Global, scalable payables automation for high-velocity companies.
1001-5000 employees
Financial Services
Americas
Tipalti is the leading end-to-end cloud solution for automating every phase of the supplier payments process. Its comprehensive accounts payable automation tool eliminates the need for time-consuming and complex spreadsheets. Through a single intuitive platform, organisations can oversee everything from supplier onboarding to invoice management, reducing up to 80% of the typical workload.
Paired with an exceptional customer-first approach, Tipalti has achieved excellent customer retention rates with big brands like Canva, Cazoo, GoDaddy and Twitch. On a weekly basis, Tipalti supports thousands of organizations paying millions of suppliers. For a single customer, this can amount to tens of thousands of daily transactions. They needed an automated transaction monitoring solution with the capacity to address and monitor all their transactions securely and efficiently.
AML transaction monitoring
High volumes made it difficult for Tipalti to monitor transactions for AML (anti-money laundering). Screening for unusual activity or patterns of activity and the ability to receive alerts for suspicious transactions was critical to prevent money laundering and ensure Tipalti met with regulatory compliance.
Workflow inefficiency
The laborious process of vetting suppliers, monitoring and altering transactions, verifying bank accounts and maintaining extensive financial reporting across more than 190 countries created undue workflow friction that needed to be streamlined to properly execute a robust compliance process.
Despite an 80% increase in transaction volumes in the first year, Tipalti maintained a consistent ratio of alerts, with a monthly average of 0.05% to 0.08% in the busiest months.
Tipalti needed an automated solution that could monitor transactions securely, efficiently and continuously in real time. Following a thorough vendor evaluation, Tipalti eventually chose our automated AML transaction monitoring system to supercharge their AML compliance process.
GBG Compliance delivers the risk-based approach to AML transaction monitoring favoured by regulators around the world. The automated assessment process screens transactions in real time, applying AML rules and contextual analysis to individuals and entities, activities and behaviours, issuing alerts when anomalies and deviations trigger those rules and flag a transaction as suspicious.
The system delivered a built-in library of pre-configured AML rules, plus simple, code-free rule customisation to reflect Tipalti's own risk-based AML strategy. This helped to seamlessly adapt to the global and evolving regulatory landscape that Tipalti operates in and the compliance demands of different jurisdictions, products and services.
The transaction monitoring alerts generated by GBG Compliance feed directly into its enterprise case management dashboards, business intelligence and regulatory transaction reporting. With AML risk alerts rated and prioritised according to severity, Tipalti was able to continuously stay on top of alerts, triage cases and prioritise case management, focussing on critical alerts and streamlining its AML monitoring efforts even as the number of daily transactions that flow through its business continued to scale.
Tipalti has turned around the task of transaction monitoring. It has reduced false positives and enabled the business to focus on the escalation and management of high-risk cases, making the best use of its talented compliance team and building a solid regulatory reporting track record. Tipalti’s risk-based approach continues to expand on the AML transaction typologies being monitored through the GBG Compliance platform.
0.05% - 0.08%
Despite an 80% increase in transaction volumes in the first year, Tipalti maintained a consistent ratio of alerts, with a monthly average of 0.05% to 0.08% in the busiest months.
400%
Supported spikes in transactions over 400% when compared to the monthly median, accommodating Tipalti’s explosive growth.
Machine Learning
Risk models based on unsupervised machine learning adapted with typology models easily adjusting to address requirements.