  P2P lending presents as an innovative and flexible alternative for
conventional lending institutions like banks, where lenders and borrowers
directly make transactions and benefit each other without complicated
verifications. However, due to lack of specialized laws, delegated monitoring
and effective managements, P2P platforms may spawn potential risks, such as
withdraw failures, investigation involvements and even runaway bosses, which
cause great losses to lenders and are especially serious and notorious in
China. Although there are abundant public information and data available on the
Internet related to P2P platforms, challenges of multi-sourcing and
heterogeneity matter. In this paper, we promote a novel deep learning model,
OMNIRank, which comprehends multi-dimensional features of P2P platforms for
risk quantification and produces scores for ranking. We first construct a
large-scale flexible crawling framework and obtain great amounts of
multi-source heterogeneous data of domestic P2P platforms since 2007 from the
Internet. Purifications like duplication and noise removal, null handing,
format unification and fusion are applied to improve data qualities. Then we
extract deep features of P2P platforms via text comprehension, topic modeling,
knowledge graph and sentiment analysis, which are delivered as inputs to
OMNIRank, a deep learning model for risk quantification of P2P platforms.
Finally, according to rankings generated by OMNIRank, we conduct flourish data
visualizations and interactions, providing lenders with comprehensive
information supports, decision suggestions and safety guarantees.
