Real Life Examples of Cognitive Computing

Real Life Examples of Cognitive Computing

Cognitive computing aims to automate and streamline processes, enabling them to further reduce costs and provide better customer

Today, computers that previously only performed pre-programmed tasks are gaining the ability to automatically store information and use it for future activities.

In this regard, it is important to understand what cognitive computing is, a feature that uses intelligent computing systems to mimic some skills of the human brain, such as recognizing patterns and processing languages.

What is Cognitive Computing and its importance?

Cognitive computing, the study made from the functions of the human brain and the mechanisms of computer science, to develop technological systems able to acquire knowledge through the experiences and information received.

Key Benefits of Cognitive Computing include:

  • Improves current level of efficiency by accelerating decision making
  • Scales process quantum quickly and consistently
  • Accelerates performance by capturing real-world knowledge
  • Contributes massive reforms to existing business practices that are error-prone or inefficient

It aims to automate and streamline processes, enabling them to further reduce costs and provide better customer experience. This is because the cognitive system is capable of absorbing information, processing it and proposing paths from it.

Applications of Cognitive Computing

Considering a large amount of data and information that a company needs to manage and the care it must take to ensure that nothing is damaged, it is essential to adopt more effective analysis systems than traditional ones.

Thus, cognitive computing through data mining, language processing, and machine learning can be used to identify these risk points.

Risk assessment

To predict the vagueness involved in an investment, risk management in financial services includes the analyst going through historical data and market trends. Cognitive computing helps to blend market trends and behavioral data to generate insights that can further be evaluated by senior analysts for predictions.

Fraud Detection

Fraud detection is basically a type of anomaly detection. The objective of this application is to determine transactions that seem to be unusual. Nevertheless, this also requires programs to analyze past data to understand the parameters to use to judge a transaction. To detect anomalies a variety of data analysis techniques such as decision tree, logistic regression, cluster, random forest can be used.

Chatbots

When you chat with an autoresponder tool and it understands what you are demand is, knowing the needs of the user based on previous communication, giving suggestions, etc. even if you ask the same query in different ways, cognitive computing is being used. It enables chatbots to have a certain level of communication intelligence.

Cognitive Computing Examples: Use Cases

Cognitive Computing in Insurance

Cognitive computing in the insurance industry is helping insurers reduce underwriting risks, insurance assessment inaccuracies and reduce claims costs. After all, the predictive capabilities of cognitive computing help to accurately estimate future claims amounts based on the financial arrangements that can be made. For example, IBM Watson was deployed by USAA to check that a policy application deserved approval or needed to be denied incase if it was out of line with contemporary policies.

Cognitive Computing in Customer Service

Routine and uncertainty alternate wildly in the domain of customer service. Therefore, customer service agents need to stay current with product changes. This, in addition to understanding the customer perspective and providing assistance without letting human inefficiencies get in the way. At the same time, the cost must also be kept to a minimum to maintain the profitability of the business. For example, VentureBeat researched the world about popular chatbots and focused on some that were outperforming human customer service.

Cognitive Computing in Healthcare

Handwritten notes, long periods to identify disease symptoms, and lack of information remain primary causes that undermine the efficiency of health care professionals. By offering insightful information via programmatic computing, cognitive computing can eradicate all these inefficiencies. For example, researchers at the University of California, Los Angeles (UCLA) were able to quickly identify people with diabetes changes by mining thousands of patient records in digital format. Data mining also revealed patterns that helped identify the chances of previously unknown disease patterns.


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Jason Hoffman

I am the Director of Sales and Marketing at Wisdomplexus, capturing market share with E-mail marketing, Blogs and Social media promotion. I spend major part of my day geeking out on all the latest technology trends like artificial intelligence, machine learning, deep learning, cloud computing, 5G and many more. You can read my opinion in regards to these technologies via blogs on our website.