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Neuton Use Cases for Neural Network and Machine Learning

Credit Scoring
Challenge:
Banks and enterprise creditors use credit scores to better understand the risk associated with their potential borrowers. Traditional credit scoring takes into account such factors as credit history and salary, often rejecting borrowers who are credit-worthy, yet do not qualify, such as college graduates, immigrant entrepreneurs and other consumer groups that otherwise show high potential. On the other hand, banks might give credit to consumers that are not profitable.
Solution:
Machine learning solutions are a powerful tool for performing smarter scoring by taking a more individualized approach. A neural network model allows a more accurate assessment of each borrower based on large number of complex and in-depth rules and increases the pool of credit-worthy customers without taking too much risk. Furthermore, a self-learning model can continuously improve itself as new data is fed into the system.
Why Neuton:
Neuton builds models that execute significantly faster and more accurately than models produced by other solutions. This means it provides a faster and more accurate assessment of a potential borrower, and faster assessment means faster decisions and more issued credits with less risk, which translates into higher profitability.
Financial Trading
Challenge:
Predicting or timing the stock markets is an ongoing challenge for many individuals and companies. A stock market is considered as one of the most highly complex systems, consisting of many components whose prices move up and down without having a clear pattern.
Solution:
Machine Learning prediction algorithms are getting closer to solving this machine learning regression challenge all the time. Today, many prestigious trading firms use probability technology to predict and execute trades in higher volumes and speeds than a human could ever do.
Why Neuton:
In stock trading, sometimes the profitability of an operation depends on the millisecond difference in buying or selling. In this particular case, not only is the accuracy of the model essential, but the speed of execution of the prediction is equally critical. Contrary to most legacy solutions, Neuton develops models that are compact in size and executes significantly faster than other models produced.
Targeted Marketing
Challenge:
With a large variety of banking products to choose from and consumer expectations for personalized treatment, finding the right products to upsell is not a trivial task.
Solution:
AI can help define the best audience more quickly and accurately. This allows companies:
Make the upsell relevant
Prioritize which customers to target with which products
Adapt offers to the individual needs and preferences
Why Neuton:
Neuton models determine which products are the best fit for a given customer with far higher accuracy than those generated by other solutions. Furthermore, Neuton excels when working with either large or small sets of data, therefore enabling effective neural network learning in either circumstance to meet business objectives. Finally, it is so easy to use that any marketer can handle it.