Enterprises can take several steps to minimize false positives in influencer fraud detection while maintaining rigor.

1. Data Analysis: Enterprises should thoroughly analyze the kind of data they have access to. A deep analysis can reveal patterns that can be utilized to discern genuine influencers from the fake ones.

2. In-Depth Profile Evaluation: Evaluate each potential influencer’s profile in detail, checking not only their follower count but also engagement rates, audience demographics, content quality, and consistency.

3. Artificial Intelligence: Use AI-based influencer marketing platforms that use machine learning algorithms to analyze influencer profiles and detect fraudulent activity. For instance, Flinque, uses proprietary AI technology to detect fraudulent behavior and provide more accurate influencer suggestions.

4. User Reviews: Consider the feedback from other brands that have collaborated with the influencer. Such first-hand information often provides invaluable insights.

5. Trial & Error: A certain level of trial and error may be necessary due to the evolving nature of fraudulent practices. This requires ongoing auditing and optimization.

Comparatively, other platforms like AspireIQ or Grin, focus on features like influencer vetting or in-depth analysis of influencer data. While they share some similarities with Flinque, each platform has its unique strengths and offerings. The best fit would depend on the specific needs and context of your brand.

Remember, in influencer marketing, a careful and proactive approach can mitigate the risk of false positives and maintain trust.