Credit scoring sits at the core of any lending activity. But in its traditional form, it comes with a simple limitation: it is too costly to scale efficiently for SMBs.

For decades, this cost has shaped the market:

• Large enterprises are well served
• SMBs remain partially excluded

Not because they are riskier, but because they are harder to analyze in a cost-effective way.

Today, this model is shifting.

Open banking credit scoring, automation, and new scoring models are drastically reducing the cost of risk analysis. What used to be a bottleneck is becoming a growth lever.

For lenders, the challenge is no longer just to assess risk—but to do it instantly, at scale, and reliably.

3 key takeaways

• Traditional credit scoring mechanically limits SMB access to financing due to cost and complexity
• Open banking credit scoring and automation enable faster, more accurate, and scalable risk analysis
• New scoring models are turning SMB lending into a market that is finally addressable at scale

Credit scoring: definition and role in lending

Credit scoring refers to all the methods used to assess a borrower’s ability to repay a loan. In practice, it means transforming financial and operational data into a decision: lend or not lend.

A central function in credit decisions

Scoring is involved in every financing decision. It defines both the level of risk accepted and the conditions offered.

• It evaluates the probability of default
• It determines credit parameters (amount, duration, pricing)
• It enables partial or full automation of decisions

Without a robust scoring model, risk is poorly calibrated, losses increase, and portfolio growth is constrained.

A direct impact on lenders’ economics

Credit scoring is not just about managing risk—it directly impacts profitability.

Accurate scoring allows better selection of applications and pricing aligned with real risk, while reducing per-case analysis costs.

Conversely, slow or inaccurate scoring increases operational costs, limits processing capacity, and restricts the ability to serve segments like SMBs.

From traditional scoring to modern scoring models

A scoring model refers to the architecture used to generate this assessment. Historically, these models relied on:

• Fixed rules
• Standardized financial data
• Often static analysis

But they are evolving rapidly, with real-time data integration, open banking, and advanced algorithms.

We are moving from point-in-time scoring to continuous, dynamic scoring that reflects real business activity.

This shift is enabling a deep transformation of SMB lending.

Open banking credit scoring: toward real-time risk analysis

The rise of open banking has fundamentally changed how lenders access information. Where scoring once relied on static, self-reported data, it can now leverage real-time financial flows.

This is open banking credit scoring.

Direct access to operational data

Open banking allows lenders, with borrower consent, to access much richer financial data, including:

• Bank transaction flows (inflows and outflows)
• Detailed transactional data
• Connections to accounting or ERP systems

Unlike traditional approaches, data is instantly available and reflects actual business activity—not just declarations.

Scoring becomes much closer to operational reality.

A dynamic view of risk

With this data, scoring is no longer fixed in time. It evolves continuously based on business activity.

• Rapid detection of cash flow variations
• Identification of late payments
• Analysis of business cycles

Risk is no longer assessed once—it is continuously monitored.

A massive reduction in friction

Open banking also simplifies the borrower experience. Fewer documents are required, thanks to direct data connections and automated processes.

This enables:

• Faster onboarding
• Lower drop-off rates
• Higher processing volumes

Scoring becomes not only more accurate, but also more accessible.

AI and automation: making credit scoring scalable

If open banking improves data quality, AI for credit scoring and automation transform how that data is used. Together, they redefine the core of underwriting.

Credit scoring becomes fast, scalable, and economically viable for SMBs.

Automating the entire underwriting process

Modern technologies can automate most key steps:

• Data collection and aggregation via APIs
• Automated document verification
• Risk analysis using algorithms
• Near-instant decision-making

What used to take days or weeks can now be done in seconds.

Drastically reducing operational costs

Automation changes the economics of lending:

• Less human intervention
• Fewer manual errors
• Significantly lower fixed costs per case

As a result:

• Marginal analysis cost approaches zero
• Small-ticket lending becomes profitable
• SMBs become a truly addressable segment

This is a structural shift for lenders.

Improving both quality and speed

Contrary to common belief, automation does not reduce quality. AI-based models can analyze more data, faster.

• More effective fraud detection
• Better handling of atypical cases
• Continuous model learning

This leads to lower default risk, broader eligibility, and improved portfolio performance.

AI score credit systems outperform traditional approaches in both speed and accuracy.

Toward new scoring models: beyond individual analysis

Despite progress, most scoring models still focus on a single entity: the company itself. But no company operates in isolation.

Its risk also depends on its ecosystem.

Integrating the ecosystem into scoring models

The most advanced alternative credit scoring models now include a new dimension: economic relationships.

• The company’s customers
• Key suppliers
• Financial flows between these actors

The goal is simple: understand not just the company’s health, but also that of its ecosystem.

Risk becomes relational, not just individual.

A much more complete view of risk

This approach corrects two major biases of traditional models:

• A company may look strong but depend on fragile partners
• A young SMB may be reliable if connected to strong counterparties

By integrating these elements, scoring becomes more accurate, reducing false positives and negatives.

Toward “augmented” scoring

We are moving from static, isolated models to ones that integrate:

• Dynamic data
• Economic relationships
• Weak signals

This evolution opens access to credit for profiles historically excluded.

Scoring becomes a more faithful representation of economic reality.

The Defacto scoring model: a network-based approach to risk

Defacto pushes this logic further with a scoring model based on business networks. Instead of analyzing only the borrower, it maps the entire ecosystem.

Each relationship becomes a signal of risk.

• Mapping economic relationships. The model reconstructs each company’s network using transactional, accounting, and public data—identifying clients, suppliers, and dependencies.
• Scoring each counterparty. Each actor is evaluated individually, with its own risk level, providing a deeper understanding of the environment.
• Improving credit decisions. By integrating relational data, scoring becomes more precise and nuanced—detecting risks invisible to traditional models while enabling financing for strong but underserved SMBs.

Scoring becomes fairer—without increasing risk.

What this changes for lenders

These evolutions are not just technical—they directly transform lending economics and operations.

Credit scoring becomes a growth driver, not a constraint.

Serving SMBs at scale

With automated, enriched, real-time scoring:

• Analysis costs drop
• Processing volumes increase
• SMB lending becomes profitable

What was once marginal becomes scalable.

Improving portfolio quality

Richer scoring leads to better selection:

• Less fraud
• Fewer defaults
• Better capital allocation

Risk is better understood—and better managed.

Unlocking new product opportunities

With real-time, flow-based scoring:

• Instant eligibility checks
• On-demand financing
• Continuous customer monitoring

This enables:

• Dynamic credit lines
• Embedded financial products
• Seamless user experiences

Credit scoring becomes infrastructure.

Credit scoring is becoming infrastructure—not a bottleneck

For a long time, credit scoring limited SMB financing. Not because risk was too high, but because analyzing it was too expensive.

That is changing.

Open banking credit scoring, automation, and modern scoring models now make it possible to:

• Access richer data
• Analyze risk in real time
• Drastically reduce operational costs

For lenders, the challenge is no longer to improve existing systems, but to rethink the role of scoring within their architecture.

In this context, SMB financing is no longer a constraint. It becomes a scalable opportunity—provided the right models and technology are in place.

FAQ: Credit scoring and scoring models

What is the difference between credit scoring and underwriting?

Credit scoring is part of underwriting. It focuses on risk evaluation, while underwriting covers the entire decision process (analysis, structuring, validation).

What does open banking credit scoring change in practice?

It provides access to real-time financial data directly from bank accounts and management tools, making scoring faster, more accurate, and less dependent on static documents.

Are modern scoring models riskier?

No, they're generally more accurate. By incorporating more data and dynamic signals, they better detect real risks and reduce evaluation errors.

Can credit scoring be fully automated?

In most cases, yes. Modern technologies allow automation of:

• Data collection
• Verification
• Risk analysis
• Decision making

Human oversight may still be relevant for complex cases.

Why have SMBs historically been underserved?

Mainly due to operational costs. Analyzing an SMB costs almost as much as a large company but generates less revenue—limiting their access to financing.

What is the benefit of real-time scoring for lenders?

It enables:

• Faster decision making
• Higher processing volumes
• More flexible products
• Embedded finance experiences