Case Study / Capital Group / 2019

Capital Group Internal Tools & Early ML.

Designing internal financial-services workflows, cross-site operational visibility, and an early privacy-aware machine learning pilot for document processing.

Industry

Financial Services
Internal Tools

Role

Sr. UX/UI Designer
Product & Visual Design

Year

2019

Scope

Product UX/UI
Operations & Brand

ACE critical alert component showing options and resolution paths for a retirement-plan discrepancy — Critical alert and resolution component for ACE, Capital Group's Application of Record.

Capital Group is one of the world's oldest and largest investment management organizations, with more than $2.6 trillion in assets under management. I joined a UX team focused on modernizing internal applications used by account representatives and processing teams.

The work supported a migration from a 20+ year legacy Application of Record to a newer platform. The design challenge was not simply making screens cleaner; it was preserving the flexibility and operational nuance of workflows people depended on every day.

In practice, account-opening work had to accommodate imperfect source material: incomplete forms, handwritten notes, margin comments, and documents that could arrive closer to a napkin sketch than a pristine digital input. The product experience had to support that reality without exposing more sensitive account information than necessary.

Methods & Technologies

Workflow Mapping
Requirements Translation
Wireframing
Prototyping
Operational Dashboards
UX/UI & Visual Design
Internal Brand Exploration
Appian Framework

01 · Processing

Processing tools for messy inputs.

The legacy system was deeply embedded in how teams processed financial paperwork. Highly trained specialists spent significant time interpreting intent, resolving ambiguous fields, and deciding when a document was ready to move forward.

My work focused on execution: translating requirements and workflow constraints into screens, prototypes, and interaction patterns that could support a more modern internal toolset without forcing the business into a rigid intake model.

For retirement plan processing, the replacement experience had to work within the Appian framework while improving source-document visibility, markup, notes, and contextual error messaging. These tools supported the non-ML processing path: reviewers could set up account data, annotate digitized source documents, and escalate work when specialist judgment was needed.

Design needs

Reduce rework and re-touch count
Protect PII and sensitive account information
Support specialists and lower-risk review roles
Shorten processing time against SLAs
Escalate to financial specialists when needed

Capital Group discovery and workflow board — Workflow mapping and early tool concepts for internal processing teams.

Retirement plan setup in Appian — New plan setup in the Appian framework.

Digitized document markup — Markup and review tools for non-ML processing workflows.

02 · Pilot

Early ML-assisted field review.

The pilot explored a privacy-aware, human-in-the-loop review pattern for digitized documents. Instead of routing an entire record to one person, the workflow could isolate a field-level fragment that did not expose unnecessary PII and route it for lightweight validation.

In 2019, this was early machine learning work: the system could make a best guess, ask a human to confirm or correct it, and use that feedback to improve confidence over time. For ambiguous cases, the workflow still needed a path back to a trained financial specialist.

Design principle 01

Validate fragments, not full records.

A lower-risk reviewer could answer a narrow question without seeing the broader customer account context.

Design principle 02

Let human answers improve machine confidence.

Human validation powered future ML guesses while preserving escalation paths for uncertain or sensitive cases.

Capital Group ML-assisted document review workflow — ML-assisted document review workflow exploration.

Field-level validation concept for ML-assisted document review.

Human-in-the-loop correction workflow exploration.

03 · Operations Visibility

A room-scale view of processing health.

OXSA/SASI was an internal operational dashboard used by processing teams to monitor the latency and status of form scanning, capture, processing, and upload activity across multiple U.S. sites.

The dashboard supported the same paper-heavy reality as the account-processing work. Before a form could reach ACE or an ML-assisted review path, teams needed to know where it was in the pipeline, whether a processing site was keeping pace, and where attention was required.

I created the logo, information architecture, UX/UI, and visual design. The interface was designed for an approximately five-foot-wide monitor, so status needed to be legible from across the room. A dark visual system reduced glare while clear hierarchy and strong contrast made cross-site conditions easy to scan at a distance.

Designed to show

Scanning and capture status
Processing latency
Upload progress
Cross-site health
Exceptions requiring attention

OXSA/SASI dashboard showing cross-site scan latency, capture, upload, and processing status.

04 · Internal Team Brand

A visual identity designed to belong inside the ecosystem.

Alongside the product work, I explored a visual identity for AFG, an internal team supporting Capital Group's operational tools. The concepts use the established ACE color palette and geometric language, creating a recognizable team identity without making it feel like a customer-facing brand.

Design direction