Prior to the disruption caused by the pandemic over the past two years, fraud cost businesses more than $5 trillion annually. But an increasingly distributed workforce and the acceleration of digital transformation initiatives have irrevocably changed the corporate landscape.
In doing so, the perfect climate has been created for fraud to become even more of a challenge.
A global economic crime survey has found that one in three South African respondents cite distrust as being the most significant emotional impact of fraud.
Brand damage, loss of market position, employee morale, and lost future opportunities remain unquantified. Consideration must also be paid to the availability of more sophisticated technologies such as artificial intelligence (AI), machine learning (ML), and automation that benefit organisations and criminals alike.
Technology shifts
Our recent research indicated that the pressure to safeguard against financial crime s in this connected landscape has resulted in a third of financial institutions accelerating their AI and ML adoption for anti-money laundering (AML) technology.
In the same survey, 28% of large financial institutions consider themselves to be fast adopters of AI technology while 16% of smaller financial institutions also view themselves as industry leaders in AI adoption.
The shift in consumer behaviour due to the pandemic has forced many financial institutions to move away from static, rules-based monitoring strategies as they are not accurate or adaptive to the behavioural decisioning systems required in an AI-driven landscape.
Additionally, our insurance fraud technology study has found that anti-fraud technology is flourishing due to these and other factors.
Automated red flags, predictive modelling, text mining, and reporting capability are among insurers’ most used anti-fraud technologies. They are also embracing photo analysis technology to authenticate claim damage, identify digitally altered images, and index pictures submitted in other claims.
Tying this together is how organisations are embracing financial intelligence that unite multiple information sources with analytics connecting the dots at scale to form a true, all-source-intelligence platform. For instance, sophisticated algorithms have been deployed not only to link data, but to identify the significance of a contact and predict risk of infection from malware.
Financial crime variety
Fraud and cyberattacks are just two concerns. But money laundering has become especially pervasive recently with criminals relying on legitimate businesses through which to channel their ‘dirty’ money. This is where shell companies and offshore financial transactions become part of the picture.
And then there is procurement fraud to consider with some suggesting it has become one of the most common economic crimes around the world. Because it takes so many forms, procurement fraud is very difficult to detect and investigate. Manual detection is futile with organisations requiring the right combination of advanced analytic techniques to bolster their defences against these fraudsters.
Fundamentally, today’s payment ecosystem has broadened well beyond traditional banks. Fintechs, digital banks, and payment service providers are driving new, alternative payment services.
Payments have become increasingly cashless and opened doors for new, faster payment types. But, with all the innovation comes new fraud threats – and they are emerging on different timelines in various parts of the world.
AI weaponry
For these, and other financial crime s, it has become imperative to embrace continuous monitoring solutions. These can pull millions of records from back-office systems, including purchase orders, purchase requests, invoices, payments and other accounts payable data, and supplier data.
After extracting the data, the solutions apply AI to cleanse it at scale.
The system can enhance the data by drawing on external sources, such as company registers, information about politically exposed people and transparency indexes.
Then it processes the data using alert generation processes based on mathematical algorithms and models, such as clustering and link analysis. The outcome is a real-time risk score for both transactions and vendors.
Depending on the industry and the company needs, businesses can use analytics to check for bid rigging, duplicate invoices, travel expenses, returns, fidelity cards, subscriptions, and more.
It all comes down to embracing tools like AI and ML to enhance what human experts do. Fighting financial crime is a never-ending battle. With criminals using evolving technology to bypass organisational defences, it is up to the companies to fight fire with fire.
