Machine learning has a clear advantage over the legacy model practices, as from various applications, fraud detection, fraud prevention, and anomaly prediction are been the foremost successful applications. As cybercriminals are becoming more intelligent in committing fraud, financial service providers in insurance, banking, money transferring apps, and E-commerce platforms are spending billions to create a protective firewall. The Machine Learning tools can help in reducing the company’s cost and creating a trustworthy environment.
87% of users prefer e-transactions all over the world. It simultaneously increased the usage of mobile E-commerce and digital payment apps, and thus, commits of fraud. According to 2018 reports, the rate of fraudulent activities committed all over the world is 47%. And 7,200 companies have mentioned that due to these internal and external fraudulent activities, they have lost $25 billion. But in actual, the amount will be triple as the companies in various industries will be losing potential customers.
Legacy Models versus Machine Learning In Fraud Prevention
Traditionally business entities followed data analysis tools for fraud prevention and detection. These systems required complex and time-consuming investigations to tackle fraud by test, detect, validate, correct error and monitor control systems. The key reason for the acceptance of the machine learning system is, during the early stages of technology development, the internal systems in the organization are weak against fraudulent activities. Data analytic techniques used for fraud detection are statistical techniques and AI systems. The tools used were data mining, data matching, regression analysis, and cluster analysis.
Even though data analytical tools have the potential to process structured quantitative data sets, it has limitations with unstructured data. Within the data sets, they provide knowledge to prevent fraudulent activities, as system knowledge is fed by human analysts. The business needed systems that have substantial background knowledge and the ability to perform risk analysis based on knowledge and data-driven inputs.
Automated machine learning screening networks can identify the unique characteristics of online fraud detection. It can make quick decisions based on the repeated patterns in the large volumes of unknown data sets. These implementations of machine learning can provide an advantage over traditional tools of data analytic systems.
Speed, scalability, and efficiency are three things that differ from the machine learning system from other models used for fraud detection. A machine learning-based system performs fast computation in identifying hidden patterns in the reductant task of data analysis. For humans and statistical data analytical tools, it is hard to analyze large volumes of data. But with machine learning, the more the data is – the more accurate, efficient, and predictive insights can be generated.
As fraud committers are finding new ways to exploit technology to commit schemes and target victims, the business needs to advance their technologies to stop them. According to ACFE reports, 13% of world organizations use AI and machine learning technologies to discover and prevent fraud. And it is expected that 25% of other companies are planning to adopt such technologies in the next two years.
Machine Learning keys for Fraud Prevention
The three important machine learning keys to detect and prevent fraud in various industries include:
Supervised And Unsupervised Machine Learning Models
Supervised learning models are trained with tagged outcomes, where the tags are differentiated into ‘fraud’ and ‘non-fraud’ transactions. If a transaction takes place based on the inputs fed into a supervised model, the system detects valid outputs. The accuracy of model output depends on how well-organized your data is.
Unsupervised learning models are built to identify unusual behavior in the transactions process. These models can identify hidden patterns in the transaction by self-learning, analyzing the available data and determining similarities and dissimilarities in the tractions, thus, making it a useful tool for predicting results during various tractions. Both supervised and unsupervised models are used in combinations or independently to detect the anomalies in fraud occurrence.
“Practise makes a man perfect”. It implies even for a machine. The more a machine is fed with large volumes of data, the machine needs more training sessions to drive more accurate results. The machine has the ability to self learn. Hence, the more it commits mistakes, the more it learns and recognizes new patterns. This is reinforcement learning. The machine learning models can benefit from gaining knowledge from billions and millions of live examples that help in identifying legitimate and fraudulent transactions.
The neural network is a concept that works similar to the function of neurons in the human brain. Neural networks in deep learning use hidden layers for computation, which help in building a cognitive machine learning model. With an ability to self learn and use algorithms like data mining, pattern recognition, and natural language processing for fast computation and detecting the accurate results.
The neural networks are completely adaptive and can learn from lawful behavior. By processing the same datasets through enormous layers, the machine learning model can accurately find out the fraud transactions compared to other models.
As fraudulent activists are using sophisticated ways to commit fraud, professional fraud protectors need to advance their technology usage with evolving technologies. The machine learning tools provide speed, accuracy, and efficiency by using various techniques like regression analysis, decision trees, and logical reasoning that can help reduce fraud in an enterprise. The advanced application of machine learning technology can provide an advantage over the legacy statistical models used by organizations. The machine learning based model can reduce the chance of occurrence of internal and external frauds.