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Using Machine Learning to Recommend Investments in P2P Lending

#artificialintelligence

Peer-to-peer lending marketplaces like LendingClub and Prosper Marketplace are driven by what is essentially a brokers fee for connecting investors and borrowers. They are incentivized to increase the total number of transactions taking place on their platforms. Driven by ease-of-use, their off-the-shelf credit risk assessments are scored in grouped buckets. On a loan-by-loan basis, this is inefficient given each loan's uniqueness and the sheer amount of data collected from borrowers. Scoring risk on a more granular, continuous basis is not only possible but preferable over discrete, grouped buckets.


Machine Learning Walkthrough Part One: Preparing the Data

#artificialintelligence

Cleaning and preparing data is a critical first step in any machine learning project. In this blog post, Dataquest student Daniel Osei's takes us through examining a dataset, selecting columns for features, exploring the data visually and then encoding the features for machine learning. This post is based on a Dataquest'Monthly Challenge', where our students are given a free-form task to complete. After first reading about Machine Learning on Quora in 2015, Daniel became excited at the prospect of an area that could combine his love of Mathematics and Programming. After reading this article on how to learn data science, Daniel started following the steps, eventually joining Dataquest to learn Data Science with us in in April 2016.


Analysis of Lending Club's data

@machinelearnbot

Jean took NYC Data Science Academy 12 week full time Data Science Bootcamp pr... between Sept 23 to Dec 18, 2015. The post was based on his first class project(due at 2nd week of the program). Check out the full report here! You will find all the details of the code behind the analysis and the visualisations. For this project, we wish to present and explore the data provided by Lending Club.


Lending Club investment simulator

@machinelearnbot

Lending Club (LC) is a peer to peer online lending platform. It is the world's largest marketplace connecting borrowers and investors, where consumers and small business owners lower the cost of their credit and enjoy a better experience than traditional bank lending, and investors earn attractive risk-adjusted returns.


Treasury calls for new regulations for online lenders

U.S. News

The Treasury Department called for additional oversight and some increased regulations for the online lending industry on Tuesday, the first time a government agency has weighed in on this quickly growing, but largely unrelated, part of the financial world. It also comes at a time when the biggest online lending company, LendingClub, is under scrutiny from investors after the company altered a portfolio of sketchy loans in order to sell them to an investor. The scandal forced LendingClub's CEO to resign and its stock to plunge more than 30 percent in two days. In July 2015, the Treasury Department issued a request for information on the online lending industry and received 100 responses from companies, industry lobbyists, consumer advocates, lawmakers and others. While the Securities and Exchange Commission weighed in on the industry a few years ago, the SEC was looking only at the investors' point of view.