Machine Learning for Fraud Detection
The development of digital technologies has been particularly intense in recent decades. With their help, people have many opportunities to make life easier. For example, you can access your bank account online and transfer money anywhere in the world. Also, some countries have government applications in which people can store their digital versions of documents and much more. All in all, practically everyone is experiencing digital technologies in their lives.
But, along with the aforesaid features, all sorts of frauds have also reached a new level. Hackers can break bank accounts, steal data for the purpose of selling or blackmailing and much more. All of these can be categorized as cyber attacks. But, they can be avoided with the help of artificial intelligence (AI) and machine learning (ML).
What is AI and ML in short
Artificial Intelligence is the basis of almost all digital sources at the moment. AI is essentially a semblance of human intelligence, only in its digital incarnation. It can make logical chains, evolve, and many other things.
Also, it is impossible to describe only artificial intelligence without mentioning its main component – Machine Learning. This technology works on the basis of all sorts of mathematical models and calculations. It helps in the development and training of the computer and is not based on strict sets of rules. On the basis of processed information it can analyze predicted actions in order to detect anomalies.
Fraud Detection with Machine learning
In order to identify the indicators of fraud, it is necessary to process a lot of information in order to understand by what signs it can be detected. Then, as soon as a potential threat is identified, it is important to react quickly and prevent irreparable damage.
If all of the above mentioned would be handled by a human, it would take a lot of precious time and attention would be scattered. But time and accuracy in identifying danger are important factors, especially considering the fact that cyberattacks can happen anytime and in any form. But can artificial intelligence and machine learning offer a better option?
The answer is yes, the more data and information you can provide to AI, the better and faster it can learn to identify potential threats. But, let's take a closer look at what advantages and disadvantages you should expect.
Speed and efficiency
As we mentioned above, speed is very important to respond quickly to threats. AI and ML can offer the user fast analysis of all data, as well as efficient detection of anomalies and fraud. All of these processes happen very quickly, making it possible to solve problems before they occur.
Since analyzing traffic and identifying all threats will be the main tasks for machine learning, the need for human intervention will be reduced. It means that people who work in your company will be able to devote more time to working out development plans and implementing innovative ideas in the business.
Of course, every entrepreneur or business owner will always pay attention to costs and it is quite justified. If we consider the option of hiring special employees who will monitor and identify threats and the option of machine learning application, then of course the second one will be more profitable. In addition, the option with machine learning will suit you better if you want to scale your business, because the costs will not increase very much.
When you hire employees for your team, you should realize that people can't work around the clock. They need weekends, vacations, sick days, etc. A technology solution, on the other hand, works on a continuous basis. This makes cyberattacks and fraud of all kinds virtually impossible.
No one can fully understand and track exactly how ML identifies threats. But despite the fact that it is quite effective, sometimes mistakes can happen. That is, ML can identify a completely appropriate action as spam or fraud and then categorize all similar cases as such. In this way, there will be a big disruption that can affect the success of the business as a whole. Therefore, we cannot talk about the complete exclusion of human intervention in the system. Specialists should review the system's reports at regular intervals and check whether it is working properly.
Variants of ML applications for fraud detection/prevention
Detecting fraudulent transactions
The ML system can be trained to detect and prevent fraudulent transactions. All of this is possible if you provide the system with data on all "good" transactions. In this way, ML will identify anything that does not meet the standard and will ask users to confirm the transaction, giving them time to think.
Identifying potentially harmful emails
Sometimes you may receive various emails from official organizations asking you to provide some personal information. But, you should also realize that there are similar fraudulent schemes for identity theft. ML can detect such emails and move them to spam, or simply notify the user of a potential risk.
Read also: HIPAA Compliance in Software Development
Detecting suspicious logins
Account logins can also be used to detect fraud. For example, ML will detect if someone is trying to log in to your account from another country or device. Thanks to this, you will receive a notification about it. This will help you react quickly if something is going to happen.
Let’s work together!
Oftentimes, people take their startup or business very seriously, and worry about keeping customers happy with the service. Using machine learning to identify fraud can ensure that this happens. Your partners or customers won't have to worry about the protection of their data and will trust your organization more. Trust is worth a lot.
If you're wondering whether you should implement machine learning in your business, of course you should! It doesn't matter how big your business is, because cybersecurity is always a key factor.
Software Development Hub can be your trusted partner that you can rely on to develop projects of any complexity. Our team of professionals can handle projects in any field, even though we have extensive experience as a healthcare development company.
Visit the projects section on our website to learn more about each of them and see what technologies we use. Also, you have an opportunity to get a consultation about your specific case. Make up your mind right now!