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Oportunidades Y Riesgos De Las Plataformas De Financiación En Internet

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Oportunidades Y Riesgos De Las Plataformas De Financiación En Internet Oportunidades y riesgos de las plataformas de financiación en Internet Carlos Serrano-Cinca1, Begoña Gutiérrez-Nieto Resumen Las FinTech, empresas que utilizan las tecnologías de la información y comunicación para ofrecer productos y servicios financieros disruptivos tradicionalmente reservados a las entidades financieras clásicas, surgen en el marco de la economía colaborativa. Un inversor particular puede prestar dinero a otro particular o a una empresa, en cualquier lugar del mundo, en cuestión de minutos y sin que participe un banco. Muchas empresas jóvenes e innovadoras, las start-ups, financian su crecimiento e internacionalización mediante inversores particulares utilizando estas plataformas colaborativas de préstamos en Internet. Pero no todo son ventajas, porque en estos préstamos son los inversores particulares los que asumen el riesgo de crédito y muchos carecen de formación en gestión de riesgos financieros. Los inversores particulares sufren un severo problema de información asimétrica porque tienen una desventaja informativa frente a los deudores, situados a miles de kilómetros. Por ello, las plataformas electrónicas tratan de ofrecer a los inversores potenciales información sobre los solicitantes de préstamos y califican el préstamo con un grado, en función del riesgo de crédito. Pero no solo es importante determinar la probabilidad de quiebra y sus factores explicativos, sino ser capaces de predecir la rentabilidad de cada préstamo, ya que, los prestatarios más rentables no siempre son los más solventes sino a veces aquellos que pagan intereses elevados, abonan descubiertos y pagan elevadas comisiones. Este trabajo estudia los préstamos de este tipo de plataformas y los factores que explican su rentabilidad. El estudio empírico utiliza datos de Lending Club, la plataforma líder en Estados Unidos. El trabajo compara la capacidad predictiva de ocho técnicas estadísticas de minería de datos. La técnica con mejor capacidad predictiva es support vector regression seguida de un modelo de red neuronal. Se recomienda el uso de sistemas decisionales para seleccionar los mejores préstamos. 1 Departamento de Contabilidad y Finanzas, Universidad de Zaragoza, Zaragoza, España E-mail: [email protected] 1 Palabras clave: Financiación internacional, préstamos colaborativos, riesgo de crédito. Abstract FinTech, companies that use information and communication technologies to offer disruptive financial products and services traditionally reserved for classical financial institutions, arise within the framework of the collaborative economy. A private investor can lend money to another individual or to a company, anywhere in the world, in a matter of minutes and without a bank. Many young and innovative start-ups fund their growth and internationalization through private investors using these collaborative Internet lending platforms. But not all are advantages, because in these loans the private investors assume the credit risk, and many of them lack training in financial risk management. Private investors suffer a severe problem of asymmetric information because they have an informational disadvantage against the debtors, who are thousands of kilometers away. Therefore, electronic platforms try to offer potential investors with information on loan applicants and they also rate the loan, depending on credit risk. But it is not only important to determine the probability of bankruptcy and its explanatory factors, but to be able to predict the profitability of each loan, since the most profitable borrowers are not always the most solvent, but sometimes those who pay high interest, pay overdrafts and pay high commissions. This paper studies the loans of this type of platforms and the factors that explain their profitability. The empirical study uses data from Lending Club, the leading platform in the United States. The paper compares the predictive capacity of eight statistical techniques of data mining. The technique with better predictive capacity is support vector regression followed by a neural network model. The use of decision-making systems to select the best loans is recommended. Keywords: International finance, collaborative loans, credit risk. 2 1. Introducción Los préstamos colaborativos son realizados por individuos que prestan dinero a otros individuos o empresas sin que intermedie una entidad financiera. Un inversor particular puede prestar dinero a otro particular o a una empresa, en cualquier lugar del mundo, en cuestión de minutos y sin que participe un banco. Muchas empresas jóvenes e innovadoras, las start-ups, financian su crecimiento e internacionalización mediante inversores particulares utilizando estas plataformas colaborativas de préstamos en Internet. Estos préstamos pueden analizarse desde diversa ópticas. Pueden verse como un fenómeno de desintermediación financiera (Lee and Lee, 2012); como otra disrupción tecnológica originada por Internet (Stalnaker, 2008); como un ejemplo de funcionamiento de economía colaborativa (Belk, 2014) o incluso como una plataforma para prestar dinero a personas excluidas de la financiación bancaria tradicional (Yum et al, 2012). Aunque no existen bancos, sí existe una empresa que mediante una plataforma electrónica pone en contacto los prestamistas y los prestatarios y cobra una comisión por este servicio. Empresas como las estadounidenses Prosper o Lending Club, la alemana Lendico, la española Circulantis, la argentina BancaClub, la británica Zopa o la china Lufax canalizan este tipo de préstamos, y muchas de ellas no solo se limitan a sus países de origen sino que han internacionalizado sus préstamos. Otras plataformas como Kiva se centran en financiar a personas en situación de exclusión financiera en todo el mundo, mediante donaciones o préstamos sin intereses. Su crecimiento está siendo espectacular. Estos préstamos colaborativos, llamados P2B si se trata de financiar a una empresa o P2P si se financia a un particular, son una actividad arriesgada para los inversores particulares, porque los préstamos no los concede la plataforma sino los inversores, quienes asumen el riesgo de crédito, es decir el riesgo de que el prestatario no devuelva el préstamo. En general, los préstamos no tienen colateral ni están garantizados por algún fondo de garantía, como sucede en los depósitos bancarios, aunque algunas plataformas ofrecen garantías de recompra. Cada vez es más frecuente que una pequeña empresa de un país cualquiera, solicite un préstamo en una de estas plataformas y obtenga financiación de inversores de otro país. Además en muchas de estas plataformas es posible revender el préstamo, es decir, disponen de un verdadero mercado secundario en los que se comercializan los préstamos, lo que aumenta la liquidez e incluso atrae a especuladores que detectan los mejores préstamos no para invertir sino para volver a venderlos a un mayor precio. La creciente popularización de estas plataformas hace que muchos inversores particulares estén destinando parte de sus ahorros a realizar préstamos, algunos de ellos sin conocimiento suficiente sobre gestión de riesgos financieros. De hecho, en muchos países estas plataformas de préstamos tienen muchas limitaciones legales o incluso no pueden operar. Pero en otros países no existen restricciones, incluidas plataformas que operan desde paraísos fiscales o desde países con mercados financieros muy abiertos, sin apenas supervisión gubernamental. Por ello es importante conocer los factores que explican la probabilidad de quiebra y la rentabilidad de estos préstamos, y desarrollar sistemas decisionales capaces de seleccionar los mejores préstamos, que es el objetivo de este trabajo. 3 El problema del riesgo de crédito se acentúa con la información asimétrica. La información asimétrica consiste en que los prestamistas tienen menos información que los prestatarios y es uno de los problemas graves de los mercados de crédito (Stiglitz y Weiss, 1981). Pero en el caso de los préstamos colaborativos el problema se acentúa porque al menos el banco tiene acceso a información histórica de los clientes e incluso a veces los conoce en persona, mientras que un inversor particular escrutando préstamos en su ordenador apenas tiene acceso a una ficha con unos pocos datos del cliente, que puede estar a mucha distancia. La información asimétrica conduce a la selección adversa (Akerlof, 1970), situación que surge porque los inversores no pueden diferenciar entre prestatarios buenos, que devuelven el préstamo, de los malos, con alta probabilidad de no devolverlo. La selección adversa se mitiga con información de calidad. Por eso las plataformas electrónicas de préstamos colaborativos hacen esfuerzos por dotar de transparencia al proceso de prestar; no solo está disponible la información de cada prestatario sino que cualquiera puede descargar información histórica con todos los préstamos concedidos, sus características y su estado como solvente o quebrado. Esto contrasta con la práctica habitual utilizada en la banca, cuyas bases de datos de clientes permanecen ocultas. El riesgo de crédito, la asimetría de la información y la selección adversa son problemas a los que deben hacer frente las plataformas P2P/P2B, pero estos préstamos también ofrecen soluciones a otros problemas habituales de los mercados de crédito, lo que explica su crecimiento. Los préstamos colaborativos pueden minimizar el racionamiento del crédito (Lin at al, 2013) que consiste en que no todas las personas o empresas que precisan un préstamo lo obtienen, aunque estuvieran dispuestas a pagar
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