Manager Due Diligence Record

Manager due diligence knowledge lives entirely in the heads of the fund selection team. 'Why did we hire Manager X?' depends on which analyst was on the search committee three years ago. When the CIO asks about a manager's operational risk profile or style consistency, the answer requires tracking down meeting notes that may or may not exist. Manager evaluation criteria shift with each analyst who joins the team.

None — AI cannot perform any manager assessment or monitoring because no formal due diligence records exist in any system.

Manager due diligence records exist as Word documents and PowerPoint decks stored in analyst folders. Each analyst writes up managers differently — one focuses on performance attribution, another on organizational stability, a third on risk metrics. There is no standard template. When the investment committee reviews a manager, half the meeting is spent asking 'do we have their latest ADV filing?' and 'what was our operational due diligence conclusion?'

AI could potentially read the documents, but cannot systematically compare managers because due diligence records lack consistent fields, scoring criteria, or standardized terminology across analyst write-ups.

Manager due diligence records follow a standard template with consistent sections for every evaluated manager: investment process, team biography, performance attribution, AUM history, style drift analysis, and operational due diligence findings. Analysts complete the template for each manager search and annual review. But the records are static documents in SharePoint — comparing style consistency metrics across fifteen managers requires opening each document individually.

AI can search for and retrieve manager due diligence documents by manager name or strategy, but cannot programmatically compare tracking error, information ratios, or style drift scores across the manager roster because those values are embedded in document text rather than structured fields.

Manager due diligence records are maintained in a structured research platform with discrete fields: investment process score, operational due diligence rating, annualized return (gross and net), tracking error, information ratio, style drift coefficient, AUM, capacity constraints, key person dependencies, and regulatory findings. The investment committee can query 'show me all large-cap value managers with information ratios above 0.5, tracking error below 4%, and clean operational due diligence' and get a filtered, sortable result.

AI can rank managers by quantitative and qualitative scores, flag managers exhibiting style drift beyond threshold, identify capacity-constrained strategies, and generate comparative manager scorecards. Cannot yet autonomously integrate real-time performance and holdings data because the due diligence record captures point-in-time assessments.

Manager due diligence records are schema-driven entities with explicit relationships to live performance feeds, holdings-based style analytics, regulatory filings (Form ADV, Form PF), AUM flow reports, and portfolio allocation records. Each manager record carries real-time style drift detection, peer-relative performance ranking, and organizational risk flags. An AI agent can ask 'which managers have experienced key person departures in the last 90 days, and how has their tracking error changed since the departure?' and get a precise, current answer.

AI can autonomously monitor the entire manager roster — detecting style drift, performance deterioration, organizational changes, and regulatory red flags in real-time. Manager watch-list recommendations and replacement searches can be initiated automatically when thresholds are breached.

Manager due diligence records are living entities that generate and update themselves from continuous data streams. Performance attribution recalculates as monthly returns arrive. Style analysis updates as quarterly holdings are disclosed. Organizational risk scores adjust as personnel changes, regulatory filings, and news events are ingested. The due diligence record is a real-time digital twin of each manager's investment operation.

Fully autonomous manager due diligence and monitoring. AI continuously evaluates, scores, compares, and recommends actions on every manager in the portfolio — from initial screening through ongoing monitoring — without human data gathering.

Ceiling of the CMC framework for this dimension.