Use cases

Protect Customers. Improve Efficiency. Stay Compliant.

Industries from banking to e-commerce are struggling with ever-growing threats of fraud. AI is the perfect solution, but a lack of transparency hinders adoption. So, what's needed? AI that’s accurate and transparent at the same time,
AKA: Explainable AI. With visibility and insights into the ‘why’ behind fraudulent transactions, optimize costs, improve productivity, and increase revenue.

Build Customer Trust
Reduce false positives, provide customer protection guidelines, and foster
customer trust.

Reduce Costs
Resolve alerts faster, reduce costs in manual reviews, and increase robustness of fraud detection models.

Stay Compliant
Reduce the number of suspicious activity
reports (SARs) and ensure
compliance regulations.

Challenges in fraud detection

Excessive time and effort to identify fraud - increased costs, decreased productivity, and sub-par results.

Technology is not transparent. Human oversight is limited. Compliance becomes a challenge.

False positives affect customer trust because of a lack of information on the ‘why’ behind fraudulent cases.

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Lending prediction risk – Explanation chart

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