The use of artificial intelligence for risk management is helpful when processing and evaluating unstructured data on a large scale. The key to the success of this initiative is the ability to provide inbound unstructured data with trusted and up-to-date data.
The principle is simple: Successful digital strategies build on data. The competence in data management determines how successful the risk assessment is. Managing data "the way you've always done" will not be enough in the future, as risk assessors continue to discover new approaches beyond human imagination.
Let's take fraud detection as an example. The prevalent method of detecting fraud in the past has been to use computers to analyze many structured data using a rule set. For example, fraud specialists would set a shopping basket threshold at $ 1,000 so that any transaction over that amount would be tagged by the computer for further investigation by the customer. The problem is that this type of structured data analysis in most cases led to many false positives that annoyed customers with unnecessary phone reviews or other manual verification processes. Applying simple rule sets sometimes causes hours of manual processing.
With cognitive analytics, fraud detection programs can become more robust and accurate. When a cognitive system declares a transaction that it deems potentially fraudulent, and a person declares that it is not fraud because of X, Y, and Z, the computer learns from these human insights and sends no message next time.
As cognitive systems continue to learn, they can detect more complex fraud cases. Cognitive technologies can help uncover emerging patterns in data that people could never recognize.
Our data scientists help minimize the risk in different areas of your business.
Data analysis naturally results in a predictive analysis that uses the data collected to predict what might happen in the future. Predictions are based on historical data and rely on human interaction to query data, validate patterns, make assumptions, and then test them.
Past assumptions assume that the future will follow exactly the same pattern, but "what if" assumptions are determined by human understanding of the past and the predictive capacity of data volume, time required, cost constraints and not least limited by analysts.
Machine learning is a continuation of the concept of predictive analytics, with one major difference: The AI system is able to independently make assumptions, test and learn without being biased.
AI-based machine learning makes a guess, automatically evaluates the model and algorithm, and re-evaluates the data without human intervention. AI in this context means that no human engineer needs to write a code for every possible action and reaction. AI / Machine Learning is able to test and re-test data to predict any possible scenario with a speed and ability that no human can achieve.
Let's take a look at this scenario. Do you have a lot of data, but you can not use it sensibly? They want to find, predict and reliably predict data. Well. This is the area in which our experts can use machine learning to help tip the limelight out of your structured or unstructured data and make predictions.
The term Chatbot comes from "Chatterbot", a name given by its inventor Michael Mauldin in 1994. His then creation "Julia" was the first chatbot created with "Verbot", a software and development kit popular at the time. Today, KI chatbots are referred to by many other names, e.g. Bot, IM Bot, Intelligent Chatbot, Conversation Bot, AI Conversation Bot, Talk Bot, Talking Bot, Interactive Agent, Artificial Intelligence Chatbott or Virtual Talk Chatbot.
Remember the last time you chatted online with a customer service representative. You may have complained that you received the wrong item in your order. It is very likely that the person on the other end who was trying to solve your problem was not a person at all. You may have spoken with a chatbot of artificial intelligence, basically a talking robot.
Artificial intelligence has made chatbots more realistic than ever before, and they are becoming increasingly compelling. Talking Bots accept pizza orders, reserve hotel rooms and plan appointments. In short, these robots are omnipresent.
To increase the opportunities for improving services through KI chatbots, to save money, and to maximize engagement, businesses and organizations need to understand how these programs can improve customer responsiveness and save valuable resources.
Machine learning (in the form of natural language processing, machine learning, and deep learning) allows chatbots to "learn" by discovering patterns in data. Without training, these chatbots can then apply the pattern to similar problems or slightly different questions. This ability gives them the "intelligence" to perform tasks, solve problems, and manage information without human intervention.
CodeCoda provides the NLP algorithms and platforms that enable our customers to take advantage of the full potential of automated customer interaction.
There are many other applications for machine learning and the use of artificial intelligence, e.g., product recommendation, speech recognition, image recognition, biometrics, medical diagnostics, customer segmentation, robotics, financial analysis, mobility and security applications. We believe that there are virtually no restrictions on the use cases of AI in companies. Everything a human can do can be a machine ... better and cheaper!
That's why we started the CodeCoda AI Lab: There are no restrictions on what AI can do for businesses. Working with our data scientists at the forefront of evolution, we lead our clients to successful AI applications.
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