Estimated reading time: 7 minutes
It was always not so easy to detect fraud. Accordingly, extremely many methods were used to achieve this and financial organizations tried to implement something new all the time. However, the results weren’t productive in each case. Later, a great solution for the problem was found. It was represented by the machine learning methods we are going to represent below.
The first group of the methods (supervised)
Fraud detection machine learning approaches can be separated into two big groups. We’ll proceed with the supervised algorithms primarily.
First of all, it’s necessary to point out Random Forest. In this one, randomized decision trees are applied, and in the end, the outputs are created based on their predictions. It is thought to be one of the most efficient methods today due to its strong forecast capacity. And, the research also states that this algorithm seems to detect fraud more effectively compared with KNN, which means K-nearest neighbors (here, the forecast is given regarding what class the invisible instance belongs to). The advantage of this algorithm is that KNNs demonstrate a lower error rate than the Decision Trees models.
There are also a couple of algorithms of a kind. Look through them below:
- Logistic regression. As for this method, the likelihood of the categorical response is forecast judging by the predictor variables. A lot of businesses find it very tempting due to the fact that its implementation isn’t going to take much time. At the same time, there is a great downside of it which is a lack of accuracy (some research papers state this);
- SVMs, or Support Vector Machines. Moneywise fraud detection with them will be rather effective (kernel functions are being applied) and you will not have to spend a lot of time to start using the algorithm in your financial organization.
There is also one curious tech to speak about. Go ahead to reveal what we are hinting at!
Deep learning fraud detection
This part isn’t just about the detection but also the measures to prevent fraud. So, we are about to touch upon LSTM, which is Long Short-Term Memory.
The main principle of this one is to provide the permanent error flow within cells. And, the tech can do this successfully. It is also curious that such a method turned out to be effective even for offline operations (when a holder of the card was at the bank). Nevertheless, it is not widely used today as long as it is very hard to integrate it into the apps that are being developed nowadays.
One of the payment systems that started to apply this already is PayPal. As a result of such an implementation, registered machination revealing increased about 10%.
Unsupervised options to consider
K-means is the first one in this category. Thanks to it, unlabeled data is divided into clusters in a special way which promotes decreasing the distance between the objects of data and centroid in every cluster. When such a method is being applied, cheating activities are revealed quickly and effectively.
One more option to mention is called SOM, or Self-organizing Map. It suits high-dimensional data most of all. It is also referred to as constrained K-means. In this method, due to self-organization, data is put in either of the two categories: legitimate or fraudulent. And, in the end, the result will be considered to be in one of these groups judging by the similarity of the input and correct or fraudulent transaction.
We have been delivering all this info in order to express the following: it’s better to stick to one of these methods in order to protect your data, prevent and detect fraud at the earliest stage possible. Besides, it’s a great idea to use such technologies because they will allow decreasing losses from the illegal operations with credit cards. Also, in case your company faces unauthorized access to the payment system and stuff like that, it will also be possible to fix the consequences and make them harmless for your enterprise.
One of the best ways to introduce such an option to your business is to start cooperation with Perfectial. Let’s talk about it in detail below.
A company to assist you and implement all your ideas into reality
It’s better to arrange a consultation on the matter you are going to undertake in your business activities. But it’s even more beneficial to do it with the real experts in all this stuff. And, Perfectial is about to become your true friend and a guide in it.
Not only fraud detection machine learning methods can be introduced according to your request. The company also deals with other services, such as mobile applications development, SaaS expertise, and much more. One of the basic principles of work here is the exceptional quality of any software that is being created.
Perfectial is ready to introduce any innovation that is being demanded by the client. This becomes possible thanks to the great talent pool and people who are devoted to what they do. You can explore the opinions of those who have already dealt with the company and make sure that you are about to face amazing service here.
All specialists working here constantly improve their knowledge and devote a lot of time to exploring new tech and thinking of how to better implement it to businesses of various kinds. Individual approach is one of the additional values. Being flexible makes it possible to achieve awesome results in introducing new tech into enterprises.
So, summing up everything said here, we would like to highlight that it’s real to arrange good fraud detection in your financial organization. It doesn’t matter whether it’s a small startup or something bigger. Anyway, there is always a chance to make it function better and protect from any kind of fraudulent activities coming from third parties. Just deal with the real professionals. So, good luck to you with all of that!
The total cost of IoT application development
Below we list the main factors that will affect the cost of IoT software development:
- IoT app features. In order to reasonably distribute the budget, you will need to decide on the main functionality of your IoT application. First of all, develop it, and then implement additional features when the budget allows.
- UX/UI complexity. The interface design of your IoT application is what users will prioritize. Indeed, at first glance, the success of the product will depend on the visual component. If the application is easy to use and has a nice appearance, it will most likely attract more users and become popular.
- Types of devices. You have the right to choose for what needs you are developing an IoT application (for the Internet, mobile devices or desktop computers). But at the same time, it is important to consider that the cost of development will vary depending on the type of device you choose.
- OS and API integrations. Often, when developing an IoT application, it is launched on one platform – iOS or Android. In this regard, we recommend that you choose the operating system that is popular with your users. Later, you can always add another operating system for extra money from the budget.
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