Synthetic Data vs Authentic Data in Credit

It’s well known that the significant lack of business data, specifically trade payment data available is causing more than a few headaches for Credit Bureau, Credit Teams and lenders in general.

This is exasperated by the lack of data sharing platforms to level out the playing field and monopolistic approach to data currently adopted by industry.

Personally, I would say fear and commercials drive this.

I find myself scratching my head at the use of Synthetic Data in assessing credit risk, one of the many use cases available through Experian from what I can tell.

DEFINITION: Synthetic data is information that is artificially generated rather than produced by real world events.

If there is not enough data by industry, state, region etc. to generate an authentic reflection of a businesses credit risk, how would AI synthesise this to generate a synthetic credit risk profile? 🤷‍♀️

Would you use synthetic data to assess credit approval in business?

This blog explores the pros, the cons and the alternatives. 

Let’s dive in. 

Why has synthetic data become a ‘thing’ in credit? 

Here’s the challenge: a scarcity of comprehensive trade payment data. 

This shortage is hampering accurate credit risk assessment, leaving credit bureaus, lenders, and credit teams struggling to make informed decisions. To compound the issue, the lack of robust data sharing platforms has created an uneven playing field, dominated by a few data monopolies.

In response to this data drought, synthetic data has emerged as a potential solution, promising to fill these information gaps. However, this artificial alternative raises important questions about reliability and authenticity in credit risk evaluation.

Let’s dive into what synthetic data really is and why it’s gaining traction.

What is Synthetic Data ?

Well, imagine if you could clone your most reliable customers’ payment patterns – that’s synthetic data in a nutshell. 

It is artificially generated information designed to mimic the statistical properties and patterns of real world data. 

Unlike authentic data that comes from actual transactions (you know, the ones where real money changes hands), synthetic data is whipped up using some pretty fancy AI and machine learning techniques. One popular method involves using Conditional Tabular Generative Adversarial Networks (CTGANs) – a sophisticated approach to generating data that closely resembles real world information…..apparently.

So, why are some in the industry turning to synthetic data? Let’s explore the purported benefits?

Why Synthetic Data ?

Now, you might be wondering why some people, like the crew at Experian, are getting all excited about synthetic data.

Proponents of synthetic data in credit risk assessment claim several benefits:

1. Overcoming data scarcity by creating additional data points
2. Balancing datasets to improve model accuracy
3. Simulating hypothetical scenarios to stress-test risk models
4. Enhancing privacy compliance by working with anonymized data
5. Facilitating data sharing across organisational and geographical boundaries
6. Addressing bias by generating more diverse and representative datasets

Sounds like a dream come true for data hungry credit teams, right? 

Well, we’re not sure about that. Can synthetic data truly capture the nuances and complexities of real world credit behaviours?

After all, we’re talking about using artificially generated data for high stakes financial decisions. That’s not something to be taken lightly, trust me.

Let’s take a good hard look at what we’re dealing with here.

Challenges and Limitations of Synthetic Data in Credit Risk Assessment  

Now, before we get too excited about synthetic data, let’s take a look at some of the challenges it faces:

1. Perpetuating or amplifying biases: Synthetic data is a bit like a copycat, potentially picking up and amplifying the biases hidden in its source data. This could lead to unfair credit decisions, especially for under-represented groups or non-traditional businesses.
2. Difficulty simulating complex market dynamics: Real markets are as unpredictable as Melbourne weather, with countless factors at play that synthetic data might struggle to capture. This means it could miss crucial nuances that impact credit risk, especially when it comes to sudden economic shifts or global events.
3. False sense of data completeness: Having heaps of synthetic data might make us feel like we’ve got all the bases covered, but it could lead us to overlook critical gaps in our information. We might end up putting too much faith in our synthetic models and forget to seek out real, authentic data.
4. Lack of real-world validation: Synthetic data is only as good as its source material and the algorithm used to create it. Without regularly checking it against real world outcomes, our synthetic data models might start drifting away from reality.

These limitations underscore the importance of approaching synthetic data in credit risk assessment with a healthy dose of scepticism.

What about ethics? 

This is not just about crunching numbers – it’s about doing right by people and businesses.

Organisations need to be upfront about their use of synthetic data. 

• No smoke and mirrors here. 
• Synthetic data needs to toe the line with financial regulations and data protection laws. 
• Models should be clear (but tracing decisions back to synthetic sources might be trickier than finding a needle in a haystack). 
• We can’t have synthetic data tipping the scales unfairly. We’ve got to keep a keen eye on any biases this synthetic data might be bringing to the party.  
• We need rock solid safeguards to prevent any funny business.

So, what’s the alternative? Well, let’s talk about the tried-and-true foundation of credit decision-making…

The Case for Authentic Data in Credit 

Authentic data, derived from real-world transactions and behaviours, forms the bedrock of reliable credit decision-making. 

Unlike synthetic alternatives, genuine trade-credit data captures the true dynamics of business interactions, creditworthiness and economic realities.

The value of authentic trade-credit data in assessing business health cannot be overstated. It provides:

1. Actual payment behaviours: Revealing how businesses manage their financial obligations over time.
2. Industry-specific insights: Reflecting sector-specific trends and challenges.
3. Economic indicators: Offering real-time glimpses into broader economic conditions.
4. Company-specific nuances: Capturing unique circumstances that may impact creditworthiness.

But that’s not all. 

Authentic data goes above and beyond in providing a true reflection of credit risk.

For starters, authentic data incorporates real world complexities that synthetic data might miss. It’s not just working from a script – it’s improvising with all the messy, multifaceted factors that influence credit risk in the real world.

When it comes to market volatility, authentic data is always on its toes. It naturally adapts to changing economic conditions, giving you a real-time view of the financial landscape. And let’s face it, in today’s world, that landscape can shift faster than a kangaroo on a hot tin roof.

But here’s where authentic data really shines: it captures those unexpected events, those outliers and anomalies that can make or break a business. These are the curveballs that synthetic data might not see coming.

Finally, and perhaps most importantly, authentic data builds trust. 

When you’re making decisions based on real, verifiable data, you can explain and defend those decisions with confidence.

While synthetic data may promise to fill gaps, authentic trade-credit data remains the gold standard for accurate, reliable, and transparent credit risk assessment. It grounds decisions in reality, providing a solid foundation for financial risk management.

So, what’s the bottom line here? Let’s take a look at how we can make the most of authentic data…

The Role of Data Sharing Platforms in Credit

Data sharing platforms offer a compelling solution to the challenges of data scarcity in credit risk assessment. First things first, what exactly is a data sharing platform? 

DEFINITION: A secure digital ecosystem where businesses can contribute and access trade credit information, all while maintaining control over their data.

By promoting collaboration and transparency, data sharing platforms offer an authentic alternative to synthetic data, addressing scarcity while maintaining data integrity. Here’s why:

• These platforms pool trade credit data from multiple sources, significantly expanding available information. This collective approach fills gaps in individual datasets, providing a more comprehensive view of business credit behaviours. The result? A richer, more diverse dataset that enhances the accuracy of credit risk models.
• Well-designed platforms tackle fears around data sharing head-on. They ensure robust security measures and clear usage policies, demonstrating the mutual benefits of participation. By facilitating controlled, anonymised sharing where necessary, these platforms strike a balance between transparency and commercial sensitivity.
• Democratised access to credit information puts a dent in data monopolies. Smaller lenders and businesses can now access more comprehensive datasets, enabling fairer competition. This equitable access is a breeding ground for innovation in credit products and services.
• Shared data offers a panoramic view of credit landscapes, boosting overall risk management. 

It’s a win-win situation that’s hard to ignore.

So, where does this leave us? 

Let’s recap:

Synthetic data promises to fill gaps in credit risk assessment, but it comes with significant challenges. It might perpetuate biases, struggle with market complexities, and create a false sense of data completeness. On the flip side, authentic data provides a true reflection of credit risk if you have enough of it, capturing real world nuances that synthetic data might miss.

The key takeaway? 

We need to approach new technologies like synthetic data with a critical eye. It’s not about rejecting innovation, but about understanding its limitations and potential impacts on our decision making.

Here’s the kicker: data sharing platforms offer a compelling alternative. They address data scarcity while maintaining the integrity of authentic data.  

Curious about how you can leverage authentic data for better credit decisions? 

Connect with me on LinkedIn for a chat about how credit data can transform your risk assessment.

So, what’s your take? Would you bet your business on synthetic data, or would you rather tap into the power of shared, authentic credit information?

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