Data and AI built for regulated financial environments.
Production AI and data platforms for banks, credit unions, asset managers, and fintechs. We design and build fraud detection systems, risk analytics pipelines, and regulatory reporting automation that meet model risk governance requirements and examiner expectations.
Financial institutions generate enormous volumes of transaction, customer, and market data but rarely have the infrastructure to act on it in real time. The gap between an interesting data science project and a production system that survives a model risk management review is where most AI initiatives stall.
Yearling Solutions brings engineers who understand both the technical depth required for production ML and the regulatory context that governs AI in financial services. We work with your data, risk, and technology teams to build systems that perform, scale, and hold up to internal audit and examiner scrutiny.
Standards & Regulatory Context
The compliance landscape that shapes AI and data programs in financial services.
SR 11-7 Model Risk Management
Model development, validation, and governance documentation aligned to Federal Reserve and OCC expectations for model risk management.
FFIEC IT Handbook
Data management and analytics controls aligned to FFIEC information security and technology management expectations.
GLBA Safeguards Rule
Data classification, access controls, and governance for customer financial data used in AI and analytics systems.
BSA/AML & FinCEN Requirements
Transaction monitoring model design, suspicious activity detection, and SAR generation automation aligned to BSA obligations.
Fair Lending & ECOA
Bias testing, disparate impact analysis, and adverse action explainability for credit and lending models.
SEC & FINRA Data Rules
Trade surveillance, recordkeeping, and regulatory reporting data obligations for broker-dealers and investment advisers.
What We're Seeing
The data and AI challenges driving conversations with financial services technology leaders today.
Models that fail model risk management review
Many financial institutions build technically sound models that are then blocked by model risk because documentation, validation processes, and governance were not considered from the start. SR 11-7 compliance requires engineering discipline, not just data science.
Transaction monitoring with unacceptable false positive rates
Legacy rules-based AML systems generate alert volumes that overwhelm compliance teams. ML-augmented transaction monitoring reduces false positives while maintaining regulatory defensibility.
Regulatory reporting built on fragile manual processes
DFAST, call reports, and CRA reporting often depend on spreadsheets and manual reconciliation. A well-architected data warehouse with automated extraction cuts reporting cycle time and audit risk simultaneously.
Bias exposure in credit and lending models
Fair lending scrutiny extends to algorithmic decision-making. Institutions deploying ML for underwriting or pricing need documented bias testing and adverse action explainability before going live.
How We Help
Production AI and data engineering for financial services organizations, built for performance and regulatory defensibility.
Fraud Detection & Transaction Analytics
- Real-time fraud scoring models integrated with transaction systems
- AML and transaction monitoring ML augmentation for alert prioritization
- Graph analytics for money movement pattern detection
- Model performance monitoring and automated retraining pipelines
Risk Analytics & Model Governance
- Credit risk and portfolio analytics platform design
- SR 11-7-aligned model documentation, validation, and governance
- Fair lending bias testing and adverse action explainability
- Stress testing data infrastructure and scenario modeling pipelines
Regulatory Reporting Automation
- Call report, DFAST, and CRA data pipeline engineering
- Regulatory data warehouse design with audit-ready lineage
- Automated reconciliation and exception reporting
- Data quality frameworks aligned to examiner documentation expectations
Customer Analytics & Personalization
- Customer lifetime value and churn prediction models
- Next-best-action recommendation systems for retail banking
- Deposit and loan propensity scoring with fair lending controls
- Self-service analytics platforms for business banking teams
Perfect For
Financial services organizations building production AI and data capabilities under regulatory oversight.
Community and regional banks building a data warehouse to consolidate core banking, CRM, and call report data
Credit unions replacing spreadsheet-based reporting with automated regulatory data pipelines
Fintechs deploying ML-based underwriting that needs SR 11-7 governance documentation
Broker-dealers building trade surveillance and recordkeeping data platforms
Banks upgrading rules-based transaction monitoring with ML-augmented alert prioritization
Asset managers building portfolio analytics and risk reporting infrastructure
Proof in Financial Services
Real engagements with measurable outcomes.
Regional bank reduces compliance documentation time by 50% with YearlingIQ
Multi-branch institution automated evidence collection across overlapping regulatory frameworks, cutting examination prep from months to weeks. The same data governance discipline we apply to model risk and regulatory reporting pipelines.
Read case studyAI & Data EngineeringLLM benchmarking framework accelerates enterprise AI adoption
Structured model evaluation framework that removes guesswork from AI selection. We apply the same evaluation rigor to financial services models where regulatory defensibility is as important as accuracy.
Read case studyData EngineeringEnterprise data retrieval system improves analyst productivity
Semantic search over unstructured enterprise data. The same retrieval architecture powers document intelligence for loan origination, contract review, and regulatory research in financial institutions.
Read case studyPair Data with Compliance
YearlingIQ for Financial Services
Automate evidence collection for FFIEC, GLBA, PCI DSS, and SOX alongside your AI and data governance program.
Ready to build production AI for your financial institution?
Talk with engineers who understand model risk governance, regulatory data requirements, and the standards that govern AI in financial services.