Maximising AI risk model performance with multi-bureau data

Many financial institutions shy away from multi-bureau strategies, fearing complexity and cost. What if we told you that not only is a multi-bureau approach achievable, but it can also significantly enhance your AI model performance while potentially reducing costs?

In this post, we'll show you how to maximise your AI's potential with multi-bureau data, debunk common myths, and reveal strategies for optimising both performance and cost.

The AI and multi-bureau data synergy

It’s fair to say that AI and multi-bureau data are a match made in heaven for risk modelling. But why? Here are three distinct synergies:

1. Diverse data sources

AI models thrive on data—the more diverse and comprehensive, the better. Each bureau brings its unique perspective, filling in gaps and providing a more complete picture of a customer's financial behaviour.

For instance, while one bureau might have detailed information on a customer's credit card history, another might have better visibility into their mortgage payments. By combining these insights, your AI can make more nuanced and accurate risk assessments.

2. Tackling the inconsistency challenge

"But wait," you might say, "what about inconsistencies between bureaux?" You're right to ask. We’ve talked about this at length in previous posts. Data discrepancies across bureaux are common and can be a headache—but this is where AI really shines.

AI algorithms can be trained to identify and reconcile inconsistencies across multiple data sources. They can weigh the reliability of different data points, consider the recency of information, and even learn from past discrepancies to make better judgments in the future.

3. Pattern recognition across data sets

AI's superpower is its ability to identify complex patterns that humans might miss, and this ability is supercharged when working with multi-bureau data.

By analysing patterns across multiple data sets, the right AI models can:

• Identify early warning signs of financial distress that might only be visible when looking at a customer's complete financial picture

• Uncover new segments of creditworthy customers who might be overlooked by traditional, single-source models

• Detect sophisticated fraud attempts by cross-referencing information across bureaux

In essence, multi-bureau data can make your AI more perceptive, more nuanced, and ultimately, more valuable to your business.

But how do you implement this powerful combination without breaking the bank or getting caught up in black box interpretability issues?

Overcoming the black box issue in multi-bureau AI implementation

It’s not uncommon for AI models to be black boxes. You feed in data, and out comes a decision, but the path from A to B isn't always clear.  In the world of financial services, where regulators and customers alike demand transparency, this can be a huge problem. 

Here's how Consultant, Nick Sime, at Jaywing suggests you can address it:

“Choose models that balance complexity with interpretability. Develop clear documentation of model logic and decision-making processes. Work closely with regulators to ensure compliance with existing frameworks. Implement explainable AI techniques to make model decisions more transparent.”

💡Jaywing has created an AI risk modelling maturity model where you can see where your firm sits on the curve. It’s worth taking a look and seeing what your baseline is and the steps you can take to plan towards advanced and fully explainable AI risk modelling. 

Strategies for optimising AI models with multi-bureau data

Now that we've tackled the biggest challenge, let's get to the exciting part: making your AI models more accurate with multi-bureau data. 

#1. Leverage data benchmarking for optimal bureau selection

Not all data is created equal, and not all bureaux will provide equal value for your specific needs. This is where data benchmarking comes in.

It helps you:

• Identify which bureaux provide the most accurate and relevant data for your target market

• Understand the strengths and weaknesses of each bureau's data

• Make informed decisions about which data sources to prioritise

By benchmarking, you ensure you're feeding your AI models the best possible data, setting them up for success from the start.

#2. Implement a waterfall approach in AI models

A waterfall approach can significantly boost your model's efficiency and cost-effectiveness. Here's how it works:

1. Start with your primary data source (identified through benchmarking)

2. If the primary source doesn't provide sufficient information, move to the secondary source

3. Continue down the 'waterfall' until you have enough data to make a confident decision

This approach allows you to balance comprehensiveness with cost-efficiency. You're not paying for data you don't need, but you're still getting a complete picture when necessary.

#3. Use AI for data reconciliation and anomaly detection

Let AI do what it does best: find patterns and anomalies in vast amounts of data. Use your models to:

• Automatically reconcile discrepancies between bureau data

• Flag unusual patterns that might indicate fraud or rapid changes in creditworthiness

• Identify data quality issues that might be affecting your assessments

This improves your risk assessments and helps maintain the quality of your data over time.

#5. Enhance model accuracy through cross-bureau validation

Use data from multiple bureaux to validate and refine your models. For example:

• Train your model on data from one bureau and validate it against others

• Use ensemble methods that combine predictions from models trained on different bureau data

• Regularly test your models against all available bureau data to identify blind spots or biases

This cross-validation approach can significantly improve your model's robustness and accuracy.

By implementing these strategies, you're allowing your AI models to draw insights from a rich, diverse data landscape while maintaining efficiency and cost-effectiveness.

Next up, we'll tackle a question that's likely on your mind: How do we make all this work within our budget? 

Cost considerations and ROI of multi-bureau AI models

Now, let's talk money. It's the elephant in the room when it comes to multi-bureau AI models. Many firms shy away from this approach, fearing sky-high costs. But is this fear justified? Let's crunch some numbers and debunk some myths.

Debunking the myth of prohibitive costs

First things first: yes, using multiple bureaux does mean more data, which typically means more cost. But here's the kicker—it doesn't have to break the bank. In fact, with the right approach, it can even save you money. How?

• Reduced false positives and negatives: More accurate models mean fewer costly mistakes.

• Optimised data usage: You're not paying for data you don't need.

• Improved operational efficiency: Better decisions mean less time spent on manual reviews.

Remember, the goal isn't to use all the data all the time. It's about using the right data at the right time.

Strategies for negotiating favourable multi-bureau contracts

Here are our top tips:

1. Use benchmarking data: Know what others are paying and use this in your negotiations.

2. Bundle services: Often, bureaux will offer better rates for multiple services.

3. Negotiate flexible terms: Look for contracts that allow for volume fluctuations without penalties.

4. Consider a 'pay-per-use' model: This can be more cost-effective than fixed-volume contracts for some businesses.

Remember, bureaux want your business. There's often more room for negotiation than you might think.

Unlock the power of multi-bureau data in AI risk modelling

As we've explored throughout this post, the synergy between AI and multi-bureau data is transforming risk modelling. Let's recap the key takeaways:

• Comprehensive insights: Multi-bureau data provides a 360-degree view of customer creditworthiness, enabling more accurate risk assessments.

• Enhanced model performance: AI thrives on diverse data, using it to uncover subtle patterns and make more nuanced predictions.

• Cost-effective implementation: Contrary to common belief, a multi-bureau approach can be cost-effective when implemented strategically.

• Competitive advantage: By leveraging multi-bureau data, you can make faster, more accurate decisions, potentially opening up new market segments.

• Future-proofing: As the financial landscape evolves, a multi-bureau approach positions you to adapt quickly to new data sources and regulatory changes.

However, maximising the benefits of multi-bureau data isn't just about accessing more information—it's about accessing the right information in the most efficient way. This is where data benchmarking becomes crucial.