14/05/2025

AI in Credit Management: 3 Ways to Avoid Bias

We've written before about the benefits of AI in finance and what it can do for your career, but we also know you shouldn’t blindly trust an AI tool without putting a lot of thought into how it will best serve your business. AI is an amazing tool, but it’s not a perfect tool. One study by USC found that up to 38.6% of things stated as fact by AI tools were actually biased.

Combatting bias in AI can feel like a big job: how are you supposed to fix something as massive as artificial intelligence? But the truth is, you can’t afford not to make sure your AI finance tools are as unbiased as possible. Putting in that work not only means your AI tool will make better calls for your business, it also could protect you from reputational damage down the road.

Gather unbiased data
Clearly articulate your AI model
Use AI in credit management wisely
Conclusion

1. Gather unbiased data

It’s not just important that your data is unbiased; it’s a crucial business strategy. Diverse data works across demographics, regions and economic situations. If, for example, your credit decisioning software was set up only with data from tech-savvy companies from major cities, it might miss the mark in assessing creditworthiness for companies in more rural areas. The algorithm could incorrectly see a business as too risky, simply because their finances don’t fit the narrow model built on your dataset

To gather unbiased data, consider:

Expanding your data sources: Include data from a wide rage of industries, economic sectors, demographic backgrounds and geographies. The more sources of data you use, the wider the perspective of your decisioning software – which in turn opens your business up to a wider range of customers.
Engaging with wider communities: Partner with organizations that serve underrepresented communities to gather insights your current data might overlook.
Auditing data regularly: Just because AI is here to help make our lives easier, that doesn’t mean that your data strategy should be “set it and forget it.” Continuously monitor and audit your data sources and look for areas that are over-represented, where bias could be creeping in.

2. Clearly articulate your AI model

It’s true of relationships, business… and AI: communication is key. Everyone should be involved to understand how your AI model comes to its conclusions. If the process is too opaque, it’s more difficult to combat bias. Remember sitting in high school math class and not understanding a word the teacher was saying? (Just me?) If you don’t understand the thought behind the model, you have very little ability to spot bias.

When you create your model, think about:

Explainability: To put it bluntly, people can’t question what they can’t even talk about. Simple models, like decision trees or explanations in plain, non-technical language, means more people are able to understand – and critique – the AI model.
Documentation: You should keep detailed records about how your models are built. Make sure to include information about the datasets used and the rationale behind model selection. The more people know about the model, the more they’ll trust it.
Visualizations: Who doesn’t love a good pie chart? (Okay, maybe I’m just a nerd). Visual tools are an excellent way to showcase how different variable will impact the outcome of your AI model. And that makes it easier for stakeholders and teams to grasp complex relationships within your data.

3. Use AI in credit management responsibly

Have you noticed that AI seems to be… well, just about everywhere lately? And while it can be incredibly helpful when you’re trying to explain complex data structures, I’m sure you’ve thought “why does that need to have AI?” about a product lately.

AI in credit management can be great for:

Risk assessment: Where AI has humanity beat is in the sheer amount of data it can process and analyze in the blink of an eye. That gives your business a leg up in identifying potential customer and business risks, like late payers or businesses likely to go bankrupt, well before they become a long-term issue.
Customer insights: Wouldn’t it be nice if you had a crystal ball that told you exactly what your customers would be looking for from you in the future? Well, we’re not quite there yet, but AI gets you pretty close. Analyzing your customers’ behavior not only helps you avoid late payers or businesses struggling financially, it can also give you an idea of what they’ll need from you going forward.
Automated processes: Our research has shown that 58% of sales professionals spend between one and four hours each day on manual sales data entry tasks. Automating those processes not only reduces the risk of human error, it also frees up hours of your team’s time to focus on big-picture goals.
Fraud detection: Fraud is as old as finance itself, but AI can help you sniff out suspicious activities from the jump. By continuously learning from patterns, AI can alert your team to potential fraud more quickly than traditional, manual methods.

In conclusion

AI can be a game-changer for your business' credit management processes. But just because it seems to be everywhere doesn't mean there's a "one size fits all" strategy. Avoiding bias in AI is key for a robust credit management program, so make sure you diversify your data, communicate clearly and use AI responsibly.

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Chapter 1

AI in Credit Management: 3 Ways to Avoid Bias

AI in Credit Management: 3 Ways to Avoid Bias

Table of Contents

1. Gather unbiased data

2. Clearly articulate your AI model

3. Use AI in credit management responsibly

In conclusion

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