Use cases

Increase revenue. Stay compliant. Build responsibly.

Today, financial services companies leave money on the table by restricting themselves to simple underwriting models. With AI models, businesses can save millions of dollars by reducing the number of bad loans, finding new customers, and increasing revenue. Explainable AI is the way forward: with visibility and insights into the ‘why’ behind underwriting decisions, teams ensure underwriting models produce responsible results.

Deploy efficiently
Deploy efficiently and produce human-readable explanations accessible to users, regulators,
and risk managers.

Save money
Reduce bad loans. Save millions of dollars
by increasing the accuracy of models
using explainability.

Build trust and loyalty
Use explanations to make end-user recommendations on how to change their
loan outcomes.

Challenges in loan underwriting

Use of advanced AI is limited due to the black-box nature of AI models. This results in bias and unfairness risks.

Model Risk Managers have limited tools to review AI models and effectively assess the risks - why are there errors?

Limited transparency for end users on corrective actions. How can we enable more people to get loans?

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Lending prediction risk – Explanation chart

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Want a demo of how Fiddler can enable you to use trustworthy AI for underwriting models? Understand how some of our existing customers are using Fiddler's Explainable AI Engine to deploy efficient underwriting models, increase revenue, and build trust and loyalty with customers.

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