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Financial fraud has been a major concern for many financial institutes and investing in a framework that detects fraud beforehand has become necessary and important. Predicting frauds in advance prevents unauthorized people from obtaining money through accounts through pretenses based on the hacker’s or the customer’s account behavior. It also allows for predicting which customer would be defaulting on a loan if fraudulent behavior through her transactions is noticed. Fraud detection has its applications in various industries, with a dire need in the banking and insurance sector. The manual procedure for predicting frauds is hectic, time-consuming, and costly, the major drawback involving no real-time detection. It takes time to analyze and mark a transaction as fraudulent due to various reasons. This creates an opportunity for Machine Learning to show its expertise. ML models using their high computing powers can analyze huge data sets to find patterns that lead to frauds when they a properly trained and deployed. Using machine learning to analyze customer transactional patterns, and detecting any outliers from the normal behavior can be classified as fraud/spam. The ML models can also work in real-time and detect very early and quite quickly if there is going to be a fraud. The fraud prediction accuracy of the model can be improved by feeding past transactional data into the machine learning model. This technique is cost-effective, fast, scalable, robust, accurate, and optimal for the business. The ability to detect and eliminate fraud, ultimately, results in effective and stronger management of the financial institution’s functionality and an increase in trust by the customers towards the entity. An organization using machine learning as a tool can also save a lot of money as obviously due to fewer frauds but also on various underwriting or claim and account verification tasks.
How Can ANAI Help?
ANAI is an end-to-end solution, which plays the role of automating machine learning operations, with the only requirement of feeding data into the system, and from then onwards the process of data handling and analysis, correct feature extraction, with ML model building and deployment being completely automated.
Using ANAI, any user can process the data efficiently to make it ready for the model building pipeline and further data use. This step aims to transform data into an easily interpreted form for the ML model, thus making it easy for the model to make further predictions. But for the data to be ML-ready, it needs to satisfy certain conditions and meet some requirements. Hence, the data undergoes a few pre-processing steps like data analysis, wrangling, transformation, encoding, etc.
Here, we are attempting to run a huge dataset with around a million different values through the ANAI platform to show how the platform can be used for training models hat can detect fraud from a financial data set.