6. Risk management and compliance

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AI automation helps you build an effective risk management strategy by identifying inflammatory or sensitive comments about your brand. This also enables you to manage your influencer marketing more effectively to ensure your influencers are following brand guidelines. Similarly, AI automation identifies financial and legal fraud by scanning outliers in data patterns to support compliance.

How AI automation works
Several AI tasks work behind the scenes to power AI lithuania b2b leads automation—including technologies such as named entity recognition (NER) and sentiment analysis, which are pivotal in managing brand reputation and quality assurance. For example, these technologies can automatically identify anomalies in data patterns such as potentially provocative comments based on pre-determined entities and keywords and alert the end-user. This capability helps PR, social and customer service teams to immediately address such comments and take appropriate action.

Here’s a brief look at what happens behind the scenes in an AI automation process and how data engineers build a model.

Image listing the steps in building an AI automation model., which are data collection, data preparation, model training, data processing and continuous learning
Step 1: Data collection
Data is collected from different sources such as social networks, customer forums, news articles and survey responses. If we’re building a model to identify sentiment in data and send alerts and notifications when an inflammatory response is detected, data from a wide range of social media conversations is collected. The bigger the data, the more accurate the model will ultimately be.

Step 2: Data preparation
The gathered data is cleaned by removing outliers and inconsistencies and then formatted to suit the AI tool that will be used for data analysis.