Computer Method Research on Risk Control Identification System Based on Deep Learning

Computer technology has penetrated more and more into the field of daily life. Artificial intelligence is helping us solve many complex problems. With the explosive popularization of financial information, it is more characteristic of the times to choose P2P (Peer to Peer) network platform for borrowing and financing. Its characteristics of quickness, high income and easy lending quickly attracted a large number of small and micro enterprises, individuals and investors. However, its risks should not be underestimated, especially the borrowers in the platform are mostly users who cannot apply for lending through traditional financial banks, which increases the difficulty of risk control of P2P platform. Because the credit evaluation of small and micro enterprise lending is closely related to the personal credit situation of small and micro enterprise owners, Therefore, this paper will take the public data on the lending club lending platform as the simulation sample of the main credit data of small and micro enterprises, A concise and clear P2P credit application risk control identification mechanism is constructed. The mechanism is based on AI (Artificial Intelligence) to build a black box prediction model to assist in decision-making on possible risks, and proposes countermeasures and solutions to face risks. The future development trend of online lending platform is prospected to promote the healthy and orderly development of the industry.