The importance of quality data in AI risk modelling: How data benchmarking can help

Just because your firm can use your existing data for AI risk modelling doesn’t mean you should. There’s a perception that AI can create accurate predictions based on any data set. That’s not always the case. 

AI models are only as good as the data they're fed.

Quality data is key to effective AI risk modelling. Without it, even the most sophisticated AI systems can produce flawed results. This can lead to poor decision-making and increased risk exposure.

In this post, we'll explore why quality data matters so much in AI risk modelling. We'll also show you how data benchmarking can help you achieve better results.

The foundation of AI risk modelling: Quality data

What exactly do we mean by "quality" data for AI models? It's more than just having lots of information. Quality data is accurate, complete, consistent, and relevant to the risk factors you're assessing.

In risk modelling, quality data might include up-to-date credit scores, income information, payment histories, and other financial indicators. It should be free from errors, duplications, and inconsistencies.

However, ensuring data quality isn't always straightforward. Common issues include:

• Incomplete records: Missing information can skew your model's predictions.

• Outdated data: Financial situations change quickly. Using old data can lead to inaccurate risk assessments.

• Inconsistent formats: When data comes from multiple sources, it might not all be in the same format.

• Inaccurate information: Data entry or reporting errors can significantly impact your model's output.

These issues can have serious consequences. Poor data quality can lead to:

• ❌Inaccurate risk predictions

• ❌Missed opportunities

• ❌Increased exposure to financial risks

• ❌Regulatory compliance issues

For example, if your AI model is working with outdated credit scores, it might underestimate the risk of lending to a customer whose financial situation has recently deteriorated. Or, if income data is inconsistent across different sources, your model might struggle to accurately assess a customer's ability to repay a loan.

That's why focusing on data quality is crucial. High-quality data allows your AI models to make more accurate predictions, leading to better risk management decisions. It's not just about having more data—it's about having the right data.

The challenges of ensuring data quality for AI risk models

Maintaining high-quality data for AI risk models is no small feat. Let's look at some of the main challenges:

Volume and variety of data sources

AI models often use data from numerous sources. These might include credit bureaux, bank records, public databases, and even social media. Each source has its own format and update frequency. Integrating all this data consistently is tricky. 

Data consistency across multiple bureaus

Credit bureaux are a key source of data for risk modelling. However, information can vary significantly between bureaux. For instance, one bureau might have a more recent update on a customer's credit score than another. Or they might calculate credit utilisation differently. These inconsistencies can lead to conflicting risk assessments.

Balancing data completeness with cost-effectiveness

More data often means better predictions. But unless you have the right levers, it also might mean higher costs. Unless you use data benchmarking, each additional data point or source may come with a price tag.

Keeping data up-to-date

Financial situations can change rapidly. Data that was accurate last month might be outdated today. Constantly refreshing data is essential for accurate risk modelling. But it can also be resource-intensive.

Ensuring data accuracy

Even with reliable sources, errors can creep in. These might be due to data entry mistakes, reporting delays, or technical glitches. Catching and correcting these errors is crucial. 

Complying with regulations

Using personal data for risk modelling comes with regulatory responsibilities. You need to ensure you're collecting and using data in compliance with laws like GDPR. This adds another layer of complexity to data management. You need to balance the need for comprehensive data with respect for privacy and regulatory requirements.

How data benchmarking improves AI risk modelling

Data benchmarking is a powerful tool that can help overcome many of the challenges we've discussed. But what exactly is it, and how does it help?

What is data benchmarking?

Data benchmarking is the process of comparing your data against industry standards or best practices. It's like a health check for your data. You're assessing its quality, completeness, and cost-effectiveness against what's available in the market.

Benefits of data benchmarking for AI risk modelling

1. Identifying the most reliable data sources

Data benchmarking helps you pinpoint which sources provide the most accurate and up-to-date information. This is crucial for building reliable AI risk models. By comparing data from different sources, you can spot inconsistencies and determine which sources you can trust. This leads to more accurate risk assessments and better decision-making.

2. Ensuring data consistency

When you benchmark your data, you're not just looking at individual sources. You're also checking how well different sources align with each other. This process helps identify discrepancies between data sources. You can then take steps to reconcile these differences, leading to more consistent and reliable data for your AI models.

3. Optimising data costs

Data benchmarking gives you a clear picture of what you're paying for data compared to market rates. This insight is invaluable for managing costs. Like many of our clients, you might discover you're overpaying for certain data sets, or that there are more cost-effective sources available. This knowledge allows you to optimise your data spending without compromising on quality, even if you choose to stay with the same provider.

4. Improving data coverage

Through benchmarking, you might uncover gaps in your data. Perhaps you're missing key information that your competitors are using in their risk models. Identifying these gaps allows you to expand your data coverage strategically. You can focus on acquiring the most valuable missing data, rather than blindly accumulating more information.