“Under the Hot Rod Hood”: Dr. Scott Zoldi’s AI Series on LinkedIn Live
Season 2 just started! In case you missed Season 1, here's a recap of Scott's conversations with Jordan Levine and Cortnie Abercrombie on Artificial Intelligence (AI).
Deep Dive into Distribution of the FICO® Score Across the US
Recent increase in the average FICO® Score is reflective, in part, of impacts that the pandemic has had on U.S. consumers’ credit behavior, credit report, and credit history.
Explainability in Fraud Management — What to Focus On Now
Explainability in fraud management covers everything from how fraud systems work to how their models are trained to how they are fueled by the right data
The United Nations International Day of Women and Girls in Science throws a spotlight on creating greater diversity, and it is especially important to get more women in AI
FICO Fact: How Alternative Data Enhances the Accuracy of Consumer Credit Profiles
Developed by FICO in partnership with LexisNexis Risk Solutions and Equifax, FICO® Score XD 2 utilizes alternative data—data not included in the traditional credit bureau file
Los 5 temas principales sobre puntuaciones del 2021
En nuestras mejores publicaciones sobre FICO Scores analizamos el gran alcance del conjunto FICO® Score y disipamos algunas ideas equivocadas comunes en cuanto a FICO® Scores.
Las 5 mejores publicaciones sobre gestión de decisiones de 2021
The 5 top posts last year dealt with digital transformation in insurance, automated originations, responsible AI, model governance and, strangely enough, movies!
Gestión de clientes vulnerables: cómo puede ayudar la nueva analítica
Las interacciones con clientes vulnerables ofrecen información fundamental que puede transformar los resultados comerciales y el rendimiento, además de impulsar la lealtad y retención a largo plazo
Combatir el sesgo: cómo las características latentes interpretables eliminan el sesgo en las redes neuronales
FICO’s data science labs called interpretable latent feature-based neural networks (ILF NNET) help expose and eliminate biases derived from data showing up in the model.
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