Renaud Laplanche
| Renaud Laplanche | |
|---|---|
Renaud Laplanche
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| Born | 1970 France |
| Residence | San Francisco, California, USA |
| Nationality | French |
| Alma mater | HEC Paris |
| Occupation | Entrepreneur |
| Years active | 1995–Present |
| Known for | MatchPoint, Lending Club |
Renaud Laplanche is a French-American entrepreneur and business executive. He is the founder and was for a decade the CEO of Lending Club, an American peer-to-peer lending company. He resigned from the position in May 2016 after losing the confidence of its board.
Renaud Laplanche was born in 1970 and grew up in France. He was interested in sailing and raced competitively on the national level, winning the French sailing championships on Lasers, 13.5 ft, one-sail, one-man sailboats, in 1988 and 1990.
Laplanche studied business and law. He received a post-graduate DESS-DJCE (J.D.) degree in Tax and Corporate Law from Université de Montpellier, Montpellier, France, and an MBA degree from HEC Business School in Paris, France, and London Business School.
From 1995 to 1999, Laplanche worked as a securities lawyer and senior associate at the law offices of Cleary Gottlieb Steen & Hamilton, first in Paris and later in New York. The cases he worked on included mergers, acquisitions, joint ventures, and investment transactions involving technology companies.
In New York, Laplanche soon left the law offices and, in 1999, jointly with Franck Nazikian, started his own company, a software company called TripleHop Technologies. The company had an office in the North Tower of the New York World Trade Center that was destroyed in the September 11 attacks. TripleHop suffered major losses, including its computers and recently developed software code.
In 2003 TripleHop launched its MatchPoint crawler and search engine for enterprise content. The engine provided a single search point for structured and unstructured data (databases, e-mail, file and document systems, the internet), used Support Vector Machine algorithms for indexing and concept-based retrieval, and collaborative filtering for correlating related topics, created a user profile for each user on the basis of user's search history to tailor query results to particular users, and permitted context-sensitive search where queries were expanded by synonyms from domain-specific thesaurus-type taxonomies.
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