Applications of Cognitive Computing

3.1

There are many possible applications of cognitive computing. It can handle a very minute activity of routine nature to a complex set of tasks involving logical reasoning. Here are some possible applications of cognitive computing in business:

  1. Chatbots :

Chatbots  are programs that can simulate a human conversation by understanding the communication in a contextual sense. To make this possible a machine learning technique called natural language processing is used. Natural language processing allows programs to take inputs from humans (voice or text), analyze it and then provide logical answers. Cognitive computing enables Chatbots  to have a certain level of intelligence in communication. Like understanding user’s needs based on past communication, giving suggestions, etc.

  1. Feeling analysis:

Feeling analysis is the science of understanding emotions conveyed in a communication. While it easy for humans to understand tone, intent etc. in a conversation, it is far more complicated for machines. To enable machines to understand human communication you need to feed training data of human conversations and then analyze the accuracy of the analysis. Feeling analysis is popularly used to analyze social media communications like tweets, comments, reviews, complaints etc.

  1. Face observation:

Face observation is the advanced level of image analysis. A cognitive system uses data like structure, contours, eye color etc. of the face to differentiate it from others. Once a facial image is generated, it can be used to identify the face from an image or video. While traditionally it used to be done using 2D images now it can also be done using 3D sensors which account for greater accuracy. This can be used in security systems like for a locker or even mobile phone.

  1. Risk assessment:

Risk management in financial services involves the analyst going through market trends, historical data etc. to predict the uncertainty involved in an investment. But this is analysis is not only related to data but also on trends, gut feel, behavior analytics etc. Thus it is both an art and a science. Big data analysis (i.e. analysis of past trends alone) is not sufficient to do a risk assessment. Due to the intuition and experience involved in predicting market future, it is necessary to make algorithms intelligent. Cognitive computing helps combine behavioral data and market trends to generate insights. These can then be evaluated by experienced analysts for further analysis and predictions.

  1. Fraud detection:

Fraud detection is another application of cognitive computing in finance. It is basically a type of anomaly detection.  The goal of fraud detection is to identify transactions which don’t seem to be normal (anomalies). This also requires programs to analyze past data to understand the parameters to be used for judging a transaction. A range of data analysis techniques like Logistic regression, Decision tree, Random Forest, Clustering etc. can be used to detect anomalies.

 

 

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