AI Risk Analytics Platform
Morgan Stanley
40%
Faster time-to-insight for risk assessments
90%
Accuracy in automated risk classifications
500+
Analysts adopted the platform in first 6 months
$50M+
Previously undetected risk exposures identified
Overview
At Morgan Stanley, one of the world's leading global investment banks with over $6 trillion in client assets, I own the roadmap for an AI-driven B2B risk analytics platform. The platform uses generative AI and predictive models to surface portfolio risk, limit breaches, and regulatory narratives to both internal and external stakeholders in a highly regulated financial environment.
The financial services industry is undergoing a fundamental transformation in how it approaches risk management. Traditional methods rely on analysts manually gathering data across siloed systems, producing reports that can take days to compile. Our platform replaces this with AI-powered real-time risk intelligence, enabling faster and more accurate decision-making.
This work sits at the intersection of cutting-edge AI capabilities and stringent regulatory requirements. Every model, API, and data flow must satisfy firm policy, InfoSec standards, and regulatory expectations around explainability, auditability, and data governance.
The Problem
Risk analysts in investment banking spend a disproportionate amount of their time on manual data gathering and report generation rather than actual risk analysis. Existing tools are siloed, requiring analysts to switch between multiple systems to complete a single risk assessment. This leads to inconsistent analysis, delayed decision-making, and missed risk signals. In an industry where a single undetected risk exposure can result in significant financial losses, the cost of slow and fragmented risk analysis is substantial.
My Role
Growth Product Manager
I serve as the product owner for AI-centric epics, writing user stories and acceptance criteria that specify latency, explainability, observability, and control requirements for each model and API going to production. I partner with data scientists, ML engineers, and architects to design end-to-end pipelines for risk models, and I define service boundaries and REST API contracts for model serving and analytics, working closely with architecture, InfoSec, and compliance teams.
The Approach
I led the integration of LLM-based agents that summarize complex risk exposures, generate client-ready commentary, and answer scenario questions using retrieval augmented generation (RAG) on approved data sources with strict access controls and observability. This required designing data strategies for capturing feedback signals such as overrides, exception handling, and production drift to calibrate thresholds and improve model quality.
The platform architecture follows a service-oriented design with clearly defined API contracts. Each model goes through a rigorous lifecycle: data sourcing, feature engineering, training, evaluation, deployment, and continuous retraining with monitoring, alerts, and drift detection. I partnered closely with data scientists and ML engineers to ensure models meet both performance targets and regulatory requirements.
Adoption was driven through collaboration with relationship managers and solution engineers who created Python notebooks, SQL examples, and reference integrations showing how client systems call risk APIs and embed outputs into their own workflows and dashboards.
Key Features
What we built
AI-Powered Risk Summarization
LLM-based agents that summarize complex portfolio risk exposures into clear, actionable narratives for analysts and clients.
Retrieval Augmented Generation (RAG)
Conversational AI that answers scenario questions by retrieving context from approved internal data sources and regulatory documents with strict access controls.
Predictive Risk Scoring
Machine learning models that predict risk concentrations, limit breaches, and emerging portfolio vulnerabilities before they materialize.
REST API Risk Services
Well-defined API contracts for model serving that allow internal and external systems to consume risk analytics programmatically.
Continuous Model Monitoring
Production monitoring with drift detection, alerting, and automated retraining pipelines to maintain model accuracy over time.
Client Integration Toolkit
Python notebooks, SQL examples, and reference integrations that enable client systems to embed risk outputs into their own workflows.
Tech Stack
Key Lessons
What I took away from this project
Enterprise AI in finance requires rigorous data governance from day one, not as an afterthought
Human-in-the-loop is essential for financial decisions — full automation is neither desired nor appropriate
LLM hallucinations require carefully designed guardrails, especially in regulated environments
Starting with high-value, low-risk use cases builds organizational trust in AI capabilities
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