Discovery
AuraScore 83/100

Capital Markets Risk Infrastructure Discovery Specification

Pre-trade risk and surveillance discovery specification for capital markets technology evaluation.

Use this template prior to technical discovery sessions with broker-dealers, proprietary trading desks, or asset managers. It produces an advanced discovery specification focusing on latency boundaries, regulatory reporting, and cross-asset risk controls.

Template

Role: Senior Capital Markets Sales Director & Quantitative Solutions Strategist with deep background in electronic trading infrastructure.

Context

  • Target Entity: {{trading_firm_name}}
  • Traded Asset Classes: {{asset_classes}}
  • Execution Latency Benchmark: {{latency_tolerance}}
  • Current Surveillance Platform: {{current_surveillance_vendor}}
  • Mandatory Reporting Regimes: {{regulatory_reporting_mandates}}
  • Evaluation Committee: {{decision_maker_matrix}}

Task

Draft an institutional Discovery Specification for {{trading_firm_name}} that audits pre-trade risk controls, market surveillance vulnerabilities, and regulatory reporting bottlenecks across {{asset_classes}}, framing our platform's ultra-low-latency processing capabilities.

Method

  1. Deconstruct the current trade lifecycle across {{asset_classes}} to isolate order-flow checkpoints and pre-trade margin checks.
  2. Evaluate how {{latency_tolerance}} requirements constrain risk checks across direct market access (DMA) and algorithmic routes.
  3. Identify gaps in {{current_surveillance_vendor}} regarding cross-market spoofing, layering, and wash trade detection.
  4. Audit audit-trail capture mechanisms against deadlines specified in {{regulatory_reporting_mandates}}.
  5. Formulate discovery inquiries targeting trading desk heads, chief risk officers, and low-latency infrastructure engineers.
  6. Define infrastructure validation requirements including FPGA acceleration, kernel bypass, or tick-to-trade measurement.
  7. Construct an account evaluation scorecard that establishes commercial scope and implementation feasibility.

Constraints

  • MUST evaluate specific pre-trade risk thresholds without introducing latency overhead exceeding {{latency_tolerance}}.
  • MUST NOT treat multi-asset workflows as homogenous; distinct logic must be applied for each class in {{asset_classes}}.
  • Probe questions must address both real-time intraday margin calculations and T+1 regulatory reconciliation.
  • Red flag criteria must explicitly address regulatory enforcement risks under {{regulatory_reporting_mandates}}.

Output format

  1. Market Context & Trading Architecture Overview (max 200 words)
  2. Asset-Class Specific Risk Parameter Matrix (table mapping {{asset_classes}} to risk checks)
  3. Multi-Stakeholder Discovery Battery (3 stakeholder personas with 4 technical questions each)
  4. Regulatory Compliance & Latency Trade-Off Audit (structured list)
  5. Technical Qualification & POC Gate Protocol (5 mandatory technical milestones)

Self-review

  • Does the discovery plan clearly differentiate requirements across all traded {{asset_classes}}?
  • Are the technical questions rigorous enough to challenge assumptions regarding {{latency_tolerance}}?
  • Does the document directly address reporting obligations under {{regulatory_reporting_mandates}}?
AuraScore breakdown
83/100Provisional
Instruction clarity15/15 · Strong

Explicit role, a named task, and discrete steps the model can follow.

Context architecture12/12 · Strong

Background, inputs and variables the model needs before it starts.

Constraint engineering12/12 · Strong

Hard boundaries — what the model must and must not do.

Output specification6/14 · Thin

A named, field-level shape for the response.

Reasoning structure10/10 · Strong

Ordered work items that force analysis before an answer.

Model compatibility10/10 · Strong

Length and structure that travel across frontier models.

Token efficiency5/10 · Thin

Signal density — instruction weight without padding.

Reusability7/7 · Strong

Documented variables so the scaffold adapts to new inputs.

Robustness5/5 · Strong

Quality bar, assumptions and behaviour when inputs are thin.

Observed performance1/5 · Thin

How much real usage the template has behind it.

sales
sales-discovery
financial-services
capital-markets
risk-management
fintech-sales