Karim Ben-Jafaar from Quadient delves into the peaks and valleys of AI, underscoring the substantial impact AI can have in thwarting fraud occurrences and cyber threats.
Generative AI has already brought about a revolutionary shift across various business domains, unlocking novel avenues and facilitating global digital transformation. Nevertheless, this progress is accompanied by notable perils.
AI’s developmental trajectory has a shadowy side. Tools like ChatGPT, representative of generative AI, present an enticing opening for malicious entities to orchestrate intricate digital felonies. Consequently, safeguarding businesses has become an arduous endeavor.
The financial realm, in particular, bears the brunt of an onslaught of cyber assaults, illustrated by instances such as fintech leader Revolut falling prey to multimillion-dollar scams, often before even realizing the breach.
Yet, within this landscape, AI retains the potential for positive contributions.
AI Magazine engaged in a conversation with Karim Ben-Jafaar, Senior VP at Quadient Accounts Payable by Beanworks, elucidating the ways AI can serve as a potent tool in countering digital fraud scenarios.
How exactly is AI contributing to the prevention of fraud within finance departments?
AI serves as a critical pillar in bolstering the capabilities of automated financial operations to combat the looming threat of fraud, a challenge that costs the UK nearly £200 billion (equivalent to around US$258 billion) on an annual basis.
As enterprises expand, the sheer volume of invoices demanding processing swells exponentially, reaching a juncture where manual procedures become unfeasible. This is due to the tremendous labor demands involved and the escalating peril of human errors. These errors can manifest as honest oversights or even deliberate transgressions, such as overlooking duplicate invoices or incorporating fabricated details.
The introduction of automation into the equation empowers finance teams to grapple with these issues effectively. By streamlining processes like data entry, shepherding invoices through approval stages, and closely monitoring payment progress, automation ensures efficiency, uniformity, and dependability.
Incorporating AI into the financial landscape alongside automation endows businesses with the capacity not only to process a higher volume of invoices but also to pinpoint and take action against the subtle indications of potential fraud.
Manually overseeing a substantial influx of invoices would be an insurmountable task for human teams. However, by adhering to consistent and replicable processes, AI can shoulder the responsibility of identifying anomalies. These anomalies might encompass irregular data entries on invoices, incorrect recipients for invoices, or other disruptions and deviations from standard protocols. AI then promptly signals these deviations to the designated teams for further examination. This proactive approach serves to halt fraudulent activities in their tracks, thereby safeguarding the financial integrity of the business.
While undoubtedly intricate, the success of this endeavor is underscored by a specific instance where AI of this nature enabled one of our clients to uncover counterfeit overpayments totaling over $10,000. This sum included instances of erroneous credit and duplicated invoices, all made possible through the power of AI-driven analysis.
To what extent will AI play a pivotal role in the future landscape of fraud prevention?
As enterprises embark on the journey of expansion and digitization, it becomes imperative for the finance sector to synchronize with the wave of digital transformation and the surging volume of invoices. These dual forces amass a staggering amount of data, a load that proves insurmountable for manual handling within finance functions. The specter of precision and the specter of human fallibility loom large, with the potential for counterfeit details or overlooked invoices casting shadows.
AI’s significance is set to be inseparable from automation, standing as an essential pillar in facilitating the growth of organizational operations while adeptly uncovering deviations.
Simultaneously, the progression toward sophisticated digitization within finance functions corresponds with a parallel advancement in the tactics employed by malevolent actors. Fraudsters are progressively honing their ability to evade human detection, whether it’s by conjuring counterfeit invoices or fabricating entire fictitious entities. Their art of deception perpetually evolves, introducing novel modes of perpetrating fraud.
The emergence of intricately refined AI-driven algorithms will emerge as a formidable defense against these deceptions. Such algorithms will be adept at instantaneously discerning these falsifications, leveraging automated scans to flag any irregularities and signaling potentially suspicious actions to the appropriate personnel. For instance, while AI might not directly uncover a specific scam, it’s adept at highlighting unusual changes in payment details or abrupt alterations in a company’s invoicing address.
Perhaps the most crucial aspect of this dynamic is the real-time nature of these alerts. The financial team gains the ability to act swiftly, preempting payments before they’re executed.
What hurdles must be surmounted in the journey to implement AI technology for the prevention of fraud?
Numerous organizations find themselves deprived of the advantages that stem from integrating AI into their financial operations due to entrenched organizational conservatism and the initial expenses associated with adoption. Granted, the introduction of AI and automation can indeed carry a hefty price tag and complexity, but the rewards they bring unquestionably overshadow the costs and any potential inconveniences.
Addressing this challenge necessitates the finance function to garner organizational endorsement by presenting a broader business rationale for AI deployment. For instance, especially in the prevailing economic climate, shielding against falling victim to fraud and hemorrhaging profits assumes unprecedented significance.
An AI-fortified finance function can spell the difference between swiftly detecting counterfeit irregularities and bolstering precision, ultimately mitigating the risk entirely.
Likewise, an AI-equipped to detect anomalies within automated procedures boasts applicability across a multitude of business domains, enabling the identification of budding concerns before they escalate into full-blown predicaments.
Another compelling argument teams can employ pertains to the potential reputational costs of succumbing to or facilitating fraudulent activities. Suppliers and customers harbor legitimate concerns about the potential repercussions on their own operations, whether it involves them becoming targets or encountering delays in payment.
Even if a given organization itself remains untainted, the prospect of collaborating with a partner that showcases an inability to identify or avert fraud is met with skepticism. Consequently, it’s imperative for the finance function to collaborate with other internal stakeholders to underscore the dividends yielded by AI-driven accounting tools.
