The Importance of Data and Analytics in Fraud Prevention



Data

The explosion of data and easy access to systems are driving the growth of fraud analytics

In some much-needed good news for the banking sector, the RBI recently revealed that public sector banks reported a more than 51% dip in the amount involved in frauds in FY 22. The number of cases of fraud has also declined, perhaps due to the better adoption of fraud prevention and risk monitoring systems. The proliferation of data-science-backed technology tools makes it feasible for banks as well as all other organizations to detect fraud risks early and take steps to mitigate them, resulting in minimal losses.

The explosion of data and easy access to it from internal and external systems are driving the growth of fraud analytics.

Fraud analytics is a confluence of various quantitative sciences, such as Business Intelligence (BI), data mining, Machine Learning (ML), and Artificial Intelligence (AI), to develop solutions that help detect, understand, predict, and prevent fraud. A multitude of technologies is involved in fraud analytics.

One of the most important aspects of a fraud mitigation plan is constant risk monitoring. Traditional methods are very laborious and require many man-hours from highly skilled personnel to be effective. Data-driven fraud analytics systems can perform the same risk monitoring activity in a matter of minutes and enable faster decision-making.

According to a report by the Association of Certified Fraud Examiners (ACFE), a typical fraud lasts 12 months before being detected and causes a median loss of $117,000 per case. Deploying data analytics tools can drastically reduce the detection time and minimize the loss.

Another advantage of using data analytics tools for fraud mitigation is that there is no limit on the number of sources the data can be pulled from. Cross-verifying this data can also be done rapidly. The added benefit is that text analytics tools can be programmed to scan through unstructured data (eg., social media feeds) which often provides crucial clues about fraud.

Most modern risk monitoring tools based on data science enable surveillance and provide Early Warning Signals (EWS) about any deterioration in a business’ health. Such data-driven EWS tools provide dynamic risk scores that change automatically based on the new data points that the tools have gathered. Lenders or creditors can take quick action to reduce their exposure to a business if the emerging data about it is negative and its risk score has dropped. Similar tools can help underwriters in the insurance industry as well.

Fraud analytics tools can also analyze vast swathes of Know Your Customer (KYC)  and payment transaction data to identify fraudulent transactions and flag suspicious activity that deviates from regular transaction patterns. This helps banks, NBFCs, fintech, and insurance companies in fraud prevention as well as for Anti-money Laundering (AML) activities. These smart tools are especially important for the payments industry to prevent and trace fraud.

Data-backed fraud detection platforms usually have dashboards that let users keep track of Key Performance Indicators (KPIs) in real-time. The infographics and data visualization on these platforms provide a deeper understanding of connections between entities that are perpetrating fraud and flag the irregular transactions that they may be undertaking.   

Some of the other benefits of deploying fraud analytics are:
  • It is possible to automate the scan and search of all available transactions to spot red flags.
  • It takes very little effort to merge, normalize, and compare data from different systems and sources
  • It is feasible to calculate the financial dimensions of the fraud more accurately.
  • Earlier fraud detection helps lenders or creditors to minimize their magnitude
  • Older fraud prevention systems used sampling to monitor for fraud. New-age systems scan the entire data set, eliminating sampling errors.
  • Another way in which these systems save time is by automating repetitive processes thus reducing human intervention.
  • In the medium-to-long run, automated fraud analytics systems prove to be cost-effective. 

The ACFE has found that globally, on average, organizations lose 5% of their annual revenue to frauds. Despite this, many organizations have been reluctant to adopt fraud analytics for three significant reasons:

First is the fear that fraud analytics systems will replace people. It is important to know that human input cannot be completely replaced by technology. The analytics can only help identify red flags but skilled professionals have to review the evidence and determine if fraud is actually underway.

The second is the fear of cost. Fraud analytics systems and technology may not always show a return on investment in the short run. 

The third fear is that of data being compromised if fraud analytics systems are introduced. Digital transformation entails more vulnerability to data theft and cyber risk if appropriate safeguards are not put in place. Deploying such data encryption and protection tools requires extra investment and expertise.

However, the companies, banks, and fintechs that choose inaction are more at risk than those adopting these systems. There is ample evidence to prove that companies are better off investing in data analytics technology to prevent fraud, especially with fraudsters becoming smarter and tech-savvy, and most financial transactions going digital. Detecting and preventing fraud is not a one-time effort. Constant monitoring and surveillance to detect any irregularities at all stages of a transaction is the only way to minimize the risk of fraud, and this herculean task is almost impossible without using new technologies, tools, and platforms.

Author:

Mohan Ramaswamy, Founder & CEO, Rubix Data Sciences

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