Big Data and the Assessment, Determination, and Treatment of Fraud
As a “white collar” crime, fraud ranks pretty far down the list of general public worries. However, while fraud is less traumatising than other more violent forms of crime, it still represents a major problem across the globe, especially for businesses.
Reports from the Association of Certified Fraud Examiners in 2013 highlighted the risks of fraud in Singapore, and found that Singaporean business is worryingly under prepared and even “naive” in their approach to fraud.
In Australia, the risks are similarly concerning. The Australian Payments Clearing Association recently released statistics which showed that card-not-present fraud alone amounted to AUD$363million in 2015—over AUD$200 million more than in 2010.
So, what can we do about this? Well, if the first stage of combating an issue is recognition—which we have covered above—the second stage must be deeper understanding. It is through understanding that we can stay one step ahead of the fraudsters and take steps to safeguard our organisations against their activity. This is where Big Data plays an important role.
Big Data—what is it All about?
Data is nothing new, in business or in daily life. We have always sought to build our level of knowledge and understanding by gathering facts and information, and by wielding these tools appropriately. However, we are now in the age of understanding, by exposing underlying insight. The ways we gather insight must therefore be further developed.
Big Data is simply data on a vast scale, but it is not just about size. Big data encompasses different data sources, and disparate datasets, much of which is now in an unstructured form. Advances in technology, in capability, and in practice have enabled us to gather, collate, store, present, and interpret unprecedented amounts and types of data, giving us the opportunity to unlock insight like never before. This is Big Data.
Identifying Fraud Quickly and Having a Proactive Approach
By wielding Big Data insights, we can quickly and easily identify suspicious acts of likely fraud. If we suspect that a fraudulent activity has taken place, we need no longer agonise over whether or not it is legitimate. Instead, we merely apply insights gained from Big Data and use this understanding to separate genuine customer interactions from fraudulent ones. This saves businesses and institutions time, effort, and, potentially, a lot of money when it comes to dealing with fraud. The ability to readily see beyond the “norm” is enhanced as the data you have available increases.
Of course, it is better to get the jump on potential fraudsters than to clean up the messes they make. Developing our understanding of these activities through the collection and interpretation of data pre-arms us and gives us the means by which we can built robust safeguards against fraud. The benchmarks for such early fraud detection can come from historical transactions and other data whereby what ultimately was fraud, followed a pattern. By referring to and looking for similar patterns along this benchmarked “path to fraud” we can intervene before it is too late. That is the power of Big Data.
By examining behavioural patterns and outcomes, we can understand what fraud looks like, and prime our defences to prevent it in the future. This could involve implementing a new culture in the workplace, giving each and every team member the tools they need to fight fraud. Alternatively, a business can use this information to set up automatic checks and safeguards in the form of data-dependent software and platforms to stop fraud in its tracks. This is data driven decision-making.
Automatic Protection via Machine Learning
Taking this approach a step further, we arrive at User Entity Behavioural Analytics or UEBA. This is a machine learning concept which uses the understanding derived from Big Data to prevent fraud. Other automated systems which use pre-existing, pre-programmed datasets to spot potential fraudulent activities are long established and continue to provide effective defence for businesses. However, UEBA systems go beyond this, using pre-existing data as a starting point and then gathering their own data as they actively “learn” to recognise the red flags associated with fraud.
The result is a system which requires far less maintenance and human input, and offers a more robust defence against fraud than anything that has gone before. The concept of UEBA is still in development but we can expect this sort of machine learning tool—working on a Big Data-based platform—to be on the frontline of fraud prevention methods in the near future.
A Cautious Approach
However, as is always the case regarding fraud, a cautious approach is required. Too many organisations are focusing on amassing large amounts of data and then either becoming overwhelmed or simply resting on their laurels. While more data is certainly a good thing—in fact, there is no such thing as too much data—it is what we do with all that data that really counts. Should we really need teams of data scientists to figure out how to use the data, or should this be in the hands of the business users?
Interpretation is everything when it comes to Big Data. Unless we have the tools and know-how to shape and mould our data into a genuinely useful weapon with which to fight fraud, we remain vulnerable. We have the data we need; it is time to put it to good use.
Data management systems such as Latize Ulysses can help businesses to achieve this high level of Big Data interpretation and understanding, taking their datasets and turning them into actively useful resources for insight. The end result could be anything from a business with improved operations and more profit to stopping fraud in its tracks.