Credit Risk Associate - Tech

JPMorgan Chase JPMorgan Chase · Banking · Bengaluru, Karnataka, India · Commercial & Investment Bank

This role focuses on applying AI/ML techniques, including LLMs, for credit risk analysis and reporting within a financial services context. The responsibilities include performing financial analysis, building dashboards, improving data quality, and using AI/ML for anomaly detection and trend summarization, while adhering to governance and regulatory expectations.

What you'd actually do

  1. Perform analysis of financial metrics/risk outcomes and collateral/borrowing base indicators (revenue/EBITDA/cash flow, leverage, coverage, liquidity, rating migration; availability/utilization, advance rates, collateral mix, eligibility/ineligibles, reserves, concentrations, dilution, aging/past dues, over-advances, variance drivers)
  2. Build and maintain executive-ready dashboards and monitoring packs that are consistent, auditable, and aligned to governance and regulatory expectations
  3. Deliver ad-hoc analyses for senior stakeholders, turning ambiguous requests into decision-ready insights and concise narratives
  4. Improve data quality, controls, and scalable automation across financial, collateral, and borrowing base reporting
  5. Use LLMs responsibly through strong prompting to draft executive narratives and summarize financial and collateral trends, adhering to governance and data-handling requirements
  6. Apply AI/ML (for example, anomaly detection and early-warning or predictive indicators) to financial and collateral variance detection and integrate outputs into risk monitoring and reporting

Skills

Required

  • SQL
  • Tableau
  • Alteryx
  • Python
  • R
  • analytics
  • business intelligence
  • portfolio/credit analytics
  • financial services

Nice to have

  • Data Science
  • Computer Science
  • Business
  • Economics

What the JD emphasized

  • adhering to governance and data-handling requirements
  • aligned to governance and regulatory expectations

Other signals

  • Apply AI/ML (for example, anomaly detection and early-warning or predictive indicators) to financial and collateral variance detection and integrate outputs into risk monitoring and reporting
  • Use LLMs responsibly through strong prompting to draft executive narratives and summarize financial and collateral trends, adhering to governance and data-handling requirements