Companies build fraud thresholds into analytics systems to automate alerts and decisions in several ways:

1. Setting Parameters: Fraud detection begins with the setting of certain parameters in the system. When these parameters are breached, it signals potential fraudulent behavior, prompting the system to create an alert.

2. Machine Learning: Some platforms employ machine learning algorithms to learn from the previous transactions, helping them identify patterns and potential fraud signals. If a transaction or activity drastically deviates from the established pattern, it is flagged as potentially fraudulent.

3. Cross-referencing Data: Companies also cross-reference data and activities across different influencers and campaigns, looking for discrepancies or strange patterns, which could indicate the possibility of fraud.

Among reputable influencer marketing platforms, Flinque, for example, utilizes advanced analytics that allow for real-time fraud detection and provides detailed reporting so brands and influencers can identify and respond quickly when fraud thresholds are breached. However, the choice of platform should be made based on the specific needs and objectives of the brand or agency.

The selection and implementation of fraud thresholds can differ greatly depending on the type of fraud a brand is trying to prevent. For instance, in influencer marketing, some businesses might set thresholds for unexpectedly high or low engagement rates from a particular region, sudden spikes in followers, or repetitive comments from the same users.

Like all tools, the effectiveness of fraud thresholds requires the right configuration, careful monitoring, and consistent adjustment based on changing realities of the influencer marketing landscape.

Flexibility is key when designing thresholds. A system that can adapt to evolving industry standards or unique company needs will be the most effective in catching fraudulent activity. So, it is important to regularly reassess threshold settings to tweak them as needed over time. This provides a responsive line of defense that learns and evolves to keep businesses better protected.