Machine learning is a field of science that offers machines an ability to understand data and carry out processes just as a human would do. Sometimes, even more efficiently.
The ML technology uses complex algorithms to analyze large data sets and find data patterns that help in business decisions. This is why machine learning can detect fraud in the system easily. It is, in fact, used for various other purposes such as spam detection, product recommendation, image recognition, predictive analysis, etc.
Gartner predicted that by the year 2022, the machines would be analyzing 50% of the data, which is only 10% more from the present scenario.
Since machines are far better at detecting patterns, ML can analyze huge sets of data in one chance and find fraud-related behavior through cognitive technology.
Let’s dive in and analyze in detail how machine learning helps in fraud detection:
Machine Learning for Fraud Detection
Implementation of machine learning can streamline many functions and enhance the real-time economy. Needless to say, this technology also empowers organizations to detect fraud occurrence even before it impacts the users.
Here are the three steps involved in fraud detection through machine learning:
1. Data Extraction
After the extraction of data, the ML algorithm is trained with a data set. This training is tweaked in the next step with some modifications in the testing set. Then, the results of all the sets are compared through cross-validation of sets.
The models that are high performing are extracted and various data splits are tested to ensure that the algorithm shows consistency.
2. Training Sets
The main use of ML in detecting fraud is a prediction, which means that the algorithm predicts whether a transaction is authentic or not. For instance, the algorithm will quickly check the usage pattern of a credit card along with its origin country. This will help eliminate the chances of fraud.
3. Model Building
At the time of building models, it is determined how a set of input and output data can be used to predict future occurrences of fraud. Prediction can be achieved in the following ways:
- Logistic regression
- Neural networks
- Decision tree
- Random forest
How Machine Learning Helps In Reducing Fraud?
Take Huawei Technologies for example. They use the translytical database to ensure real-time detection of fraud that may happen through mobile payments and credit cards. Every time, a user scans a phone, swipes a card, or follows any other process for a financial transaction, the authorization process is carried out. This authorization process involves making a decline or authorized decision. This decision is made with ML algorithms, where previous data is used to evaluate fraudulent behavior.
The whole training is carried out in a big data environment, where the translytical database exports information to the system. The whole model is then loaded numerous times in one day as user-defined functions or stored procedures.
Note: It is necessary to ensure ongoing training in your ML system because fraudulent methods change. Hence, it is essential to keep training the algorithm so that efficient, quality situations can be made.
It is amazing how machine learning algorithms focus on prevention of fraud rather than management. This means that ML is efficient in figuring out when any fraudulent activity is about to happen, which helps in preventing the loss that can happen.
Machine learning combined with other technologies has the power to save a lot of resources of a company. Apart from the cost saving, fraud activities put the identity of users at risk. Losing this data threatens the personal security of customers, which, of course, reduces brand loyalty. Hence, machine learning can help prevent fraud and assist companies to offer better services to users.