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Anshuman Panwar
VP & Director, AI,
TD Asset Management

ABOUT THE SPEAKER:

Anshuman Panwar is an AI leader in financial services, focused on deploying production-grade machine learning and Gen-AI in regulated environments. He leads end-to-end delivery of ML systems—from signal research and model development to governance, monitoring, and integration into business workflows—across domains including sales prospecting and investment decisioning. His work emphasizes measurable impact, auditability, and scalable operating models for enterprise AI.

TALK TITLE:

Pre-RFP Pension Fund Prospect Ranking: Proxy Targets on Noisy Mandate Data, LLM-Assisted Research, and Human-in-the-Loop Coverage

TRACK:

Technical / Engineering Talks

SUB TOPIC:

Data Engineering / Rag Pipelines – Search / Recommendation Systems

ABSTRACT:

Modern institutional sales is low-frequency and high-stakes: a small coverage team needs early, defensible signals on which allocators (pensions/endowments/insurers) are likely to be “in market” for a new mandate before an RFP appears. This session walks through a production-ready prospect ranking system that handles missing/noisy CRM data, learns intent using proxy targets under delayed ground truth, and augments structured models with controlled LLM-assisted research from unstructured sources (e.g., mandate news, personnel changes, policy updates). I’ll cover evaluation choices for top-K ranking (precision@K, stability, and leakage traps), operational handoff to human coverage, and the feedback/monitoring loop that keeps recommendations actionable over time.

WHAT YOU’LL LEARN:

  • Use proxy labels to model “near-term intent” when outcomes are sparse/delayed, and design explicit leakage checks.
  • Convert unstructured notes into numeric, model-ready features (LLM-assisted) with document-to-feature traceability and QA for uneven coverage.
  • Evaluate ranking the way teams operate: precision@K, stability, lead-time gained, plus an execution metric like first-touch reply rate.
  • Ship with a human-in-the-loop handoff, feedback capture, and basic drift monitoring so the system stays usable over time.

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2023 Technical Background

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14%
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Intermediate
28%
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7%

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