Financial Institution
Banks, fintech, and other financial and credit analysis institutions are investing in machine learning to extract customer information and study transaction histories.
This is because this technology allows predicting market risks (such as loans), guarantees the rapid detection of fraud, and assists in managing compliance — financial compliance by applicable legislation.
Government
In a sector as sensitive as health, it was to be expected that artificial intelligence would provide tools to improve diagnoses and provide more security in treatments. The main applications have to do with screening risks, analyzing medical data, and monitoring the status of patients.
Management consultancy Accenture estimates that the industry’s spending on artificial intelligence and machine learning will reach the $6.6 billion mark by 2021.
Marketing And Sales
The marketing area also benefits from the application of machine learning tools. Here, technology is used to get to know consumers better, identify their perception of the relationship with a brand and develop precise strategies based on the extracted information.
In addition, marketing itself has been automated, providing regular customer contact without human error.
Oil And Gas Industry
In the fuel market, data collection has varied objectives. This ranges from forecasting risks in certain investments to compliance actions. The machines themselves have become capable of recommending the wells most in need of attention and automating related tasks.
Transportation And Logistics
With the right machine learning tools, it becomes easier to increase operational efficiency in transportation companies, in addition to the benefits applied to the logistics of other companies. The solution can predict seasonal demands and improve the distribution and storage of products.
What Are Some Practical Applications Of Technology?
Ok, we know the primary sectors that have benefited from machine learning applications. But what are the specific applications? It’s time to introduce them.
Search Engines
Life before search engines was tough. For example, a student who needed content to finish a college project needed to buy books to get information or consult the libraries in his region.
As small towns often don’t even have a library, residents of these areas had even more significant difficulties. Likewise, there wasn’t the famous “ctrl + l” combination to find the chosen terms and not need to read entire pages uselessly.
Are you grateful for the ease of search engines, which help with any search on websites and digital documents? Well, thanks to machine learning. It is what powers tools like Google, for example.
Optimized Spam Detection
Who has never opened their email and found a dozen new messages but with few significant announcements? A technology created to facilitate communication, email has also become the target of spam.
This disrupted corporate relations, as workers wasted time reading automated messages. In a rush to delete everything, it was common for critical emails to go to the trash together.
However, machine learning and artificial intelligence tools began to identify messages from dubious sources and automatically direct them to the folder desired by the user. No more wasting time on advertisements and extortion attempts, after all — and a corporate antivirus can also help identify digital threats in emails.
Biometric Reading
A mere password is often insufficient to grant access to specific environments. There must be a more secure way to ensure that only authorized people can enter certain places, right?
There is: it is biometric recognition, a technology that also relies on machine learning. This way, access to specific environments only occurs through this type of reading or voice. This is especially true for companies that have a lot of valuable information about their customers. As careful as IT professionals are, you need to guard against fraud.
Automation Of Processes And Tasks
The robotization of processes and the automation of services offer many benefits to companies in different segments. By utilizing this type of capability, companies reduce costs related to rework and eliminate human errors.
This is because the machines are now programmed to perform tasks with the level of excellence required by the company’s standards. To facilitate automation, machine learning solutions are indispensable. It is through technology that services can be automated.
The future promises a lot, especially when we talk about the automation of machines: autonomous cars are already being tested worldwide, and the trend is that a good amount will be on the streets soon.
How To Create Sound Machine-Learning Systems?
In short, a robust machine-learning system has a few essential components:
- algorithms;
- automated processes;
- tools scalability;
- optimized data analysis capability;
- predictive modeling.
This is challenging for those who still need to get used to dealing with advanced technology. In addition, it is necessary to process the data and ensure it has quality.
In this way, the best thing to do is rely on specialized partners to implement machine learning appropriately in your company. The application will be facilitated by professionals who study the subject daily, saving time.
Also Read: The Emergence Context Of Machine Learning
