Proactively Counter Fraud and Automate Your Payments Controls

Fraud and internal controls violations significantly impact the operational and financial health of organizations of all sizes and scale. CFOs must address these threats while under the constant barrage of ever-increasing and more technologically savvy criminals. Most of the controls at the disposal of CFOs and treasurers are comprised of largely manual and detective processes and procedures. With 74% of companies being subject to attempted fraud attacks¹, the need to preemptively address fraud and policy violations is principal.

The identification of payments fraud, policy or procedural compliance issues is still largely a detective exercise, and unfortunately identification often doesn’t occur until payment files have already been transmitted to the partner bank for execution. Days or weeks later, when the paying company realizes a fraud event occurred, then a “scramble” is on to retrace and recall payments, which at this point is often futile.

For internal compliance issues, like paying too early, misapplication of terms, or paying without proper approvals, these incidents may never fully be understood or caught, particularly when there are disparate systems being used for generating payments or a lack of detection capabilities. Even when approvals and additional approvals are logically set up within an ERP or your TMS, the managers or senior leadership often have little understanding of the source of the payment or the justification. These manual, often unreliable controls can now be replaced with automation and effective preemptive validations and reports.

Layered Fraud Protection with Proactive Payments Governance

Many organizations still have payments landscapes characterized by a lack of up-front, early visibility and no preemptive countermeasures against fraud attacks. With the level of visibility and expanded threat levels, this is no longer acceptable in today’s operating climate. Weaknesses are easily exploited with the increasingly advanced technology criminals have at their disposal. Additionally, the reactive “expeditions” conducted by AP, treasury, or accounting to track down lost payments, or to discover the root causes of fraudulent and erroneous payments, can involve scans of thousands of transactions as well as change logs within the ERP to attempt to identify the point of attack.

Preemptive detection of payment compliance issues such as duplicate payment checks, critical field changes fraud attacks and fraudulent issues is now available through layered, pervasive, automated solutions with fraud protection and anomaly identification built in. Through the easy onboarding of these new solutions, along with a payment aggregation and consolidation model for all corporate payments (including treasury, AP, ad hoc or non-invoice payments) gives companies the ability to mitigate risk while saving costs. Kyriba’s Fraud Prevention solution, a part of the Kyriba Payments product line, gives our customers the ability to create a payments lifecycle where proposed and aggregated payments are scrutinized and assessed for compliance, well before file transmission to the executing banking partner. Now companies can intelligently and proactively apply standardized controls across ERPs, while gaining a holistic view on overall payments governance.

Automation Through Artificial Intelligence (AI) and Machine Learning (ML)

AI and ML technologies are now the foundation to bringing proactive, preemptive controls automation through the identification of subtle patterns in payment behavior, plus the ability to analyze vast amounts of data while combining and analyzing sometimes diverse, unrelated data sets for weaknesses. Kyriba’s solution uses these technologies to apply both rulesets and machine learning patterns that may indicate fraud. The scalability and expanded use of data, across multiple datasets, is the key to improving the results from our solutions. Some examples of how AI and ML are used within Kyriba’s Fraud solution includes instances where there are new vendors or counterparties, changes to information on invoices, payment method changes, or unauthorized or unsubstantiated changes to any information related to vendor master data.

Machine learning is used by Kyriba in identifying payment anomalies and compliance issues based on transaction history, as well. Kyriba drives pattern identification in the immense amounts of your accumulated transactional data and matches that in real-time to your specific transactions to identify potential issues. This added layer of protection looks for behaviors that may not be identified by the human eye – timing of invoice receipt or change in the frequency of payment requests. The system continually adapts based on the information that it is tracking and provides suggestions when it identifies potentially fraudulent behavior.

A Solution for CFOs, Treasurers, and the CIO

The Kyriba solution comprises five key capabilities: visibility, daily reconciliation, standard payment approval processes, enforcement of treasury policies, and alignment of treasury controls with information security policies, to deliver a more comprehensive and proactive approach to stopping payment fraud. All of these capabilities are delivered as real-time solutions using both AI and ML to ably identify and stop payments fraud. As a leader in the delivery of banking connectivity, payments initiation, and transmission, with the added benefit of fraud prevention and detection a core piece of our offering.

For more information, visit our Kyriba Payments resource center and reach out for a demo.
https://www.kyriba.com/solutions/kyriba-payments/

References

1. https://www.afponline.org/publications-data-tools/reports/survey-research-economic-data/Details/payments-fraud/