{"id":235,"date":"2025-07-08T23:47:57","date_gmt":"2025-07-09T02:47:57","guid":{"rendered":"https:\/\/brunopavani.com\/?page_id=235"},"modified":"2026-04-27T07:14:17","modified_gmt":"2026-04-27T10:14:17","slug":"customer-due-diligence-cdd-system-evoke-2025","status":"publish","type":"page","link":"https:\/\/brunopavani.com\/?page_id=235","title":{"rendered":"Customer Due Diligence (CDD) System \u2013 Evoke"},"content":{"rendered":"\n

Redesigning an internal compliance workflow to improve throughput, decision quality, governance, and time-to-market<\/p>\n\n\n\n

Project Snapshot<\/h3>\n\n\n\n

Confidentiality:<\/strong> Some screens\/data anonymized.<\/p>\n\n\n\n

Company \/ Product:<\/strong> Evoke \u2014 Customer Due Diligence (CDD), internal compliance workflow<\/p>\n\n\n\n

Timeline:<\/strong> 2025 (6 months, end-to-end delivery)<\/p>\n\n\n\n

Role:<\/strong> Lead UX\/UI Designer (end-to-end, partnering with Engineering)<\/p>\n\n\n\n

Stakeholders:<\/strong> Compliance Operations + Business stakeholders + Engineering <\/p>\n\n\n\n

Platform \/ Tools:<\/strong> OutSystems, Figma, Miro <\/p>\n\n\n\n

Scope (What I designed):<\/strong> task-based workflow, explicit states & ownership, decision context, policy guardrails\/validations, audit history, reusable UI framework <\/p>\n\n\n\n

Outcomes:<\/strong> ~30% reduction in handling time, fewer rework\/escalations, improved accountability, faster audit reviews; patterns adopted as baseline for adjacent processes (KYC\/AML)<\/p>\n\n\n\n

Confidentiality:<\/strong> Some screens\/data anonymized.<\/p>\n\n\n\n

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Summary<\/h3>\n\n\n\n

The Customer Due Diligence (CDD) system is an internal compliance product used to verify customer data, assess risk, and document decisions under strict regulatory requirements.<\/p>\n\n\n\n

Before this project, the workflow was highly manual. Agents had to access multiple systems, copy and paste data between them, and manually search for information across external sources to validate customer details. This process was time-consuming, error-prone, and heavily dependent on individual execution.<\/p>\n\n\n\n

I led an end-to-end redesign that transformed this workflow from manual data gathering into a structured, system-supported process. By integrating automated data checks and AI-assisted validation, the experience shifted from execution-heavy tasks to decision-focused workflows.<\/p>\n\n\n\n

Users no longer needed to collect and reconcile data manually. Instead, they could review pre-validated information, focus on edge cases, and make faster, more consistent decisions with full traceability.<\/p>\n\n\n\n

Context<\/h3>\n\n\n\n

Agents were required to navigate across multiple systems and external sources, manually collecting and validating information before making a decision.<\/p>\n\n\n\n

This created a workflow with high cognitive load, excessive context switching, and a strong dependency on manual effort to ensure data accuracy and compliance.<\/p>\n\n\n\n

My Role<\/h3>\n\n\n\n

I led UX and UI design end-to-end, working closely with Compliance Operations and Engineering.<\/p>\n\n\n\n

A key part of my role was understanding how agents actually performed their work \u2014 how they navigated between systems, what information they needed to validate, and where errors and inefficiencies occurred.<\/p>\n\n\n\n

From this, I redesigned the workflow to reduce manual effort, centralize decision context, and introduce automation where possible, while ensuring compliance requirements were fully respected.<\/p>\n\n\n\n

The problem<\/h3>\n\n\n\n

The main challenge was not lack of data, but how the work was performed.<\/p>\n\n\n\n

Agents were responsible for collecting, validating, and reconciling data manually across multiple sources, which led to high effort, inconsistent outcomes, and frequent rework.<\/p>\n\n\n\n

Goals<\/h3>\n\n\n\n
    \n
  • Reduce average handling time per case without compromising compliance quality<\/li>\n\n\n\n
  • Make ownership, status, and next actions explicit across the case lifecycle<\/li>\n\n\n\n
  • Standardize inputs and validations aligned to policy rules<\/li>\n\n\n\n
  • Integrate auditability (who did what, when, and why) into the core workflow<\/li>\n\n\n\n
  • Establish a reusable UI framework to extend beyond CDD<\/li>\n\n\n\n
  • Shorten the cycle from intake \u2192 decision \u2192 completion (time-to-market)<\/li>\n<\/ul>\n\n\n\n

    Constraints and approach<\/h3>\n\n\n\n

    Instead of focusing only on interface improvements, I focused on redesigning how the work itself was performed.<\/p>\n\n\n\n

    The goal was to reduce manual data handling and shift the user\u2019s role from data gathering to decision-making.<\/p>\n\n\n\n

    This involved structuring the workflow around tasks, centralizing relevant information, and introducing automated checks and AI-assisted validation to reduce repetitive work.<\/p>\n\n\n\n

    The interface was then designed to support this new model, making decisions faster, clearer, and more consistent.<\/p>\n\n\n\n

    Key improvements (iteration highlights)<\/h3>\n\n\n\n

    A key shift in the product was moving from manual data handling to system-assisted validation.<\/p>\n\n\n\n

    1) Task-based workflow with explicit states and ownership<\/h4>\n\n\n\n

    The experience was restructured into a consistent set of tasks with clear progression:<\/p>\n\n\n\n

      \n
    • Case-level status and ownership visible throughout the workflow<\/li>\n\n\n\n
    • Task states (assigned, in progress, completed) reflected in navigation and available actions<\/li>\n\n\n\n
    • Role-based actions (claim, release, resume, complete) aligned to operational handoffs<\/li>\n\n\n\n
    • Reduced ambiguity during multi-step reviews and reassignment scenarios<\/li>\n<\/ul>\n\n\n\n

      2) Decision context designed for speed and consistency<\/h4>\n\n\n\n

      Automated checks and AI-assisted validation reduced the need for manual data verification, allowing users to focus on reviewing and confirming information instead of collecting it.<\/p>\n\n\n\n

        \n
      • Consolidated identity and risk signals within the task context<\/li>\n\n\n\n
      • Structured evidence review with consistent placement of supporting details<\/li>\n\n\n\n
      • Standardized rationale capture to improve consistency across agents and reviewers<\/li>\n\n\n\n
      • Reduced back-and-forth and unnecessary navigation across systems<\/li>\n<\/ul>\n\n\n\n

        3) Guardrails and validation aligned to policy<\/h4>\n\n\n\n

        Operational safety was handled as a product requirement:<\/p>\n\n\n\n

          \n
        • Required fields and validations derived directly from compliance rules<\/li>\n\n\n\n
        • State-aware controls to prevent invalid transitions and incomplete submissions<\/li>\n\n\n\n
        • Clear error states with recovery paths to reduce rework and escalations<\/li>\n<\/ul>\n\n\n\n

          4) Standardized patterns to scale beyond CDD<\/h4>\n\n\n\n

          A reusable UI framework was established to support additional compliance processes:<\/p>\n\n\n\n

            \n
          • Consistent layout conventions (navigation, status, primary actions, history)<\/li>\n\n\n\n
          • Repeatable components for evidence, notes, checkpoints, and audit history<\/li>\n\n\n\n
          • Patterns designed to be feasible within OutSystems, supporting faster iteration cycles<\/li>\n<\/ul>\n\n\n\n

            Approved layout<\/h3>\n\n\n\n

            Final workflow layout and screens designed by me, reviewed and approved with Compliance Operations and Business stakeholders.<\/p>\n\n\n\n