Machine Learning is constantly evolving in the fight against fraud
Posted: Sun Dec 22, 2024 5:24 am
Technology is attracting the interest of companies from different segments because it offers important gains for business, including efficiency, productivity and security. In this interview, Mateus Munhoz and Joel Rodrigues, who work in the Statistical Intelligence area at Clearsale, tell us more about the impact of this business approach, where it can take us and how it has changed fraud management in the market.
After all, what is Machine Learning?
In simple terms, we can say that it is the machine's ability to learn when reproducing tasks. Just like what happens to us. The more we learn about something, the better we become at it. Machine Learning is a subfield of artificial intelligence that emulates this ability through the development of algorithms and email list france that allow the computer to analyze the data from an activity performed, create more refined patterns and apply them to new opportunities. This creates a permanent cycle of improvement at speeds much greater than that achieved by humans.
And how does the development of the method work, how does it evolve?
With the constant updating of data, creation of patterns and, mainly, with learning itself. Whenever new information is updated, the computer cross-references this data with its entire information base, generating a new discovery and automatically updating the algorithm that makes decisions. In other words, learning evolves.
Give us some examples of Machine Learning applications.
Although it has been around for some time, Machine Learning has evolved rapidly in recent years, including due to the increasing processing capacity of computers, and there are countless applications. Today, for example, a very common application is its use to improve the user experience in e-commerce. Once your preferences are identified, it is possible to personalize offers, suggest other platforms and make everything more attractive and interesting. But when we talk about learning capacity, there are countless possibilities. In a few years, we will see cars that drive themselves on the streets, as we will have models that teach on-board computers how to drive. Another real gain for people is already occurring in the area of health care. Based on a repository with thousands of images of exams with the same diagnosis, the computer can recognize a condition or disease in a much shorter time and with more accuracy than the eyes of a specialist doctor, and speed up decision-making. In fact, this is the big gain: with the signals made by Machine Learning we can quickly identify specific situations in a context in which a lot of information is generated, speeding up decision-making in different fields of activity.
What has Machine Learning added to the field of fraud management?
We have been working on this proposal since 2014 and we continue to expand its application in different market segments. With Machine Learning, we can automatically adjust statistical models according to the needs and risks of each company. Take, for example, a retail chain with a high volume of daily transactions. With technology, we can cross-reference data and buyer behaviors and identify risk factors - as a simple example, we can mention the delivery address in a given region. Fraud risk levels in each region are updated almost in real time. This is a unique feat.
This factor is essential, as fraudsters are very dynamic and are always changing their strategies, and ML updates can keep up with this dynamic. Today, one of ClearSale's main statistical models, for example, has more than 900 risk factors and, with the updates, has been running for 2 years with a unique performance in the market.
And what role does the human role play in Fraud Prevention with the arrival of Machine Learning?
I believe that human beings will always play a fundamental role in the development of any project or activity. At ClearSale, for example, people and technology go hand in hand. In the case of Machine Learning, from the conception of new analysis formats to the monitoring and checking of the quality of new information, everything depends on the efforts of a highly qualified team. We even work with what we call supervised learning, a situation in which we start from a database with patterns already identified and response variables already validated by people, so that the machine can learn from these examples. Even in so-called unsupervised learning, in which the computer starts from scratch to identify patterns, without historical data and training, human management and calibration for discoveries is essential, since the immense amount of information processed can lead a computer to draw incorrect conclusions and “false positives” for fraud. This is a risk we cannot take, especially because we are dealing with a very critical and sensitive situation for our clients. Therefore, the process of combating fraud with Machine Learning includes, in our case, a human perspective to complement learning and ensure improvement, thus respecting the good buyer. Thus, we can affirm that in our culture, human analysis is highly valued and will not be replaced.
How does ClearSale see the future of Machine Learning?
Artificial intelligence and Machine Learning, specifically, are transforming the world. It is a strong and irreversible trend in the market, technology and, it is worth highlighting, in fraud prevention management. The impacts are profound and have repercussions throughout the economy and people's lives, but discussing this deserves a separate discussion. What we can say is that, with the speed at which technology evolves, we will have increasingly more assertiveness and speed in detecting fraud attacks. The construction of a legacy of dynamic learning, combined with several technologies that are maturing, such as facial and voice biometrics, will intensify the evolution of fraud prevention in the coming years. Our goal is to always be one step ahead, analyzing not only purchasing behavior, but also incorporating methodologies and technologies, and we can say that we are indeed on the trail of fraudsters. And Machine Learning will increasingly and on a larger scale help us anticipate and block the actions of these criminals to generate more trust between the market and the good consumer.
After all, what is Machine Learning?
In simple terms, we can say that it is the machine's ability to learn when reproducing tasks. Just like what happens to us. The more we learn about something, the better we become at it. Machine Learning is a subfield of artificial intelligence that emulates this ability through the development of algorithms and email list france that allow the computer to analyze the data from an activity performed, create more refined patterns and apply them to new opportunities. This creates a permanent cycle of improvement at speeds much greater than that achieved by humans.
And how does the development of the method work, how does it evolve?
With the constant updating of data, creation of patterns and, mainly, with learning itself. Whenever new information is updated, the computer cross-references this data with its entire information base, generating a new discovery and automatically updating the algorithm that makes decisions. In other words, learning evolves.
Give us some examples of Machine Learning applications.
Although it has been around for some time, Machine Learning has evolved rapidly in recent years, including due to the increasing processing capacity of computers, and there are countless applications. Today, for example, a very common application is its use to improve the user experience in e-commerce. Once your preferences are identified, it is possible to personalize offers, suggest other platforms and make everything more attractive and interesting. But when we talk about learning capacity, there are countless possibilities. In a few years, we will see cars that drive themselves on the streets, as we will have models that teach on-board computers how to drive. Another real gain for people is already occurring in the area of health care. Based on a repository with thousands of images of exams with the same diagnosis, the computer can recognize a condition or disease in a much shorter time and with more accuracy than the eyes of a specialist doctor, and speed up decision-making. In fact, this is the big gain: with the signals made by Machine Learning we can quickly identify specific situations in a context in which a lot of information is generated, speeding up decision-making in different fields of activity.
What has Machine Learning added to the field of fraud management?
We have been working on this proposal since 2014 and we continue to expand its application in different market segments. With Machine Learning, we can automatically adjust statistical models according to the needs and risks of each company. Take, for example, a retail chain with a high volume of daily transactions. With technology, we can cross-reference data and buyer behaviors and identify risk factors - as a simple example, we can mention the delivery address in a given region. Fraud risk levels in each region are updated almost in real time. This is a unique feat.
This factor is essential, as fraudsters are very dynamic and are always changing their strategies, and ML updates can keep up with this dynamic. Today, one of ClearSale's main statistical models, for example, has more than 900 risk factors and, with the updates, has been running for 2 years with a unique performance in the market.
And what role does the human role play in Fraud Prevention with the arrival of Machine Learning?
I believe that human beings will always play a fundamental role in the development of any project or activity. At ClearSale, for example, people and technology go hand in hand. In the case of Machine Learning, from the conception of new analysis formats to the monitoring and checking of the quality of new information, everything depends on the efforts of a highly qualified team. We even work with what we call supervised learning, a situation in which we start from a database with patterns already identified and response variables already validated by people, so that the machine can learn from these examples. Even in so-called unsupervised learning, in which the computer starts from scratch to identify patterns, without historical data and training, human management and calibration for discoveries is essential, since the immense amount of information processed can lead a computer to draw incorrect conclusions and “false positives” for fraud. This is a risk we cannot take, especially because we are dealing with a very critical and sensitive situation for our clients. Therefore, the process of combating fraud with Machine Learning includes, in our case, a human perspective to complement learning and ensure improvement, thus respecting the good buyer. Thus, we can affirm that in our culture, human analysis is highly valued and will not be replaced.
How does ClearSale see the future of Machine Learning?
Artificial intelligence and Machine Learning, specifically, are transforming the world. It is a strong and irreversible trend in the market, technology and, it is worth highlighting, in fraud prevention management. The impacts are profound and have repercussions throughout the economy and people's lives, but discussing this deserves a separate discussion. What we can say is that, with the speed at which technology evolves, we will have increasingly more assertiveness and speed in detecting fraud attacks. The construction of a legacy of dynamic learning, combined with several technologies that are maturing, such as facial and voice biometrics, will intensify the evolution of fraud prevention in the coming years. Our goal is to always be one step ahead, analyzing not only purchasing behavior, but also incorporating methodologies and technologies, and we can say that we are indeed on the trail of fraudsters. And Machine Learning will increasingly and on a larger scale help us anticipate and block the actions of these criminals to generate more trust between the market and the good consumer.