A novel, game-based cognitive assessment built to guide hiring and improve investment performance — taken from inception to deployment.
CogCards is an online, game-based cognitive assessment that measures risk appetite to guide hiring decisions and improve investment team performance. Instead of a personality test, candidates play a card-based gambling game — their decisions become the data.
Stakeholders agreed that current risk-taking among the investment team was suboptimal — but had no reliable way to assess risk appetite in current or prospective hires. Better risk-taking, the thesis went, meant better investment skill.
Build a tool to assess risk appetite for current and prospective employees that's easy to deploy at scale, and engaging enough that people actually want to take it.
Can we identify behavioral patterns that impact investment performance — and what methodology, validated rigorously, can deliver that quickly with limited engineering resources?
Competitive analysis found no real precedent — just personality tests and talk-throughs. Behavioral science pointed toward a behavioral task plus a brief self-report survey.
Built and piloted a game prototype and results dashboard, cycling through stakeholder feedback and analysis between rounds.
Shipped the final platform (JavaScript + Claude Code) with a Python results dashboard, live with candidates and current analysts.
Validated the measurement framework internally and externally, then mined behavioral insights from the results.
A card-based gambling game where players try to win as much money as possible — decision-making reveals risk-taking directly.
Closed- and open-ended questions to triangulate against behavioral data and surface qualitative insight.
A polished game platform plus a dashboard surfacing player results for hiring teams.
Validating the framework internally (behavioral × self-report) and externally (against portfolio metrics and personality tests) surfaced a consistent pattern: the team's worst performers overestimated their own performance, while the best performers underestimated theirs.
The current investment team was missing a key range of risk appetite scores entirely.
An unexpectedly high rate of cognitive biases, including gambler's fallacy.
Clear evidence of a Dunning-Kruger effect — overconfidence concentrated among weaker performers.
Open-ended responses (analyzed with Claude) surfaced recurring player archetypes.
Behavioral + self-report measures, externally validated against portfolio metrics, gave a framework that could survive scrutiny — and leaning on LLMs for prototyping and qualitative analysis let a single owner ship something this rigorous in five months.
Some findings have been generalized or omitted, and all visuals are mockups, to preserve confidentiality. They remain faithful to the original results and content.