Detect and prevent fraudulent activities using data science solutions
There are a wide variety of threats posed to enterprises across multiple industries, whether internal or external. Fraudulent activity is the biggest threat that can compromise the integrity of your organization as well as affect your bottom line. Finding hackers and tracking their fraudulent activities is a tough task. You can avoid fraud by analyzing patterns of data and understanding those patterns can help you identify the type of fraud.
There are various types of fraud and risks a company faces such as identity theft, credit card fraud, malware, healthcare fraud, internet fraud, money laundering, insurance fraud, employee fraud, tax evasion and more.
The process of fraud analytics includes gathering and storing pertinent data and mining it for detecting patterns, discrepancies, and anomalies. The findings are then converted into insights that can help companies in managing potential threats before they happen as well as in creating a proactive fraud detection environment.
One of our clients, who is a reputed financial institution was experiencing recurrent fraud and heavy losses. They were also facing issues due to other fraudulent activities such as identity theft, credit card skimming, loan loss, cybercrime, money laundering, black money scenarios and more.
Our team of data scientists at Softweb gathered interactions between products/services, locations, and devices and then worked out those data points to individual users, customers, and employees. Depending upon the complexity involved, they used fraud detection techniques such as statistical data analysis, predictive analytics, and data mining to create better connections from raw data and then discovered which interactions expresses potential fraudulent behavior.
Also, our team with the help of powerful data visualization functionalities created rich graphical charts & visuals that convey exactly how fraudulent activity can occur in the future.