Neural networks -
Creating and configuring neural networks for your business
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An artificial neural network (ANN) is a mathematical model, as well as its software or hardware implementation, built on the principle of organization and functioning of biological neural networks - networks of nerve cells of a living organism. This concept has arisen in the study of processes occurring in the brain, and when trying to simulate these processes. The first such attempt was the neural networks of W. McCulloc and W. Pitts. After developing learning algorithms, the resulting models were used for practical purposes: in forecasting tasks, for pattern recognition, in control problems, etc.
ANN is a system of simple processors (artificial neurons) connected and interacting with each other. Such processors are usually quite simple (especially in comparison with processors used in personal computers). Each processor of a similar network deals only with signals that it periodically receives, and signals that it periodically sends to other processors. And, nevertheless, being connected to a fairly large network with controlled interaction, such separately simple processors together are capable of performing rather complex tasks.
Применение нейросетей в финтехе
CHURN PREDICTION AND CUSTOMER SEGMENTATION
Build predictive models to know your customers better. With the help of machine learning methods, we analyze your customers' behavior to determine their loyalty and to prevent them from churning.
RECOMMENDATION SYSTEM
With the help of machine learning make sure your customers are getting what they like throughout their experience using your services.
FRAUD DETECTION AND ANTI MONEY LAUNDERING
Apply predictive models and anomaly detection models to your data to detect, identify and prevent fraudulent actions.
INFORMATION EXTRACTION AND SENTIMENT ANALYSIS
Gather and analyze real-time data from sources like market data, financial articles and news, relative contents in different languages and social media to create relative reports and assess their impacts using natural language processing methods, e.g. sentiment and intelligent specifications on a stock.
RISK MANAGEMENT AND PORTFOLIO OPTIMIZATION
Estimate portfolio risk and return using machine learning methods, predict portfolio risk appetite with lower estimate error and high accuracy.
INSURANCE UNDERWRITING
Evaluation of risk and exposure of potential customers, e.g. how reliable a person or an application is!
Процесс разработки
1
Business understanding
Focuses on understanding the project objectives and requirements from a business perspective, and then converting this knowledge into a data mining problem definition and a preliminary plan.
2
Data understanding
Starts with an initial data collection and proceeds with activities in order to get familiar with the data, to identify data quality problems, to discover first insights into the data, or to detect interesting subsets to form hypotheses for hidden information.
3
Data preparation
The data preparation phase covers all activities to construct the final dataset from the initial raw data.
4
Modeling
Modeling techniques are selected and applied. Since some techniques like neural nets have specific requirements regarding the form of the data, there can be a loop back here to data prep.
5
Evaluation
Once one or more models have been built that appear to have high quality based on whichever loss functions have been selected, these need to be tested to ensure they generalize against unseen data and that all key business issues have been sufficiently considered. The end result is the selection of the champion model(s).
6
Deployment
Generally this will mean deploying a code representation of the model into an operating system to score or categorize new unseen data as it arises and to create a mechanism for the use of that new information in the solution of the original business problem. Importantly, the code representation must also include all the data prep steps leading up to modeling so that the model will treat new raw data in the same manner as during model development.
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Before we start, we sign a confidentiality agreement. Also under the contract, the customer acquires the rights to the developed software and will be able to register the rights to the original software.
1
Selection and monitoring
2
Signing agreement
3
Developing a solution or the ready made solution adaptation
4
Development results transfer
5
Technical support
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