Looking at companies’ experiences, of various sizes and across all industries, I think we would all agree that fraud attacks often don’t come from where one would expect! Companies still rely too much on guesswork and empiric methods while investigating potentially fraudulent transactions.
And to make things worse, fraud patterns evolve quickly and constantly. Thus, as companies put in place measures to prevent fraud, perpetrators quickly adapt and find ways to circumvent them. There’s clearly a need for better processes and tools to enhance their fraud detection and investigation.
Investigators’ experience isn’t sufficient anymore
To analyse and understand how and where fraud happens, one can’t just rely on the experience and intuitions of even the best investigators, or the analysis of standard fraud reports and basic metrics. Also, the more common analytical tools appear ineffective to scan very high and fast-growing volumes of data – where critical information to understand fraud patterns and hidden paths is buried.
Moreover, the range of data to examine to properly identify fraud trends is increasingly diverse – structured and unstructured. More than ever, fraud detection is a Big Data problem!
Fast-developing predictive technologies offer great potential for improvement
On the other hand, predictive analysis technologies are fast developing, becoming more widely available and easier to use, yet more powerful. They can help companies get deep insights into how and where fraudulent transactions originate, and analyze changing fraud patterns, in order to enhance their fraud detection strategies and adapt faster to new types of attacks.
So the combination of traditional fraud management solutions complemented by predictive analytics not only enhances capabilities to detect fraud, but also contributes to better prevention of potential future fraud. It enables a deeper, more forensic approach against fraud, helping users to improve the effectiveness of their investigations by better focusing on new types of fraud risks, and continuously updating and refining their fraud detection strategies using the data from predictive analyses.
Today’s best fraud management and predictive analytics solutions have many benefits. They:
- Identify fraud patterns and trends more precisely: where fraud comes from, how it happens, who is involved, what areas of the business it impacts, and so on.
- Enable going after the less known and more complex patterns and networks, and detecting earlier to minimize the damage of cleverly hidden suspicious transactions.
- Provide the needed capabilities to analyze a wide variety and very high volume of data very fast, leveraging in-memory computing technology.
- Help fraud investigators by reducing false alerts resulting from inadequate fraud detection mechanisms— a critical issue today for many fraud investigators as they’re faced with an excessive workload of potential alerts to analyse, and wasted efforts as many turn out to be false positives.
Can predictive analytics benefit a wider audience?
The innovation brought by predictive analytics touches many other areas of the business, and in areas such as governance, risk and compliance (GRC), its use will develop to enable better predictability of risk, increased insight in areas of control weakness, support for internal audit programs, and so on.
These multiple applications create a high demand for experts such as data analysts and specialized business analysts, but the scarcity and high cost of these resources pushes for better usability of the tools. In the area of fraud in particular, invaluable expertise resides within fraud investigation teams who don’t have these skills as their primary asset.
For them, and others, it’s important that new predictive technologies become approachable for the non-experts, and more readily consumable by their most interested audience—which is just what the latest generations of predictive technologies enable.
For more on security strategies, see Cybersecurity: Is It Time To Change Our Mindset?