Real-World ROI - Cutting Onboarding from 6 Weeks to 1 Day

For innovative payment companies, transaction speed is everything. 

This holds true for business clients, often referred to as “merchants,” who use payment services to process customer transactions. Unfortunately, the onboarding process for these services remains a major roadblock, often taking 30 to 45 days. This delay not only frustrates new clients but also defers revenue and strains operational resources. 

The root cause is often a reliance on manual, fragmented workflows for fraud and compliance reviews. However, real-world results from leading financial institutions, shared on our expert webinar “Beyond Compliance - Transforming Client Experience Through Intelligent Data,” show a clear path forward. By shifting from manual processes to AI-supported efficiency, one payments company reduced its onboarding timeline from weeks to under a day.

This transformation highlights the measurable gains possible with intelligent detection and data orchestration. It’s not just about speed; it’s about creating a connected risk data layer that directly supports business growth.

The High Cost of Disconnection

Outdated, manual workflows incur significant hidden costs that affect both revenue and client satisfaction. When onboarding is slow, the time-to-value for new clients stretches out, delaying revenue realization and giving competitors an opening. 

The problem isn't a lack of data; it's a lack of connection. Legacy systems force teams to conduct duplicate reviews across fraud, AML, and compliance departments. This fragmentation creates "risk blind spots" where analysts act as data couriers rather than decision-makers. They hunt for context across disparate systems, leading to redundant checks and human error.

As Kevin Shine notes in the webinar, "We'd like to ideally measure twice, cut once." Instead, many organizations ask clients for the same information multiple times, demoralizing staff and frustrating customers. This environment of inefficiency leads to inconsistent outcomes and analyst fatigue, undermining the very security it's meant to ensure.

Intelligent Automation in Action

The contrast between manual inefficiency and automated precision is stark. One payments company, for example, used FraudNet’s rules-based workflows and machine learning threshold detection to completely overhaul its onboarding. What used to take 30 to 45 days to get a client fully transacting now happens in under a day. This is the power of intelligent automation.

Another financial services client faced an overwhelming number of alerts. By applying machine learning and configurable alerts, they prioritized reviews and focused only on high-risk cases. The result was a 93% reduction in their alert queue. This isn't just an improvement; it's a dramatic increase in operational efficiency.

The Downstream Impact

The benefits of automation extend far beyond the initial onboarding phase, creating a positive ripple effect across the entire organization. Faster onboarding directly improves the client experience, which is a key driver of retention. When you can deliver value almost immediately, you build a stronger, more trusting relationship from day one.

Reducing the volume of manual reviews also has a profound impact on your team. It frees up skilled analysts from tedious, repetitive tasks, allowing them to focus on what they do best: conducting deeper investigations into complex, high-risk cases. When the right risk data is organized in one place, they can stop hunting for context and start making decisions.

As stated in the webinar, "Less garbage in means less garbage downstream." Cleaner, more accurate data at the point of entry strengthens compliance, simplifies audit readiness, and ensures that downstream processes are built on a solid foundation.

Machine Learning and Anomaly Detection for Continuous Optimization

The true power of an intelligent risk management platform lies in its ability to learn and adapt. Everyone is talking about AI, but AI is only as good as the data you feed it. Machine learning models continuously analyze vast datasets, both from the client and cross-referenced against a global consortium, to identify patterns and suggest optimized detection thresholds.

This system creates a continuous feedback loop. Every review and decision helps the models become smarter and more accurate over time. This ongoing optimization allows teams to move away from a reactive posture, where they are buried under a mountain of false positives, to a proactive one. Fraud doesn’t wait for annual reviews, and neither should your risk profiles. Orchestration keeps merchant profiles alive and evolving.

What Success Looks Like

The value of intelligent automation is not abstract; it can be quantified with clear, compelling metrics. Financial institutions that embrace this technology see transformative results that directly impact their bottom line and competitive standing.

The numbers speak for themselves:

• 90%+ faster onboarding time: Activating clients in hours instead of weeks accelerates revenue and enhances the customer experience.
• 80–95% fewer manual alerts: Drastically reduced alert queues free up analyst capacity and cut operational costs.
• Higher analyst productivity: Teams can focus on high-impact investigations, improving risk detection, and overall security.

These outcomes demonstrate that automation is a strategic investment that delivers a powerful return. It allows organizations to scale efficiently, improve accuracy, and build a more agile and resilient risk management function.

Ready to Transform Your Operations?

Leading payments and banking institutions are accelerating their time-to-value with intelligent automation. By moving beyond outdated manual processes, they are creating a significant competitive advantage.

To see these real-world results for yourself, watch our full webinar, Beyond Compliance: Transforming Client Experience Through Intelligent Data. Learn how you can achieve similar gains in efficiency and growth.