18 by Dumont Richard

Author:Dumont, Richard
Language: eng
Format: epub
Published: 2017-09-18T16:00:00+00:00

How Machine Learning Is Improving Companies Work Processes

Machine learning allows companies to expand their top-line growth and optimize processes while improving employee engagement and increasing customer satisfaction. Here are some concrete examples of how AI and machine learning create value in today's business:

Customized customer service: The potential to improve customer service while reducing costs makes it one of the most attractive areas of opportunity. By combining historical customer service data, natural language processing and algorithms that continually learn from interactions, customers can ask questions and get high-quality responses. Customer service representatives can intervene to handle exceptions, algorithms that explore their shoulders to learn how to do next time.

Improve customer retention and loyalty: Companies can exploit customer actions, transactions and social sentiment data to identify customers who are at risk of leaving. Combined with profitability data, organizations can optimize "next best action" strategies and customize the end-to-end customer experience. For example, young adults who leave mobile phone projects from their parents often move to other carriers. They can use machine learning to anticipate this behavior and make customized offers, depending on the individual's usage patterns, before they are defective to competitors.

Hire the right people: The software quickly spends thousands of work applications and small list candidates who possess the credentials most likely to succeed at the company. Care must be taken not to reinforce the human bias implicit in the previous hiring. But software can also combat human bias by automatically signalling biased language in job descriptions, detecting highly skilled candidates who might have been neglected because they did not meet traditional expectations.

Finance Automation: AI can expedite the handling of exceptions in many financial processes. For example, when a payment is received without an order number, a person must settle the payment voucher and determine what to do with an excess or loss of earnings. By monitoring existing processes and learning to recognize different situations, AI dramatically increases the number of bills that can correspond automatically. This allows organizations to reduce the amount of outsourced work in service centers and free up the finance staff to focus on strategic tasks.

Measure Brand Exposure: Automated programs can recognize products, people, logos and more. For example, advanced image recognition can be used to track the position of brand logos that appear in video footage of a sporting event, such as a basketball game. Corporate sponsors see the return on investment of their investment by sponsoring with detailed analysis, including the quantity, duration, and placement of company logos.

Fraud Detection: By building models based on historical transactions, social network information, and other external sources of data, automated learning algorithms can be used to identify spot pattern abnormalities, exceptions, and extreme values. This helps to detect and prevent fraudulent transactions in real time, even for unknown types of fraud previously. For example, banks can use historical transaction data to build algorithms that recognize phishing behavior. They can also detect suspicious patterns of payments and transfers between networks of individuals with overlapping contacts with companies. This type of "security algorithm" applies to a wide range of situations, such as cyber security and tax evasion.

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