In an online credit market where adverse selection and moral hazard are prominent, it is widely considered as an effective way, based on Dynamic Incentive Mechanism, to promote self-fulfillment by contacting personal credit quota and reimbursement. However, the frequent “arbitrage loan” in recent years makes it necessary to re-examine the effectiveness of the system. To test this, we develop the Fractional Response Regression Model, supported by defining the rate of credit default with the “proportion of overdue times in total times of repayment” as the measurement of default risks, to empirically analyzes the impact of the change of credit quota on personal self-fulfillment.

The results show that: （1）Dynamic Incentive Mechanism produces a “reverse incentive” effect which may excite the rate of online credit default; （2）Compared with ordinary borrowers, people well-qualified can be much more affected by the “reverse incentive”; （3）The phenomena of “over loans” and “borrowing new loans to repay the old” do exist in the online credit market, and the default rate of people with such behaviors are more affected by “reverse incentive” with a higher rate of default. Then, we next discuss the potential mechanism of this system. We find that early repayment, multiple repayment（on time）and large amount repayment would increase the credit quota and help to promote borrowers’ subsequent self-fulfillment, and overdue repayment also helps to increase the credit quota and the level of default, which results in the “reverse incentive” effect.

These conclusions will provide theoretical support for online credit companies to take differentiated regulatory measures for different loan groups, and also provide experience support to prevent online credit default risks（like “arbitrage loans”）for regulatory departments and online credit companies. Especially, on the regulatory side, we suggest that the “credit blacklist” system and borrowers’ information exchange platform should be improved by regulatory authorities to alleviate asymmetric information. On the market side, it is suggested that online credit companies should pay more attention to risk control management to fight against default risks from excessive concentration of investment and excessive lending behavior.