Combat cyber fraud with Raals


Cybercrimes are traditional crimes, which can be increased in their scale or reach by the use of computers, computer networks, or other forms of information communications technology (ICT). Cybercriminals may seek to obtain personal and financial data for fraudulent purposes.


A few examples of valuable data types are: ​

  • ​personal information (names, bank details, and National Insurance numbers); ​ 

  • company accounts; ​ 

  • client databases; ​ 

  • intellectual property (such as new products or inventions from the organization).




Breach costs could reach $3.92 million.


According to research on cybercrime done in 2019 on analyzing breaches and their effects, a growing number of breaches are costing businesses millions of dollars. The percentage of exposed sensitive files is highest in the financial and manufacturing sectors - 21%. The most sensitive information was exposed in the financial services sector, with an average of 352,771 files, followed by 113,491 files in the healthcare, pharmaceutical, and biotechnology sectors. Studies show that 63.8% of firms have experienced cybercrime as a result of the rise in phishing and social engineering assaults in 2019.



Only 5% of corporate files are sufficiently secured.


Many companies' corporate structures don't include or maintain folder security protocols. Due to this neglect, statistics on cybercrime demonstrate that only a very small percentage of firms have successfully implemented internal policies that will genuinely protect company files.



Use RAALS to protect your data.


In the struggle against computer-related crimes like bank fraud, data breaches, and cyber-attacks, RAALS aids law enforcement. RAALS aids in the automation of procedures for examining cybercrime. Usually, an investigator spends between 60 and 70 percent of their time obtaining information about a subject. Without the need for human intervention, RAALS may direct computers to automatically search and retrieve data, conduct database queries, and gather data from third-party data sources. By doing this, the time to case detection is decreased by 20 to 30 percent.

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