We use Artificial Intelligence for several use cases
We are a group of consultants that have expertise in business (Telecom, Utilities) and in Artificial Intelligence technology. Together we can develop customized solutions for your needs.
Why you should use Artificial Intelligence for fraud detection?
Artificial Intelligence is the science of creating mathematical models that are capable of using data to learn. Therefore, if data from the past are placed on these models, they can learn from them and be able to predict or classify future or current events. Artificial Intelligence is a perfect case for fraud detection, but it can have its complications, because fraud events can be few compared to the total data set which means that the AI team has to be expert to be able to make a good detection. Detection with AI is amazing as it can analyze millions of data records in seconds and see similarities, something almost impossible for humans.
Artificial Intelligence models for fraud detection are more effective than traditional rule-based algorithms.
ML outperforms traditional fraud detection systems
Telecom operators usually detect fraud in various ways using a team of Analysts. We can classify a fraud detection team in 3 levels according to the support tools they have.
The first level generally is composed of a business IT team that only performs queries in the databases, trying to find characteristics that they have already seen before in other fraudulent activities.
The disadvantage of this model is that it is a very manual procedure and many times the analyst is dizzy with so much data. The fact of being manual means that a person is dedicated to running the processes and many times the weekends are not covered.
The second level would be to have a fraud management software based on static rules. These systems automate the loading of data and have a set of rules that are configurable which allow fraud to be detected if they are within the thresholds of the rules created. The disadvantages of these systems is that the rules have to be constantly updated and corrected to reduce false positives, otherwise analysts are overwhelmed by false alarms.
The system in this case is automated, but changes in the data formats which are common in the operators’ networks, requires constant changes to the implementation. These systems are generally from third-party companies and each rule or format change entails a service order which ends up being inefficient and expensive to maintain.
The third level is to have an Artificial Intelligence model that detects fraudulent based on data. These models require an initial investment in the initial training but after training, surprising detections are achieved. It is a system that does not need so much configuration of rules. If a retraining is needed, this can be carried out automatically, without the need for human intervention. In addition, the modification of the data adapters is very simple and in our case we include it within the support, so it has no associated cost.
With the rapid growth of data and the next entry of 5G, artificial intelligence becomes increasingly imperative.
The AI models, the greater the amount of data, the better the training and therefore improves the detection of fraud, being more precise alternatives than others.
Fraud Detection
The models we develop are based on Machine Learning . The Artificial Intelligence algorithms using Machine Learning, basically uses 3 types of machine learning:
Supervised Learning
Supervised learning is the most common way to implement machine learning and the one that best detects Fraud. The fundamental basis for this type of ML is that the data has to be labeled (Fraud, No Fraud, etc. ) so that the model can learn from past data and perform prediction or classification of future data or new data.
Unsupervised Learning
This unsupervised learning model is based on the analysis of data and the detection of anomalous behaviors or sets of behaviors. This model is one more model of continuous research where Artificial Intelligence analysts together with users identify these behaviors.
Semi-Supervised
Learning
This semi-supervised model is used when the amount of tagged data is not enough to ´perform supervised learning. The idea is to discover similar groups to the fraudsters already detected and label this new ones.