How AI help in Fraud Detection-Humaci.com

1. How AI help in Fraud Detection-Humaci.com Artificial Intelligence has paved the way for a more profound understanding of businesses and is nowadays helping with the process of making key business decisions. Apart from the decision making processes that are common for every business be it a small-time or a major corporation, there is one more problem that every business of every industry faces. This problem is handling fraudulent activities. Over the years, Artificial intelligence has become a very powerful tool to identify and even avert fraud. Using Artificial Intelligence for building a fraud detection and prevention solution has brought about dynamic security for businesses. Artificial Intelligence solutions are efficient and timeless. Evaluating frauds concerning the assets of a company helps in a real-time restructuring of business processes. This helps in preventing loss incurred. Before the use of artificial intelligence was so rampant, finance companies used to identify fraud by going through the pattern of transactions. These patterns were then analyzed and more often than not, it would be too late to salvage the loss. The underlying principle of fraud prevention solutions for artificial intelligence is also the same. It also finds a pattern of transactions and when that pattern does not fit into the pattern either previously recognized or in any way does not fulfill the parameters of an ideal transaction, it is considered as fraud. But the way it does so is by using a series of intelligent algorithms. The concept of machine learning under artificial intelligence consists of broad categories such as supervised and unsupervised learning. Supervised learning deals with the analysis of labeled data and unsupervised learning deals with the analysis of unlabelled data. For a successful fraud detection system, a combination of both is put to use. Machine learning algorithms help in identifying the correct transactions thereby reducing what is called a “false positive” also. Since artificial intelligence solutions can deal with large amounts of data and can automatically identify patterns once programmed, they prove to be the most effective in fraud prevention. While the labeled data can easily be classified as fraud or not using supervised learning, the use of unsupervised learning is to detect anomalies. They are designed to find outliers that do not match with any previous fraud thereby making the artificial intelligence solution proper for handling unseen errors. This makes the analysis different for different situations and one model is not used for all fraud detections. There can be various types of frauds such as tax refund frauds, mail frauds as well as internet frauds but for a deeper understanding of fraud detection, let us take the example of credit card fraud detection. The data of any person with credit card contains various time stamps which help in understanding the time that has been taken in between transactions. The very first step towards building a successful AI model is to scale the transactional data of an individual. This will help in normalizing the values of features within a range. Finding out missing values in the data is also necessary since we do not want our algorithm to throw us an incorrect result.

2. Since the problem was undertaken is a classification problem as we want to classify our result into binary outputs i.e. whether the transaction is fraud or not, various machine learning algorithms called classifiers can be employed. They include Logistic Regression, Linear Discriminant Analysis(LDA), K Nearest Neighbors(KNN), Classification Trees Random Forest classifier. Support Vector Classifier etc. Let’s consider the random forest classifier for this problem. A random forest classifier creates a set of decision trees from a randomly selected subset of data. Using this, it combines flags from different decision trees to find the final range. Random forest is based on ensemble learning where more than one algorithm can be combined (multiple decision trees) once or multiple times to result in the most optimum predictive model. The working of a random forest algorithm is to randomly choose x number of records from the dataset and build a decision tree based on that. Then identify the number of decision trees needed for the algorithm and repeating the above process until all the data is covered. In the classification process, every decision tree will then give a prediction on the category of fraud or not a fraud. The category with the most votes or flags will be the result of the classification problem. The means of finding the correctness of the AI model is not accuracy rather a system of analysis called the ROC curve. The skewness of this curve helps in finding whether the model is accurate or not. Accuracy score a model is not very useful for prediction of correctness in the case of credit card fraud detection since the data provided in any person’s profile mostly consists of values where there is no fraud. So the accuracy will be biased due to this. The advantages of using the random forest for classification is that since every set of random feature values are being given equal weightage in the decision-making process, the resulting model is unbiased. It is a highly stable algorithm and can work well with outliers in the data also. The random forest can work with both numbers and categories. A new entry of data also does not affect the model since that can make for one new tree and does not impact the previously analyzed trees. This makes the AI solution highly dynamic. Fraud detection is a multifaceted problem and requires intensive data but AI solutions make the measures of identification very easy. To Know more how HUMACi will help you in building solutions in AI, please visit us at https://www.humaci.com/artificial-intelligence-training-research-program/