Top Trends For Fraud Management System Implementation

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In a survey carried out by PwC, the 2020 Global Economic Crime and Fraud Survey, about 47% of enterprises globally reported fraudulent attacks. 13% of these organisations reportedly lost more than $50 million because of these fraudulent attacks These statistics indicate the growing rate of cyberattacks on the global stage.

The persistent challenge is that cyberattackers are becoming more and more advanced. They continuously upgrade their attack modes so that enterprises cannot be protected on a single pattern or parameter-identifying protocol. Fraud risks are real and ever-advancing. Legacy methods of protection are no longer protective enough. Existing safety protocols must evolve and should use enterprise data and behaviour patterns to identify and predict irregular behaviour patterns before any damage is done.
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Using a fraud management system is longer an additional layer of security but a fundamental part of enterprise security.

Top Trends for Enterprise-Level Fraud Management Solutions:

1. Use of Advanced Analytics:

Before the recent advancements in technology, analysing all an enterprise’s data for the purpose of fraud detection was time-consuming and impractical. However, with the latest data analytics tools and functionalities, enterprises can analyse large volumes of data rapidly and identify irregular behaviour patterns that could lead to fraud.

The newest analytics solutions combine multiple analytical methodologies to help in identifying non-intuitive and subtle behaviour patterns. Some examples of these analytics methodologies are as follows:

  • Pattern analysis compares past behaviour with the current behaviour of users and their peer groups to discover odd patterns.
  • Model development uses statistical analysis to offer quantitative insight into odd human activity.

2. Real-time Monitoring:

100,000’s transactions take place in a single minute each day. Financial organisations don’t use just data from consumer transactions to prevent bank fraud. Today these organisations also collect and analyse third-party vendor data and user data from social media platforms to enhance their fraud detection skills. There is a wide range of data processing and collection systems available for enterprises today. Therefore, a wide range of data can be assimilated, collected and processed using fraud management solutions in real-time.

3. Behaviour analytics approach:

Enterprise fraud management systems today are using adaptive analytics to identify new modes of fraud attacks before the incident can occur. An approach that depends on behavioural analytics uses ML to identify unknown risks by gathering behavioural data across channels and sources to analyse every new activity.

Also Read: Payment Gateway vs Merchant Online Payment Account: Key Features and Differences

The objective of fraud management solutions is to prevent fraudulent behaviour before it occurs by identifying fraudulent behaviour and stopping it before it compromises customer data. Here, data is used to create deep historical profiles for every user and entity. It then builds massive data sets for each of these profiles.

4. Next-generation authentication:

Cybercrime and fraud attacks generally take place because of several avoidable steps such as weak user passwords. Finance companies today are looking to improve transaction security using strong authentication methods such as risk-based authentication or two-factor authentication that is mobile-dependent. The challenging part is to get the right mix of enhanced security and authentication while ensuring a seamless user journey.

With the right fraud management system implementation combined with predictive and advanced analytics technologies, businesses can keep systems safe and keep customer data secure.

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