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A neural network is a methodology or set of algorithms that functions similarly to a human brain. It can apply deep learning techniques to recognize patterns and draws conclusions without human intervention. Neural networks, a type of machine learning, learn and refine results over time. They are capable of organically learning and modeling complex, non-linear relationships. They can also find shortcuts, which is highly valuable in big data analysis. Neural networks can also infer relationships and self-repair when data is missing or when error conditions occur.
Neural network applications can be used in market segments, to create an ideal marketing campaign approach for each segment. In retail, forecasts are more accurate and neural networking can provide a better picture of which products were purchased on a particular day, how many times and what combination of products were purchased most often. In finance, they can provide more accurate exchange and stock rate predictions and banks can offer loans to people based on statistical data collected. On manufacturing floors, neural networks can analyze data from machinery, sensors, beacons and cameras to monitor and optimize plant processes. And, insurance companies can segment their customers for marketing, pricing and risk purposes.
Across all industries, neural networking is improving fraud prevention capabilities by detecting and alerting on fraudulent schemes. Businesses can better identify customer segments, target their marketing and sales efforts and determine why customers may be choosing their competition.