6 Steps to AI Adoption

One of the most powerful use cases to emerge for AI/ML is credit risk underwriting. Machine learning-based models are more accurate at predicting default risk and credit eligibility by generating a more holistic view of an applicant. For lenders, ML models boost approvals with no added risk. With more confidence about whom to say yes to, ML also drives higher levels of auto-decisioning.

While AI adoption in banking and lending is on the rise, AI projects can struggle to get from proof of concept to production. With AI project failure rates nearing 50%, the need to follow a proven roadmap is critical.

Download the infographic to learn how banks and credit unions are successfully moving to AI-powered underwriting.