest capital allocation decision in financial services risk infrastructure isn’t build vs. buy, it’s knowing what’s actually worth building. 

The competitive environment in financial services has fundamentally changed. Margins are compressed. Regulatory complexity is accelerating. Customer acquisition costs are at historic highs. And the fintechs gaining ground aren’t necessarily the ones with the most sophisticated technology, they’re the ones deploying it fastest. 

That context matters when you’re evaluating whether to build proprietary risk decisioning infrastructure from scratch. 

The Real Cost of Building

The true cost of building a decisioning platform compounds over time. 

The upfront capex is significant: architecture design, engineering resources, data integration across bureau and alternative data providers, security infrastructure, compliance frameworks. Organisations that have gone through this report 18 to 36 months before a production-ready system is operational. In a market where a competitor can launch a new credit product in weeks, that gap carries direct revenue implications. 

The ongoing opex picture is frequently underestimated at approval stage. Maintaining data integrations as providers update APIs. Rebuilding model deployment pipelines as cloud infrastructure evolves. Keeping pace with regulatory change across markets. Resourcing the support function so the decisioning engine doesn’t become a bottleneck to every product iteration. These aren’t exceptional costs. They’re structural, recurring, and they scale with complexity. 

McKinsey research consistently shows that large-scale internal technology builds in financial services exceed budget in many cases, with five-year total cost of ownership frequently running 40–60% above initial projections. The resource drag on engineering teams is harder to quantify but equally real. Senior talent allocated to infrastructure maintenance is senior talent not working on competitive differentiation. 

Speed is Now a Strategic Variable

Digital-native lenders are entering established segments with lower cost bases and faster decisioning cycles. Embedded finance is putting credit products inside customer journeys that traditional institutions don’t own. Open banking and alternative data are changing what good underwriting looks like. Regulators are demanding more explainability and auditability. 

The organisations gaining ground can test, launch, and iterate on new products in weeks, not quarters. That agility is very difficult to sustain when the decisioning infrastructure itself requires lengthy development cycles every time the business wants to change something. 

What Provenir Changes in the Capital Equation

Provenir’s Decision Intelligence Platform is built for exactly this trade-off. The infrastructure is already built, maintained, and continuously updated: cloud-native deployment, a marketplace of integrated data providers, model management, compliance and auditability frameworks. What organisations configure on top of it is entirely their own. 

Rather than funding a multi-year infrastructure build, capital goes into configuration, integration, and the proprietary decisioning logic that actually differentiates the business. Time to production is measured in weeks, not years. 

The opex shift is equally significant. Data provider integrations, infrastructure scaling, security patching, regulatory update cycles all move from internal cost centres to the platform’s responsibility. Engineering resource shifts from maintaining infrastructure to building product. The ongoing cost base is predictable, subscription-based, and scales with usage rather than requiring constant reinvestment just to stand still. 

BBVA, Atom Bank, and SoFi each deployed Provenir to run fundamentally different business models: global commercial lending, retail digital banking, consumer refinancing, at different scales and in different regulatory environments. The underlying platform is common. The decisioning logic, risk models, and customer strategies are not. 

The IP Question

The executive concern about IP is legitimate and worth addressing directly. Competitive advantage in financial services credit sits in the credit policy, the data strategy, the risk appetite calibration, and the customer relationships built on top of the engine. On Provenir’s platform, all of that remains entirely proprietary. Scoring models are deployed inside the platform, not exposed. Decision logic is configured by your team to reflect your underwriting philosophy. Two organisations on the same infrastructure share no more of their competitive advantage than two companies hosting on AWS share their code. 

What Provenir removes is the infrastructure layer: the part that costs the most, delivers the least competitive differentiation, and consumes the most ongoing resource to maintain. 

There’s also value that’s difficult to replicate internally. The R&D investment across Provenir’s global client base creates platform capabilities that no single organisation, building in isolation, could justify on its own. 

The Bottom Line

The build option carries significant upfront commitment, multi-year timelines, and a structural opex burden that compounds over time. In a market where speed and adaptability are increasingly decisive, it also means slower product iteration and delayed competitive response. 

Provenir reframes the question from build vs. buy to where you deploy your capital and your talent. The platform provides the infrastructure. Your team builds the advantage. Your IP, your models, your risk strategy are fully proprietary, executing faster and at materially lower total cost than the build alternative. 

That’s a strategic decision, not just a procurement one. 

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

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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FibaFaktoring

Customer Story: Fiba Faktoring

Fiba Faktoring is a leading non-bank financial institution in Turkey, providing factoring and SME financing solutions. The company focuses on delivering fast, data-driven credit decisions to support small and medium-sized businesses while managing risk effectively.
  • Industry
  • Region
  • Countries

    Turkey

  • Line of Business
  • Solution
  • Module
  • Infrastructure
  • ROI
  • Competition
Initial Opportunity Details

  • Customer Challenge

    Fiba Faktoring needed to improve the speed, consistency, and scalability of its credit decisioning processes. Manual and siloed systems limited automation, slowed decision times, and made it difficult to support business growth.
  • Provenir Impact

    • Operational Efficiency Gains
      Provenir’s decisioning solution delivered a 65% automation rate in credit decisions for targeted SME ticket sizes, significantly reducing reliance on manual processes:
      • Automation eliminated manual bottlenecks
      • Decisions are standardized and consistent
      • Staff time redirected from manual tasks to higher-value work

    • Speed & Productivity Improvements
      Credit decision processing became five times faster, dramatically accelerating service delivery for SME customers and improving internal throughput.
      • Faster time-to-decision improves customer experience
      • Shorter wait times support SME cash flow needs
      • The company can handle higher volumes without additional headcount

    • Workload Reduction & Customer Experience
      The platform delivered a 40% reduction in workload across credit decision processes, enabling strategic risk assessment and improving satisfaction through quicker outcomes.
      • Streamlined workflows reduced operational strain
      • Faster processing led to improved client satisfaction
      • Competitive advantage in the SME financing market
  • Competitors

    Legacy in-house systems
    Manual decisioning processes
  • Why We Won

    • Single, unified decisioning platform
    • Fast time to value and implementation
    • High flexibility and business-user configurability
  • Pain Points

    • Slow credit decision turnaround times
    • Limited automation and scalability
    • Difficulty adapting decision rules quickly
Customer Growth

Growth Opportunities

  • Scalable Operations and Expansion of Offerings
  • The automation foundation positions Fiba Faktoring to scale operations efficiently across higher volumes and broader product sets.
  • Advanced Analytics for Competitive Advantage
  • By integrating advanced predictive models and AI workflows, the company can strengthen risk insights and enhance differentiation in the SME lending market.
  • Enhanced Customer Experience as a Strategic Growth Lever
  • Shorter decision times and data-driven service delivery enable improved customer acquisition and retention.

Expansion

With the core decisioning platform successfully implemented and delivering measurable value, Fiba Faktoring is now progressing toward expanding the use of Provenir’s capabilities to additional strategic areas: ​

  • Predictive Early Warning Systems: Leveraging analytics to detect risk trends proactively
  • Marketing & Pricing Optimization: Using AI insights to refine pricing strategies and product targeting
  • Additional Decisioning Use Cases: Exploring automation across broader internal decision workflows beyond credit decisions
OTHER CUSTOMER STORIES

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newday

Customer Story: NewDay

NewDay Ltd is a UK-based financial services company focused on responsible consumer credit who have just been acquired by KKR (private equity). Serving over 3.6 million customers, it offers products such as credit cards, instalment finance, and Buy Now Pay Later through brands like Aqua, Marbles, and Fluid, as well as co-branded solutions with major retailers. With £15.5 billion annual spend, 4.4 billion gross receivables, and advanced digital platforms, NewDay combines data-driven underwriting and technology to widen access to credit. Headquartered in London, regulated by the Financial Conduct Authority, and employing over 1,200 staff, NewDay’s mission is simple: help people move forward with credit.​
  • Industry
  • Region
  • Countries

    UK

  • Line of Business
  • Solution
  • Module
  • Infrastructure
  • ROI
  • Competition

Customer Timeline
Projected MRR: $150K
Projected ARR: £1.8m
Expand MRR: £27k
Expand PS: £324k


TCV: $5.4m
  • Renewal Created
    • Relationship since 2019
    • Cloud 2 positioning from early 2024
    • Long time users of Cloud 1 processing ~100 million trns per month
    • Originations / Collections / Customer Management
  • Renewal Result
    • Natural compelling event, however KKR Funding Challenge highlighted
    • Summer 2025
  • Go-Live
    October and November 2025
  • Customer Expansion
    • NEXT: Roll-Out: Fraud, DI, Cloud 2, Simulation
    • FUTURE:
      • Profiling
      • Case Management
      • NewDay Technology Clients
Initial Opportunity Details

  • Customer Challenge

    • Legacy decisioning systems were slow and costly to update.
    • Needed faster processing & delivery cycles (market changes, releases, tests).
    • Required greater internal control over credit decisioning logic and data sources.
    • Aimed for sub-second decisions and more product flexibility.
  • Provenir Impact

    • Speed & Agility:
      • Speed of Change Reduced by 80%
      • NewDay can now implement multiple credit decisioning changes within the same sprint.
      • Sub-Second Decisioning
      • Credit decisions are now delivered in under 1 second, enabling rapid customer feedback and better experience.
      • Impact: Faster market response and improved competitiveness.
    • Internal Control & Cost Efficiency: Enhanced Internal Control​
      • Business users can add data sources and update strategy without reliance on external vendors.
      • Reduced Operational Costs
      • Lower external costs for managing data items and system changes.
      • Quicker Onboarding
      • New hires familiarize faster due to intuitive decisioning UI.
      • Impact: More self-sufficiency, faster internal execution, and better resource allocation.
    • Competitive Advantage & Customer Experience:
      • Improved Customer Management & Collections
      • More control over limit strategy changes and refined customer decisioning.
      • Award-Winning Implementation
      • NewDay won the 2024 FSTech Award for Best Use of IT in Consumer Finance for tech innovation – powered by Provenir.
      • Impact: Enhanced customer experience, strategic differentiation, and industry recognition.
  • Competitors

  • Why We Won

    Provenir was chosen because its flexible AI-powered decisioning platform met all of NewDay’s requirements:

    • Enabled faster delivery cycles and autonomous configuration.
    • Integrated seamlessly with NewDay’s extensive data lake.
    • Supported full lifecycle decisioning from origination → collections.
  • Pain Points

    • Long release cycles and slow system updates.
    • Heavy reliance on external teams for change implementation.
    • Limited real-time testing and model deployment capabilities.
    • Inefficient credit decision support with big data sources.
Customer Growth

Growth Opportunities & Expansion

  • Fraud expansion through fraud profiling and 3rd party data integration (Focus in a future session)
  • Professional Services and Analytics opportunities – support for migration and beyond
  • Case Management
  • NewDay Technology Platform – Provenir White labelling for 3rd party use – LBG, Debenhams are live today, working towards more growth.
OTHER CUSTOMER STORIES

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RytBank

Customer Story: Ryt Bank

Ryt Bank is a Malaysia-based digital bank backed by YTL Group and Sea Limited. It positions itself as the first AI-powered bank, using its Ryt AI assistant (built on Malaysia’s ILMU LLM) to let you chat to pay bills, transfer money, and manage your account, targeting young professionals and frequent travelers with a simple, app-driven experience and transparent fees.
  • Industry
  • Region
  • Country

    Malaysia

  • Line of Business
  • Solution
  • Module
  • Infrastructure
  • ROI
  • Competition

Customer Timeline
Land MRR: $6,500 USD
Land PS: $16K USD
Expand MRR: ~$10K USD
Expand PS: $80K USD
  • Opportunity Created
    26th May 2023
  • Opportunity Won
    12th May 2025
  • Go-Live
    20th July 2025
    Technical Go-Live


    30 th August 2025
    Full Go-Live

  • Customer Expansion
    • Future: Property & Infrastructure-Linked Products
Initial Opportunity Details

  • Customer Challenge

    As a newly launched AI-powered digital bank, Ryt Bank needs to onboard and serve customers in seconds while maintaining robust risk controls and regulatory compliance. Early processes rely on a mix of internal systems, manual reviews, and hard-coded rules, making it difficult to support rapid product launches, dynamic pricing, and personalised credit decisions. This fragmentation slows time-to-yes, drives up operational effort, and limits the bank’s ability to fully leverage data and AI across the customer lifecycle. Ultimately, this impacts Ryt Bank’s ambition to scale quickly and deliver a seamless digital experience.
  • Provenir Impact

    • Smarter, AI-Driven Risk Decisions
      By combining Provenir’s decisioning platform with Ryt’s own AI models, Ryt Bank can assess creditworthiness in real time using a broader set of data points. This delivers more accurate approvals, reduces risk exposure, and supports consistent, data-driven decisions across the retail portfolio.
    • Faster Turnaround and Fully Digital Journeys
      End-to-end automation – from KYC and fraud checks to bureau calls and decision execution – has significantly reduced manual intervention, enabling near-instant decisions for onboarding and credit requests. This improves straight-through-processing rates, shortens time-to-yes, and enhances customer conversion in Ryt’s mobile-first channels.
    • Policy Compliance and Scalable Decisioning
      The solution enforces Ryt Bank’s credit, risk, and regulatory policies through configurable rules and strategies, ensuring consistent compliance with internal standards and Malaysian regulations. At the same time, it provides a flexible, scalable foundation to rapidly introduce new products and tweak policies as the bank grows.
  • Competitors

    FICO
  • Why We Won

    • Digital-Bank Ready, Cloud-Native Platform
      Provenir provides a modern, cloud-native decisioning platform designed for high-growth digital banks, supporting real-time decisions for onboarding, cards, and PayLater in a single environment.
    • Speed to Market and Business User Autonomy
      Our low-code configuration and reusable components allow Ryt Bank’s teams to rapidly design, test, and deploy strategies without heavy IT dependency, accelerating product launches and change cycles.
  • Pain Points

    • Need for instant, consistent decisions across onboarding
    • Difficulty orchestrating multiple data sources and analytics in one place
    • Limited agility to test and roll out new strategies, products, and risk policies
    • High operational overhead from manual reviews and fragmented workflows
Customer Growth

Growth Opportunities

Data Science Initiative: Collaboration with ILMU

Initial discussions have commenced between Ryt Bank, ILMU (YTL’s AI lab) and Provenir’s Data Science team to explore how ILMU’s LLM can be embedded into Provenir decisioning. This early collaboration focuses on use cases such as conversational credit applications, smarter risk insights, and automated policy explanations, laying the foundation for future AI-powered decision intelligence across Ryt Bank’s products.

Expansion

Property & Infrastructure-Linked Products

As YTL expands its townships, transport, and utilities footprint, Ryt Bank can create embedded financial products that are tightly linked to YTL’s property and infrastructure ecosystem. This includes tailored financing for YTL developments, bundled offerings that combine housing, utilities, connectivity, and banking, as well as subscription-style payments for transport and community services—all managed through the Ryt app. Such offerings deepen ecosystem stickiness, unlock new recurring revenue streams, and position Ryt Bank as the primary financial layer across YTL’s integrated developments.

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Event WFIS

WFIS Indonesia 2025

Event

WFIS Indonesia 2025

The Premier Financial Services Innovation Event

  • November 25–26, 2025
  • Booth P13

Provenir is proud to be a Gold Sponsor at WFIS Indonesia 2025 – The Premier Financial Services Innovation Event 

The two-day event will unite C-suite leaders, VPs, Directors, and decision-makers from over 200 banks, insurers, and fintechs across Indonesia. Together, they’ll explore how data, AI, and intelligent decisioning are reshaping the region’s financial ecosystem. 

Discover Intelligent Decisioning @ Booth P13 

Join us at Booth P13 on November 25–26, 2025, to experience how Provenir enables intelligent, data-driven decisions for financial services providers. 

As a global leader in AI decisioning, Provenir empowers organizations to automate, predict, and personalize every customer interaction – driving growth and trust across the financial lifecycle. 

Why Meet Us at WFIS Indonesia 2025? 

  • Smarter Risk Decisions – Automated in Real Time – Manage losses and approve more good customers with adaptive, AI-driven decisioning that learns continuously from data.
  • Predict Customer Needs with Behavioural Insights – Leverage contextual and behavioural data to anticipate customer intent and deliver proactive, relevant offers.
  • Hyper-Personalize Customer Experiences – Use AI-powered decisioning to personalize onboarding, engagement, and servicing at every touchpoint – driving loyalty and lifetime value.
  • End-to-End Financial Decisioning Solutions – Credit Risk Onboarding: Fast, accurate approvals with intelligent automation
    Application Fraud & Compliance: Detect, prevent, and stay compliant in real time
    Customer Management & Hyper-Personalization: Understand, engage, and retain with data-driven intelligence
    Collections Optimization: Recover smarter, faster, and more empathetically
  • Scalable, Cloud-Native Platform – Accelerate innovation with a configurable, low-code environment that scales effortlessly with your business.

Join our Session at 9.25 am | Day 2 – 26th Nov

Balancing Innovation and Trust: How AI Decisioning is Redefining Risk, Inclusion, and Customer Experience

  • How Provenir helps financial institutions embrace AI innovation responsibly by balancing automation, transparency, and compliance
  • Exploring how real-time decision intelligence detects social engineering and safeguards digital trust across customer interactions
  • Using Provenir’s AI and data marketplace to promote financial inclusiveness and expand access to underserved customer segments
  • Delivering hyper-personalized financial experiences that remain compliant, secure, and customer-centric
  • Uncovering how GenAI and agentic AI are shaping the next generation of intelligent, ethical, and inclusive financial ecosystems
Register your interest here

Speaker:

Wana Sedayu

Wana Sedayu

Senior Presales Consultant, APAC – Provenir

Wana is a Senior Presales Consultant at Provenir, supporting clients across the APAC region in driving digital transformation within the financial services sector. With over 15 years of experience in the industry, Wana brings deep expertise in loan origination, core leasing, credit decisioning, and customer management solutions.

Beginning his career as a software developer, Wana later transitioned into business consulting before dedicating the past decade to presales and value engineering roles. He has worked with prominent institutions such as Citibank, SMBC Indonesia, Bank Danamon, Bank of America, the Indonesia Stock Exchange, and the Ministry of Finance, contributing to numerous high-impact technology initiatives. OnlinePajak as Senior Manager Presales, and Fujitsu Indonesia as Presales Manager.

Combining his technical foundation with a strong business perspective, Wana is passionate about helping financial institutions accelerate innovation, optimize their decisioning processes, and achieve measurable business outcomes through data-driven solutions.

Why Provenir:

At Provenir, we help financial institutions automate smarter risk decisions, use behavioral insights to drive growth, and personalize every interaction with contextual intelligence – all from a single, unified platform. 

Let’s Connect:

Meet our team at Raffles Jakarta to discover how Provenir’s AI Decisioning Platform can help your organization accelerate approvals, prevent fraud, and deliver personalized customer experiences that build trust and profitability. 

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