Un análisis del perfil de riesgo en la dinámica de préstamos persona a persona mediante clústeres de K-medias
DOI:
https://doi.org/10.24275/uam/azc/dcsh/ae/2023v38n99/MotaPalabras clave:
LendingClub, Fintech, préstamos persona a persona, Clústeres de k-medias, riesgo de créditoResumen
Se analiza de manera empírica la base de datos de la Fintech estadounidense LendingClub, la empresa precursora del mercado de préstamos digitales persona a persona. Se busca contextualizar al modelo de negocio a través de su base de datos y las variables implicadas en el perfil de riesgo de los participantes. Se toman cuatro ventanas de tiempo para capturar ciclos económicos y su impacto en el perfil crediticio de los prestatarios. Para identificar los perfiles de riesgo de manera analítica, implementamos la técnica de clústeres de K-medias, definiendo un patrón de segmentación de participantes en función de la tasa de interés de los préstamos y la calificación FICO (Fair Isaac Company). Los patrones de agrupación son constantes a lo largo de los periodos estudiados y permiten determinar niveles promedio de ingreso, tipos de préstamos adquiridos y proveniencia geográfica según perfil de riesgo. Pocos estudios contemplan toda la información disponible de la operación de LendingClub (2007-20203T); por lo tanto este estudio también identifica la transición a la madurez de este modelo de negocio. Este trabajo logra dos objetivos: demostrar la evolución de los clientes y la plataforma a través del tiempo en cuanto se refiere a los perfiles de riesgo de los participantes prestatarios, y destacar el uso de clústeres de K-medias como una herramienta apropiada para el perfilamiento frente a otras alternativas analíticas.
Clasificación JEL: C24; G23; O16.
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Derechos de autor 2023 Pamela Moncayo Mejia, Martha Beatriz Mota Aragón
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.
