Breaking Down Silos: Turning Data Chaos into Clarity

Fragmented data and redundant reviews don’t just slow down processes. They also create barriers to growth and introduce unnecessary risk. 

When teams responsible for onboarding, compliance, and fraud work in isolation, the client experience suffers, and operational costs rise. This internal data chaos directly impacts your ability to compete. The solution lies in creating clarity and consistency in how information is used across the organization.

Insights from our webinar, Beyond Compliance: Transforming Client Experience Through Intelligent Data, highlight a clear path forward. Drawing on expert discussion, we will cover why fragmented workflows are so damaging and outline a repeatable framework for building a more unified, automated, and intelligent approach to risk and compliance.

Why Fragmented Workflows Slow Everything Down

For many banks and fintechs, the client onboarding and compliance process is a complex puzzle of disconnected systems and manual handoffs. Legacy technology and siloed teams often mean that crucial data isn't shared effectively. The separation between departments, such as Fraud, AML, and Compliance, creates significant inefficiencies.

The consequences of these data silos are felt throughout the organization:

• Redundant Data Collection: Teams often find themselves asking clients for the same information multiple times, leading to frustration and a disjointed experience.
• Manual Handoffs: Information is passed between departments through manual processes, which are slow and prone to human error. This can lead to missed service-level agreements (SLAs) and compliance gaps.
• Lack of a Unified View: Without a single source of truth, it’s impossible to get a complete picture of a client’s risk profile. Each team makes decisions based on incomplete information.
• Inconsistent Reviews: When analysts use different tools and datasets, their risk assessments can vary, leading to inconsistent decisioning and potential vulnerabilities.

As Kevin Shine, Head of Sales at Fraud.net, explains, the core issue is not a lack of data, but a lack of organization. "Where we see the biggest issues is data organization, data labeling, understanding the workflows, and how to streamline the usability of that information."

Establishing a Clear Framework for Transformation

Transitioning from siloed operations to a more integrated model requires a deliberate and structured approach. It's not about ripping and replacing entire systems overnight but about strategically implementing a framework for change.

Yetunde Ekunwe recommends a clear, step-by-step process for tackling these workflow challenges. The focus should be on building a solid foundation before introducing new technology. "You have to define very clear objectives that will drive the outcomes you set forth," she states.

This foundational work includes:

1. Map Current Processes: Begin by thoroughly documenting existing workflows to identify bottlenecks, redundancies, and communication gaps.
2. Define Measurable Goals: Establish clear KPIs that align with business objectives, such as reducing "time to transact," enhancing data accuracy, or consistently meeting SLAs.
3. Build Consistency: Work to establish shared standards and processes that can be applied consistently across different teams, ensuring everyone operates from the same playbook.
4. Pilot New Frameworks: Before a full-scale rollout, use a proof of concept (POC) to test new workflows and technologies in a controlled environment. This allows you to validate the approach and make adjustments before committing significant resources.

By following this process, institutions gain a clearer understanding of what their future state should look like and how to address their current gaps and redundant workflows. Ekunwe adds that this includes "understanding the right tools or potential digital tools, really - resources that you require in order to get there."

Collection, Validation, and Verification: A Repeatable Model

Once a clear strategy is in place, you can begin to implement a more automated and streamlined process. Kevin Shine presents a simple yet powerful three-step model that serves as a blueprint for improving data workflows: "Collection, validation, and verification of what you've collected."

This model offers a consistent framework for transforming manual processes into efficient, data-driven operations.

Step 1: Collection
The first step is to shift from manual document gathering to digital data ingestion. Instead of asking clients to upload documents or relying on employees to key in information, you can use APIs to pull data directly from trusted sources. This "digital data collection" minimizes errors and accelerates the entire process from the very beginning.

Step 2: Validation
After data is collected, it must be validated for accuracy. This involves moving toward a "digital validation and verification set of workflows as opposed to having a bunch of people involved in key-entering information and QAing information," explains Shine. Automation, supported by rules-based checks and machine learning, can instantly confirm that the data is legitimate and correctly formatted.

Step 3: Verification
The final step is to apply decisioning logic. This involves using predefined thresholds and risk scoring to determine the appropriate next steps. High-risk cases can be automatically flagged for manual review, while low-risk applications can proceed without intervention. This allows your expert analysts to focus their time where it is needed most.

An orchestrated platform makes this model possible. It connects to a single data feed and applies rules and AI-native models to identify anomalies. Alerts are then generated and routed to the appropriate teams (fraud, AML, compliance), ensuring everyone operates from the same verified data.

The Power of Shared Visibility and Automation

Breaking down silos isn't just about efficiency; it's about making smarter, faster, and more consistent decisions. When teams have shared visibility into client data, the entire risk management process becomes more effective. As Shine notes, "If the program that you have in place to onboard new clients is in good shape and it's as real-time as possible, the likelihood of having fraud downstream goes down dramatically."

Fragmented data and siloed teams are no longer sustainable in the competitive financial landscape. By establishing a clear transformation framework and embracing a model of digital collection, validation, and verification, your institution can turn data chaos into a source of clarity and competitive advantage.

Want to learn more about how to implement these strategies in your organization?

Watch the full webinar: Beyond Compliance: Transforming Client Experience Through Intelligent Data to get deeper insights on building a unified data strategy that drives faster, cleaner decisions.