Choosing a credit risk and fraud modelling partner is one of the most important decisions a lender or credit provider can make. The right provider works with you to help improve model performance, strengthen governance, support regulatory compliance and speed up delivery. The wrong one can create delays, increase costs and provide an off the shelf solution that doesn’t reflect your business.
Whether you're reviewing an existing supplier, building a business case for external support or preparing for a procurement exercise, knowing what good looks like isn't straightforward.
This guide explores what to look for in a credit risk modelling partner, the questions every credit provider should ask before making a decision, and the warning signs that can indicate future problems.
In this guide
- Why organisations seek external modelling support
- In-house vs external credit risk modelling
- What makes a good credit risk modelling partner?
- 10 questions to ask before choosing a modelling consultancy
- Warning signs to watch for
- What successful modelling programmes do differently
Why organisations look for external modelling support
Choosing a credit risk consultancy used to be fairly straightforward. Build a model, validate it, put it into production, and monitor performance.
Today, there's much more to think about.
AI and machine learning are opening up new possibilities. Models can identify trends that traditional approaches might miss. Decisioning can become more precise. And risk and fraud can be understood in greater detail.
But better performance brings new questions.
- How do you explain the decisions a model makes?
- How do you challenge a model that few people fully understand?
- How do you satisfy regulators that a model is fair, unbiased, robust and doing exactly what it's supposed to do?
Sound familiar?Many lenders seek external modelling support when they need to:
Even organisations with experienced analytics teams bring in external specialists. Fresh perspective, additional capacity and niche expertise can make the difference between a programme that delivers and one that drifts. |
According to the Bank of England and FCA, 75% of financial services firms are already using AI, with another 10% planning adoption within the next three years. This shows how quickly technology is moving. But expectations around governance, explainability and model risk are moving just as fast.
Of course, credit risk and fraud modelling doesn't stop because a redevelopment project is underway. Existing models still need monitoring, regulators still expect answers, and customers still need decisions. Meanwhile, someone has to build what's next.
That's why many lenders look for external support.
In-house vs external credit risk modelling: which approach is best?
It's one of the first questions many lenders ask when a major modelling programme looms.
Should we build this ourselves, or bring in external support?
The answer depends on the challenge you're trying to solve.
Many lenders have highly capable analytics teams with deep knowledge of their customers, products and lending strategies. That knowledge is invaluable.
External specialists bring something different. They've seen how other lenders have tackled similar challenges. They've worked across different portfolios, regulatory environments and modelling approaches. They bring fresh perspective, specialist expertise, and the ability to hit the ground running.
In reality, the strongest approach is often a combination of both.
In-house vs external credit risk modelling
The hybrid model
We find that the most successful programmes often combine internal knowledge with external expertise. Internal teams understand the business. External specialists bring additional experience, challenge established thinking and help accelerate delivery where required.
That's particularly valuable when organisations are tackling large-scale model redevelopment, IFRS 9 reviews, validation exercises, or the adoption of newer modelling techniques.
As the Head of Credit Risk Financial Planning at Nationwide, put it; “Aside from the Jaywing team feeling like an extension of my own stress testing function, I was equally impressed with their appetite to explore and present a wide breadth of options.”
❔A good question to ask: When evaluating a modelling partner, don't ask whether they can replace your internal team. Ask how they'll strengthen it. The best partnerships leave organisations with stronger models, greater confidence in their decision-making and more capability than they had at the start.
What makes a good credit risk modelling partner?
A good credit risk modelling partner brings more than technical modelling skills. Yes, they need to know their way around scorecards, IFRS 9, affordability, collections, fraud models, decisioning and model monitoring. That part is a given.
The real value comes from knowing how those models work in the real world: inside live lending journeys, under regulatory expectations, across different customer segments, and in businesses where every decision has a commercial and customer impact. That also means understanding the organisation behind the models: its strategy, culture, ambitions and customers.
For credit providers, this is key.
A model can perform well in development, then struggle when customer behaviour changes. It can improve risk selection, while creating new questions around explainability, fairness or operational use.
That’s why the best partners tend to combine four things.
1. Technical depth
Credit risk modelling is a specialist discipline. A good partner should have experience across the models and methods that are critical to your business, including:
- Application scorecards
- Behavioural scorecards
- Affordability models
- IFRS 9 models
- Collections and recoveries models
- Fraud detection models
- Propensity and customer value models
- Decisioning strategies
- Model monitoring and recalibration
They should also understand when to use simpler, transparent approaches and when more advanced methods are worth the extra governance effort.
Proof in practice"Jaywing's flexible approach and comprehensive track record in both traditional and AI risk modelling made it a sound partner for us to work with to boost the effectiveness of our existing models." — Dr J. Serradilla, Senior Data Modeller, Newcastle Building Society The project delivered an 18% uplift in predictive power on Newcastle Building Society's Buy-to-Let risk model and more than 4% on its residential mortgage model. |
2. Regulatory and governance experience
For banks, building societies, insurers and other credit providers, model performance is only part of the story. A good partner should understand the governance that surrounds the model: documentation, validation, monitoring, audit trails, explainability, and senior stakeholder sign-off.
This becomes even more important as firms explore AI and machine learning. Black-box models may be powerful, but power without transparency creates problems. Lenders need confidence that models are fair, robust, explainable and being used appropriately.
What clients look for"What made Jaywing stand out was their knowledge of ICAAP requirements, their client engagement approach and their commitment to deliver on all aspects of the plan, collaborating seamlessly with the internal teams." — Tim Blackwell, Chief Financial Officer, Hampshire Trust Bank |
3. Commercial understanding
A model is only useful if it supports better decisions. This might mean improving approval rates without increasing bad debt. Reducing fraud losses without adding too much friction. Improving collections outcomes. Strengthening affordability assessments. And supporting fairer, faster customer decisions.
Good modelling partners understand that technical performance and commercial performance need to work together.
4. Practical delivery
Plenty of models look impressive in a slide deck. The real test is whether they can be implemented, monitored and used by the people making decisions every day.
A strong partner should be able to work with internal risk, data, compliance, finance, operations and technology teams. They should understand the messy bits: data gaps, legacy systems, sign-off processes, stakeholder challenges and production timelines.
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Quick check: What good looks like A strong credit risk modelling partner should be able to show:
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If they can build the model, explain it, govern it and help get it into use, you’re probably having the right conversation.
5. Tailored support
Every credit provider is different. Its customers, products, appetite for risk, internal expertise and ambitions will shape what the right solution looks like.
A good modelling partner takes the time to understand all of that before recommending an approach. They listen, ask questions and work with your team to find the right answer for your organisation, rather than reaching for a standard solution.
A useful test is to think about how the relationship feels. Are they listening and working with you? Do they understand what you're trying to achieve? Are they bringing ideas that make sense for your business?
The best external partners should feel like part of the team, with enough independence to bring fresh thinking and challenge when it's useful.
10 questions to ask before choosing a credit risk modelling consultancy
Every consultancy will tell you they have experienced consultants, proven methodologies and a strong track record. The challenge is working out which ones can genuinely deliver. These questions can help separate marketing claims from real capability.
1. Have they delivered models that are currently being used?
Building a model is one thing. Building a model that survives validation, gains stakeholder buy-in and delivers value over time is another. Ask for examples of models that are actively supporting lending, fraud or customer decisions today.
2. Can they demonstrate measurable outcomes?
A good consultancy should be able to talk about more than model performance statistics. Ask what changed as a result of their work. Did approval rates improve? Were fraud losses reduced? Did collections performance increase? Were processes made more efficient?
3. Do they understand your regulatory environment?
A consultant might be technically brilliant and still struggle if they don't understand the expectations placed on your business. Whether you're a bank, building society, insurer or specialist lender, ask how they approach governance, validation, documentation and regulatory scrutiny.
4. How do they approach explainability?
As models become more sophisticated, explainability becomes more important. Ask how they balance predictive power with transparency. If they can't explain their approach in plain English, that's worth exploring further.
5. Can they support validation and independent challenge?
Model development is only part of the picture. Strong partners understand validation requirements and are comfortable having their work challenged. In fact, they should welcome it.
6. What happens after implementation?
Many projects look successful on launch day. The real test comes six, twelve or twenty-four months later. Ask how they approach monitoring, recalibration and ongoing performance management.
7. Who will actually be doing the work?
It's an obvious question, but one that's often overlooked. Will the work be delivered by the people you meet during the sales process? Or handed to a different team once contracts are signed? Ask to meet the people who will be involved day-to-day.
8. How do they transfer knowledge?
The best consultancies leave organisations stronger than they found them. Ask how they document their work, share expertise and support internal capability development.
9. What experience do they have beyond your sector?
Specialist sector knowledge matters. Fresh perspectives matter too. Consultancies that work across banking, lending, insurance, fraud and customer analytics often bring ideas that wouldn't emerge from a single sector alone.
10. Can they show evidence?
Look for case studies, outcomes, references, and results.
Good providers are usually proud to share it.
From Virgin Money, to Nationwide and Hitachi, here’s what good evidence looks like.
Procurement shortcut: 5 things to look forIf you're shortlisting providers, look for evidence of: ✓ Technical modelling expertise ✓ Regulatory and governance knowledge ✓ Experience in live production environments ✓ Measurable business outcomes ✓ Strong client relationships and repeat engagements |
The strongest partnerships are often built on expertise, transparency and the ability to solve real business problems.
Warning signs to watch for when choosing a credit risk modelling partner
Most providers will tell you about their strengths. Fewer conversations focus on the warning signs. The problem is that issues often don't become visible until a project is underway. By then, changing direction can be expensive, time-consuming and politically difficult.
Here are a few red flags worth paying attention to.
🚩 Red flag #1: Every problem has the same solution
Credit providers have different customers, products, data sources and objectives. A consultancy that immediately recommends the same methodology, technology or modelling approach for every situation may be bringing a template rather than a solution. Good consultants ask questions first.
🚩 Red flag #2: They can't explain their models clearly
Complexity isn't a sign of expertise. If a consultant struggles to explain how a model works, why a particular approach was chosen or what trade-offs were made, that's worth exploring further. The people building a model should be able to explain it to technical teams, business stakeholders and senior leaders alike.
🚩 Red flag #3: Governance is treated as an afterthought
Documentation, validation, monitoring and model governance aren't administrative tasks that happen at the end of a project. They're part of the project. A provider that focuses exclusively on model performance metrics may leave you with challenges further down the line.
🚩 Red flag #4: There is no discussion about implementation
A model only creates value when it is being used. Ask how the provider approaches deployment, monitoring, ongoing management and stakeholder adoption. If implementation barely features in the conversation, that's a concern.
🚩 Red flag #5: Success is defined by statistics alone
Model performance is key. Business outcomes are critical too. The best partners can explain how modelling decisions connect to approval rates, fraud losses, customer outcomes, operational efficiency or portfolio performance. Strong statistics should support better decisions.
🚩 Red flag #6: They have no examples of long-term client relationships
Some projects are deliberately short-term. Many are not. Ask how long clients typically work with them and what happens after delivery. Long-standing client relationships often tell you more than a polished case study.
A simple testWhen you're speaking to a potential modelling partner, ask yourself one question: Do they sound like people who want to build a model, or people who want to solve a problem? The distinction is key. The strongest partners never lose sight of the business challenge sitting behind the analytics. |
What good looks like
By this point, you might be wondering whether successful modelling programmes have anything in common.
They do.
The details vary from one organisation to another, but the strongest programmes tend to share a few characteristics.
They start with the business problem
The best modelling projects begin with a clear understanding of what success looks like. That could mean improving approval rates, reducing fraud losses, strengthening affordability assessments, improving collections performance or enhancing customer outcomes.
The objective comes first. The modelling approach follows.
They treat governance as part of the process
Governance works best when it's built into a programme from the start. Documentation, validation, monitoring and review shouldn't be squeezed in at the end of a project because somebody suddenly needs a sign-off pack. When governance is considered early, it tends to be faster, cleaner and far less painful for everyone involved.
They bring together different perspectives
Risk specialists, analysts, compliance teams, operations teams and decision-makers all have something valuable to contribute. Different perspectives often uncover issues, assumptions and opportunities that would otherwise go unnoticed.
They plan beyond implementation
Putting a model into production is an important milestone, but it isn't the final destination. Customer behaviour changes, products change, fraud patterns change, and economic conditions change. Successful organisations review performance regularly and make adjustments when the evidence suggests it's time.
They build capability along the way
The strongest partnerships leave organisations in a better position than when they started. Knowledge is shared. Teams gain confidence. Processes improve. Internal capability grows.
That's valuable long after a project has been completed.
What good collaboration looks like"Because this was done collaboratively, we have acquired a thorough understanding of the end result." — Rosemary Byde, Risk Infrastructure & Analytics, RBS |
Choosing the right credit risk modelling partner
To sum things up, the right partner should bring technical expertise, regulatory understanding, commercial awareness and delivery experience. They should be able to explain complex concepts clearly, challenge assumptions constructively and help turn analytical outputs into better business decisions.
For credit providers, insurers and other organisations that rely on risk-based decisioning, the quality of those decisions has a direct impact on growth, profitability, customer outcomes and operational performance.
That's why selecting a modelling partner deserves careful consideration.
Ask questions. Look for evidence. Speak to the people who will be doing the work. Focus on outcomes rather than promises.
The strongest partnerships are built on expertise, transparency and a shared understanding of the problem being solved.
Looking for a credit risk modelling partner?
Whether you're planning a model redevelopment programme, reviewing IFRS 9 models, strengthening fraud controls or exploring new modelling approaches, choosing the right partner can make a significant difference to the outcome.
Talk to our team to discuss your objectives, challenges and priorities. We'll help you understand what good looks like and where the biggest opportunities may lie.