7 June 2019

Fraud Prevention with Data Analytics

Fraud can arise in different types: from credit applications, fraudulent transactions, identity theft, false claims of insurance, among others. This problem undoubtedly haunts companies of different sizes and sectors, and brings with it severe damage and losses of value to organizations. And if digitalization brought several benefits to companies, the truth is that it has brought new challenges in security and risk management issues.

With the evolution of technology, frauds have also been gaining more sophistication, not only because of the increase in data volume that companies are concentrating more and more, but also because they offer online sales and forms of payment for example, creating an amount of extremely desirable data to circulate for the practice of this crime.

The study by consulting company Marsh, “The View of Portuguese Companies on Risks,” published in April 2018, reveals that cyberattacks are at the first spots of risks tops that the world and companies will face. According to this study, in relation to 2017, this risk increased from 45% to 65%, with respect to the top 5 risks that the world will face, and from 36% to 57%, regarding the first five risks that companies will face.

But how can you protect your business? How can you prevent fraud in your organization and prevent it from causing harm? What steps should be taken?

With the speed of new attacks, existing technologies make it possible to detect fraud in time and, above all, play a key role in enabling companies to prevent fraud.

Thus, Data Analytics solutions allow to aggregate large volumes of data and extract insights to detect fraudulent acts and anomalies by identifying default fraud behaviours before they even happen.

We come across two essential elements: detection and anticipation. In detection, a pattern and actions recurrence analysis is made to ensure that the occurrence is indeed fraudulent. In anticipation, the challenge is to foresee crimes before they even happen, that is, suspicious behaviours are analysed in the network, actions that escape a certain pattern, and other traces left in a system by fraudulent ones. However, the difficulty in this anticipation is big, since they are dynamic actions that change and adapt over time.

Using Analytics and Big Data tools for fraud prevention is a natural and indispensable trend. These solutions help to track information, identify suspicious patterns, and anomalous behaviours. The use of Data Analytics solutions, along Artificial Intelligence and Machine Learning, also provides greater data security.

In this way, you manage to have a complete view of the various sources and channels, thus allowing a significant mitigation of risks and fraud through historical analysis, identification of patterns and anomalous behaviours.

Certainly, the use of technology enables to achieve higher levels of security more effectively, which allows to maintain competitive advantage, protecting yourself against unwanted attacks and at a low cost. In addition, you get a greater knowledge of your clients, being able to offer a service with superior quality.

6 behaviours that help you detect fraudulent acts in the company

  • Identify the factors and risk profiles in order to block actions of these profiles
  • It is a constant process of monitoring, identification, management and learning
  • There are several technological solutions that help you to control operations more accurately, protecting data, and previously detecting anomalous situations
  • With Machine Learning, you can predict threats more easily by crossing data and behaviours, allowing to identify fraud patterns before they occur.
  • Using Data Analytics allows automating the process of detecting and combating fraud, making the same easier and at a lower cost.
  • Filter strange content, usually attempts to intrude on a company’s servers are done through emails with hidden malware.