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Neuton Use Cases for Neural Network and Machine Learning

Customer sentiment analysis
Challenge:
With the increasing role of Internet services in the telecommunication industry’s everyday functions, it has become difficult to keep up with both problems and opportunities. A neural network can solve this challenge using a sophisticated customer sentiment analysis. However, accuracy depends on how closely the test data set resembles the dataset used to develop the dictionary or the machine learning model. Both can perform poorly if the datasets have little in common. Additionally, most algorithms are designed to handle large pieces of text like news articles, therefore might not work on shorter pieces like customer reviews.
Solution:
Neuton easily overcomes these challenges allowing companies to:
Assess its customers’ positive or negative reactions to their service or product quickly, even when the number of customers that reacted is not large
Reveal trends using aggregated data from text reviews and social media sources
React to customers’ sentiment quickly in order to prevent churning.
Why Neuton:
Neuton is not dependent on significant amounts of data to produce predictable and accurate results. Neuton excels at working with both large and small sets of data, therefore enabling effective customer sentiment analysis.
Customer requests prioritization
Challenge:
Handling an increasing number of customer requests can be resource intensive and costly to maintaining good service. One of the key factors to meeting customer expectations is to find a way to prioritize which support ticket should be addressed first, but it is difficult to prioritize while a continuous flow of requests are forever coming in to a help desk. There are also a lot of possible ways in which to classify each request. Moreover, combining and weighing such requests is nearly impossible for the staff to do manually. Prioritizing tickets should be automated by the system and be both scalable and team independent.
Solution:
AI takes the guesswork out of customer requests classification. All the requests pass through a neural network module that categorizes customer requests by assigning a category attribute that defines the processing path, through which company departments or experts the case should go to be resolved. AI-based algorithm removes the possibility of a human error and any form of subjectivity in reading a ticket.
Why Neuton:
Neuton is much more effective than any other neural network framework or non-neural algorithm available on the market. Higher accuracy translates to fewer errors resulting in faster problem resolution and enhanced customer service.
Fraud detection
Challenge:
Mobile communication fraud is a big issue to all telecommunication companies around the globe, and is a significant factor in their annual revenue losses. There are many types of fraud with subscription fraud taking the lead. Subscription fraud is characterized by a criminal using their own, or a stolen or fabricated identity, to get services with no intention to pay. The theft is hard to detect at the point of sale.
Solution:
It is possible to detect fraudulent behavior using machine learning. Neural network models can process a large volume of transactions and other activities to find fraud patterns and then use those patterns to identify fraud as it happens in real-time. When fraud is suspected, AI models can be used to flag these subscribers for investigation and can even score the likelihood of fraud.
Why Neuton:
Typically, a significant amount of data is required to effectively identify any hidden or potential fraudulent activity. Neuton easily handles such challenge because it doesn’t require an exceptional amount of data in order to produce highly accurate and predictable results.
Predictive maintenance and network optimization
Challenge:
Loyalty is no longer a matter of choice, but a necessity dictated by the growing expectations of customers. Improving customer experience at all stages of interaction is one of the key success factors for telecom companies. One of the most important ways to give customers what they want is to prevent outages.
Solution:
AI can help companies monitor equipment, learn from historical information, anticipate equipment failure, and proactively fix it. Another important aspect is network optimization. A Self Organizing Network (SON) powered by AI can help networks continually adapt and reconfigure based on current needs. It is also beneficial when designing new networks. Since AI-enabled networks can self-analyze and self-optimize, they are more efficient at providing consistent service.
Why Neuton:
Neuton can immediately bring value to telecom companies, and is equally effective, whether the organization is looking to monitor existing equipment that has been in the landscape for an extended period of time, with adequate historical data, or with new equipment which when installed has minimal to no data. Neuton is able to bring value and operate effectively in both scenarios due to the fact that Neuton is not dependent on significant amounts of data as other solutions are.
Malicious Network Traffic Detection
Challenge:
Due to an increased awareness on the corporate level of the high potential for malicious attacks, including DDoS attacks to social networks and phishing attacks, organizations are more concerned about their network and data security then ever before.
Solution:
AI can both help detect malicious events and deal with the attack as quickly as possible when identified. Based on the metadata of encrypted network traffic packets, machine learning can help classify traffic from network devices and determine the likelihood of it potentially belonging to dangerous traffic. Data on traffic marked as potentially dangerous can then used by the information security service for further investigation.
Why Neuton:
Neuton builds models that work so much faster than other solutions that the time needed for traffic analysis for the first line of NOC (Network Operations Center) operators can be significantly reduced further, even if previously utilizing another AI product.
Spam Filters
Challenge:
Spam is the scourge of the modern world. In a spam stream, it is becoming extremely difficult to see a truly relevant message.
Solution:
Machine learning to fight spam is essential. Nobody can control the spam that exists now on the Web. Therefore, the use of machine learning algorithms is our best option in the fight against spam. To trick spammers who come up with new ways to cheat each time, AI classifies the mail flow using unique neural algorithms by scanning metadata such as sender’s location, keywords, etc.
Why Neuton:
Neuton builds models that are self-growing and learning. Its resulting models work with much higher accuracy in comparison to other algorithms, enabling detection of more spam more efficiently and eliminating false positives at the same time.