Overview
What is regulatory reporting automation?
Regulatory reporting across regimes like CCAR, FRTB, MiFID, and SEC requires aggregating data from many systems, applying complex rules, and producing precise, auditable submissions, a process that is manual, error-prone, and deadline-driven.
Automation uses AI and rules engines to gather and reconcile the data, validate it against requirements, flag exceptions, and generate submissions with full lineage, turning a recurring fire drill into a controlled, traceable process.
What the system analyzes
The Challenge
Reporting is a manual, high-stakes fire drill
Each reporting cycle means reconciling data across systems under deadline, where a single aggregation error can mean regulatory scrutiny and penalties.
Manual Aggregation
Pulling and reconciling data across systems is slow and error-prone.
Complex, Changing Rules
Requirements span regimes and change frequently.
Error & Penalty Risk
Aggregation errors invite scrutiny and penalties.
Deadline Pressure
Recurring deadlines strain teams and crowd out analysis.
Education AI Solutions
How Solnix automates regulatory reporting
Data Aggregation
Gather and reconcile data across source systems.
Rule Validation
Validate data against CCAR, FRTB, MiFID, and SEC rules.
Exception Handling
Flag and route exceptions for review.
Submission Generation
Produce accurate, formatted submissions.
Lineage & Audit
Maintain end-to-end data lineage.
Cycle Analytics
Track and shorten reporting cycle time.
End-to-End Implementation
How Solnix implements reporting automation
A phased rollout that automates one reporting regime end-to-end, proves accuracy and cycle-time gains, then extends across CCAR, FRTB, MiFID, and SEC.
Discovery & AI Opportunity Mapping
We start by understanding your operations, data landscape, and goals, then map where AI delivers measurable value and where it does not. Every engagement begins with a prioritized opportunity backlog, not a technology pitch.
Data Foundation & Readiness
AI is only as good as the data behind it. We assess data quality, connect fragmented sources, and build the secure, governed pipelines that production AI depends on, with privacy and compliance designed in from the start.
Model & Agent Development
We build the models, retrieval systems, and AI agents tailored to your use cases, selecting the right approach (fine-tuning, RAG, multi-agent orchestration) for accuracy, cost, and latency, and validating against your real-world edge cases.
Integration & Workflow Embedding
AI only creates value when it lives inside the tools your teams already use. We embed models and agents into existing systems, surfaces, and workflows, so adoption is natural and human-in-the-loop controls stay in place.
Deployment, Security & Compliance
We deploy to production with the security, monitoring, and compliance controls enterprises require, including bias and fairness testing, audit logging, and the observability needed to operate AI responsibly at scale.
Optimization & Continuous Improvement
AI systems improve with use. We measure outcomes against the goals set in Phase 01, retrain and tune from live feedback, and expand to the next set of use cases, turning a single deployment into a compounding capability.
Key Capabilities
Key capabilities
Automated Aggregation
Reconcile data across systems.
Benefits
Rule Engine
Validate against regimes.
Benefits
Exception Routing
Surface issues early.
Benefits
Lineage & Audit
Trace every number.
Benefits
Submission Generation
Produce filings.
Benefits
Cycle Analytics
Measure and improve.
Benefits
Who Benefits
Outcomes for reporting teams, compliance, and the firm
For Reporting Teams
Less fire drill, more control.
For Compliance & Audit
Defensible, traceable reporting.
For the Firm
Lower regulatory and operational risk.
Education Segments
Explore related capital-markets AI
AML & KYC Automation
Connect reporting to financial-crime detection.
Risk Model Enrichment
Feed enriched risk into regulatory reports.
Portfolio Attribution AI
Reconcile P&L for reporting.
Market Sentiment Intelligence
Monitor disclosure-relevant signals.
Enterprise Automation
Automate the broader back office.
Agent Ecosystem
Orchestrate reporting workflows.
Methodology
How Solnix builds for capital markets
01, Trading & Risk Workflow Assessment
01, Trading & Risk Workflow Assessment
We map your desk, risk, and compliance workflows to target the AI interventions with the clearest measurable edge.
02, Market-Data & Model Architecture
02, Market-Data & Model Architecture
We build governed pipelines over market, reference, and alternative data with the latency and lineage trading and risk demand.
03, Model Validation & Backtesting
03, Model Validation & Backtesting
Every model is backtested and independently validated against historical regimes before it informs a decision.
04, Compliance & Audit by Design
04, Compliance & Audit by Design
Explainability, audit trails, and model-risk-management alignment (SR 11-7) are built in, not bolted on.
05, Continuous Monitoring
05, Continuous Monitoring
Live monitoring tracks model performance and drift across regimes, triggering revalidation as markets change.
FAQ
Questions from compliance and reporting leaders
Get Started
End the Reporting Fire Drill.
Talk to a Solnix regulatory-tech specialist about automating your highest-effort reporting regime first.
Talk to a Regulatory-Tech Specialist →