The future of automation thanks to Machine Learning

If you've ever wondered how to Spotify recommends songs you like or how Siri and Alexa can understand what you say to them... the answer is automation and the "machine learning", also known as machine learningartificial intelligence, a branch of artificial intelligence that allows machines to learn without explicit programming. This technology makes it possible to identify patterns in data and make accurate predictions, transforming personalization into services.

Basically, machine learning is capable of creating a computer program from a sample of data that can draw inferences from new data sets without prior training, i.e. it is an expert in pattern recognition. This form of machine learning is making the impossible possible and is being used for the improvement of search engines, robotics, fraud detection and automation in companies.

How is Machine Learning used in automation?

The machine learning has led the automation towards a new concept, the intelligent automationThe company can help companies even more by improving their operations, workflows or reducing redundant responses, all through the use of pre-existing data and the automation of the analysis derived from it.

Intelligent automation has the ability to aggregate and automate multiple different data sets, for example, in the security sector it does this with license plate recognition, temperature measurement data, video, map-based data, in addition to other records.

When applying machine learning to automation, the correlation and combination of data sources is used, which makes it possible to evaluate specific scenarios and problems. It should also be noted that the intelligent automation resulting from this application is more efficient when used in well-defined situations. For example, if a procedure finds X and another finds Y, the intelligent automation would have to perform Z.

Intelligent Automation Applications

The role of machine learning in automation varies according to the requirements of each sector or market where it is applied, fulfilling various functions and manipulating information efficiently for companies. The main areas where this intelligent automation is applied are:

• Time optimization: Knowing when to make critical decisions such as reducing production, laying off personnel or investing.
• Customer study: Using available historical information, we can determine how many customers have a high probability of abandoning us by analyzing their previous behavior patterns and comparing them with those of our current customers in order to take effective action.
• Customer service: New questions or keywords can be found that can be answered by a bot based on behavioral analysis.
• Information security: To protect our customers' personal data by providing them with assurance and trust with the application of machine learning.

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Intelligent Automation applied to the financial sector

Machine learning has brought about a positive transformation in the banking sector. Improvements in the efficiency of internal procedures, user navigability and security are some of the benefits achieved thanks to the implementation of machine learning technologies. artificial intelligence.

Below we describe 3 aspects in which machine learning benefits banking and financial sector automation.

Automation of procedures

Process automation in the financial industry reduces operating costs and increases productivity. The use of artificial intelligence software, based on machine learning, allows to verify data, generate reports and extract information from documents and forms. By focusing automation on redundant tasks with high periodicity, bias is reduced and workers' efforts are redirected to those processes that require essential human participation.

Automated credits

The implementation of predictive technology and machine learning by banks will enable the generation of credit risk models through artificial intelligence. Behavior based on credit, financial information and user consumption will be used to automate the entire process and allow customers to monitor the status of their application through online channels. This will speed up the delivery process to customers and reduce risk through in-depth analysis of each customer.

Fraud prediction

The detection of fraudulent activities and the prevention of money laundering can be effectively solved by artificial intelligence. In addition, by applying machine learning, it is possible to learn and create models that allow the automatic detection of increasingly complex frauds.

Other sectors influenced by Machine Learning

The use of machine learning has meant a real revolution in many sectors, not only in the financial sector. Examples of this are retail, the great consumption and insurance, which have seen how this technology has significantly improved their processes and results. The use of this artificial intelligence has enabled these industries to analyze large amounts of data faster and more effectively, leading to more informed decisions and improved customer experience.

Retail Sector

Artificial intelligence has made it possible to create systems of automatic labeling in the retail sector, which has revolutionized the way in which products are identified. Through these systems, product recognition is automated, without the need for human intervention. For example, a simple image of a pair of sneakers allows the system to identify the model, brand, type of sole and color.

Through machine learning, this automatic tagging is refined with user collaboration, resulting in reduced costs and operation times, and improved search results.

Mass Consumption

AI-based supermarkets rely on machine learning algorithms to achieve their specific objectives, and also use Walk Out technology, which is composed of artificial vision to process real-world images and obtain numerical information. With this technology, the store can identify its customers and track their purchases in an automated way, without the need to queue or use cash.

Insurance Sector

In the insurance industryIn most cases, customer dissatisfaction or the appearance of a more competitive offer are the main reasons that lead to the cancellation of a service. However, companies contact the user once the cancellation has taken place or even during the service resignation process, which is not very effective since the customer has already chosen to change.

Faced with these problems, machine learning allows automated prediction of which users are more likely to leave a company, which allows companies to design retention strategies to prevent them from leaving, in addition, this tool facilitates contact with the customer, allowing them to offer new products and services.