With the expansion of data, economic crime can no longer be an accepted cost of the business. Fraud schemes are growing more sophisticated with disadvantages related to cyber security. Corporate Fraud in laymen’s language is “Activities undertaken by an individual or company that are done in a dishonest or illegal manner, and are designed to give an advantage to the perpetrating individual or company. Corporate fraud schemes go beyond the scope of an employee’s stated position, and are marked by their complexity and economic impact on the business, other employees and outside parties”.

Shareholders and customers are equally sensitive towards financial fraud. The cost can include long term erosion of brand value and customer trust, with customer often switching to business switching to the competition to protect personal and corporate assets. Cyber attacks like ransomware are using modern techniques to enter the system and corrupt the data as a whole. A PriceWaterhouseCoopers survey on Global Economic Crime 2016 suggests that 36% that is one of three organisations are a victim of economic fraud. 44% organisations also believed that local laws are not good enough to investigate economic crimes, leaving the responsibility of fighting fraud on the organisation.

Top 5 economic crimes in India – Asset misappropriation, Cyber crime, Bribery & Corruption, Procurement fraud, accounting fraud. – Source, PWC Global Economic Crime Survey, 2016. 

Digital technology continues to transform and disrupt the world of business, exposing the organisation to opportunities and threats. It is evident that cyber crime continues to escalate as the second most used source of crime. Technology has the answer to the ills of technology. Big data analytics can act as a solution to prevent frauds.

We live in the age of information. More and more information is stored on multiple databases as the well as the cloud. The data procured turns into knowledge and creates a demand for new and powerful data mining tools. Using data mining new patterns can be discovered. If data mining results in discovering meaningful patterns, data turns into information. Patterns that are valid and potentially useful are not merely information, but knowledge. These patterns have a potential to detect fraud, especially in a regulatory driven environment.

Best Practices for Implementing Big Data

  • Start with small and specific uses for big data: The first thing organizations should do is identify one or two business problems that can be resolved by improving fraud detection, and then dedicate the R&D resources to develop solutions. This type of ‘outcome-based thinking’ will ensure the business success of the initiative.
  • Ensure you’re working with high-quality data: Take the time on the back end to ensure you are collecting the proper data and are separating the signal from the noise to allow for proper data analysis.
  • Know your regulatory environment: Understand the boundaries for using customer data and the relevant privacy laws. This will continue to be a challenge for organisations but can be managed with the right approach.
  • Ensure IT and business units are collaborating: Implementing big data systems can be disruptive to an organization. Adopt destination-driven thinking where you and your team articulate and agree upon clear goals, and then evangelize your big data strategy across the organization to gain broad buy-in. These clear objectives will motivate teams to work through inevitable friction.

Big data allows corporations to greatly enhance the speed of fraud detection and prediction using a massive amount of data from sources like point of sale, digital media and customer databases. The biggest threat is fraud is transperency. Technologies like Big Data, Data Mining, Mobile Applications and advanced forensic tools are gonna help us deal better with fraud and reduce the levels of corruption.

Source – PWC Global Economic Crime Survey, banktech.com, dqindia.com, weforum.org, IBM.

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