Case Studies & Technical Whitepapers
At RootStock, we believe in sharing our research and implementation insights with the broader community. This collection highlights some of our recent case studies and technical whitepapers on AI implementation in financial services.
Case Study: Implementing Explainable AI for Credit Decisions
Working with a mid-sized regional bank, we implemented an explainable AI system for credit decisioning that balanced accuracy with transparency. Key outcomes included:
- 14% improvement in credit decision accuracy
- 28% reduction in customer appeals of credit decisions
- 42% increase in customer satisfaction with decision explanations
The technical approach combined gradient-based feature attribution methods with a novel natural language explanation generator that produced contextually appropriate, personalized explanations for each decision.
Whitepaper: Architectural Patterns for Responsible AI in Finance
This technical whitepaper outlines architectural patterns for implementing responsible AI systems in financial services, with a focus on:
- Federated Learning Patterns: Maintaining data privacy while enabling model training
- Explainability Layers: Integrating explanation generation into the inference pipeline
- Fairness Monitoring: Continuous evaluation of model outputs for demographic biases
- Human-in-the-Loop Workflows: Effective designs for human oversight of AI decisions
Each pattern includes implementation considerations, code examples, and lessons learned from real-world deployments.
Case Study: AI-Assisted Financial Planning for Underserved Communities
In partnership with a nonprofit financial counseling service, we developed an AI-assisted planning tool specifically designed for underserved communities. The system addressed unique challenges including:
- Variable and unpredictable income streams
- Limited credit history
- Complex government benefit interactions
The results showed a 37% increase in savings rates among participants and a 24% reduction in financial stress as measured by standardized assessments.
Whitepaper: Benchmarking Methodologies for Financial AI Systems
This technical paper presents a comprehensive framework for benchmarking AI systems in financial contexts, covering:
- Performance metrics beyond accuracy (calibration, robustness, fairness)
- Stress testing methodologies for market volatility scenarios
- Human evaluation protocols for assessing explanation quality
- Longitudinal evaluation approaches for tracking performance drift
The framework has been adopted by several financial institutions and is being considered as a standard by industry working groups.