Clarity Investigator Ecosystem
Clarity Investigator Ecosystem
Transforming and reimagining how we investigate financial crime at Capital One
My role
Lead product designer (research, UI/UX design, prototyping and testing, strategy, team building)
Timeline
I worked on various features for Clarity from November 2019 until May 2022
I worked with…
UI/UX designers, data designers, engineers, product partners, stakeholders
Overview
Risk management is critical to the health and continued existence of a bank. At Capital One, we have an army of experts who are trained to assess risk and make decisions based on what they find, but often their processes and workflows can be inherently complicated, time-consuming, and very manual. We found ourselves wondering: How might we use technology to help make these processes and workflows for performing financial crime investigations more predictable, repeatable, and intelligent?
140+ manual processes and reports
4 investigations per investigator per day
routinely accessing 10-15 different systems
The challenge
In 2018, the existing AML investigative platform did not facilitate a best-in-class program. There were over 140 manual processes and reports, only 4 investigations per investigator per day being completed, and investigators were routinely accessing 10-15 different systems to do a single investigation. The platform was lacking many desired capabilities and its tools felt disparate and inflexible.
Additionally, there was a desire from our FIU stakeholders to streamline the investigative process to ensure compliance - we needed to strike a balance between designing a usable tool for investigators and supervisors, while also managing the risk that comes with simplifying such a complex space.
Design process
Throughout my work on Clarity, my team and I followed a fairly iterative framework to help us organize and understand our challenges. We typically kicked off work with our cross-functional team to create a shared approach and to get early alignment with stakeholders. Then we worked to validate business needs and did the necessary research to fully understand the user needs. After research, we typically synthesized our findings into insights that helped guide prioritization and ideation. With those insights in mind, we typically generated many possible solutions to the user and business problem. This led us to design and iteration based on feedback and thoughtful critique. As a last step before circling back into the process, we would measure to see how well we met user and business needs.
I partnered closely with my tech and product partners to align on roadmap milestones and dates, often presenting research findings that would help influence our collective next steps. The Clarity team began with an MVP that was designed with one line of business in mind. From there, we layered in functionality and enhancements that would allow us to launch for more users over several years. The roadmap was such that we would continue to build and iterate, hitting milestones along the way. My design team was set up for dual-track agile - we were working both in the present development cycle and also thinking ahead to the next wave of functionality.
One last important piece of our design process was documentation. To ensure repeatability and consistency, we created a design library with reusable, interactive, components in Figma. Additionally, we created a shareable prototype for our stakeholders.
Solution
Clarity is an investigator platform that consolidates all data and tools necessary to resolve an anti-money laundering alert in a timely, accurate, efficient, and fully-documented manner. Beginning on the dashboard, investigators are able to open up an alert, comb through KYC information for one or more customers, customize transactional data for analysis, and eventually make a decision for filing.
Since its launch, Clarity has revolutionized the way that investigators and supervisors complete their day to day tasks. Before Clarity, investigators were individually accessing numerous internal and external systems, copying and pasting information, uploading screen grabs, and storing data in folders on their computers. Now, Clarity sources data directly from APIs, Snowflake, and One Lake; bots retrieve information from other systems and Clarity exports evidence packages. Several key processes and reports have been automated, more investigations are being completed per investigator per day on average, and far less external systems routinely being accessed.
In 2022, Clarity was a 2022 TechX Divisional Level finalist.
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