Connected Execution
Operates inside your systems — loan origination, claims management, servicing. End-to-end execution. No rip-and-replace.
Industries
AI Agents for Financial Services
The workflows that resisted automation for decades now run in production: 99%+ accuracy in the flagship lending deployment, 70%+ less processing time, and productivity gains a CFO can audit.
Why MightyBot
The hardest workflows in financial services have resisted automation for decades. The technology existed; the architecture for regulated production did not. MightyBot finishes them end-to-end. Built processes some of the largest construction lending volumes in the market. 99%+ extraction accuracy in a production lending deployment, in production with 15+ banks and lenders. Audit trails ready for current AML/CFT and AI disclosure requirements. The adoption path from pilot to production is covered in our financial services guide.
The Workflows No One Else Can Solve
Commercial Loan Underwriting
Construction Draw Reviews
Insurance Claims Adjudication
Compliance Monitoring
Large document packages. Financial spreading, credit analysis, and policy evaluation executed in parallel.
Inspection photos cross-referenced against budgets, lien waivers, and invoices across different formats. Broke automation for years. Runs in production today.
State-specific rules across jurisdictions. Medical necessity review. Every decision backed by evidence pointers.
State-specific rules across jurisdictions. Medical necessity review. Every decision backed by evidence pointers.
Regulatory readiness
The compliance calendar for AI in financial services
| Rule | Effective date | What it requires | What evidence looks like |
|---|---|---|---|
| FinCEN investment adviser AML/CFT rule | January 1, 2028 (delayed from January 1, 2026) | Certain investment advisers establish an AML/CFT program and file suspicious activity reports with FinCEN. | A versioned AML/CFT policy, risk evaluation, reporting decision, and source-linked audit trail. |
| Colorado SB 26-189 (replaced SB 24-205) | January 1, 2027 (developer documentation) | Developers document automated decision-making technology used in consequential decisions, including lending. Deployers give consumers notice, a plain-language explanation after an adverse outcome, data correction and human review. | Risk policy, impact assessment, annual review, consumer notice, correction, and appeal records. |
How MightyBot
Executes
Policies Compile to Execution Plans
Business rules in plain English compile into parallel execution plans. Faster execution. Zero variance.
Edge Cases Handled
Not ignored. Not escalated to a dead queue. Evaluated against your rules, routed with full context. Every decision has a why-trail.
Same-Day Policy Deployment
Regulations change. Criteria shift. Rule changes deploy immediately.
Execution is governed by the policy engine and recorded through compliance controls.
Production Results
Metric
Construction lending deployment
Institutions served
Processing time reduction in MightyBot production deployments
Manual interactions eliminated
Extraction accuracy in production lending
Throughput per team member
Result
Production
Many
70%+
Fewer
99%+
Higher
Built Technologies Draw Agent: 95% time reduction in production.
Industries We Serve
Commercial Lending
Full lending lifecycle: underwriting, draw reviews, servicing. Production scale through Built Technologies.
Insurance
Claims adjudication, medical necessity, coverage verification. State-by-state compliance with evidence pointers.
Payment & Processing
Merchant statement analysis across hundreds of processor formats. Hours to seconds through RocketFee.
Real Estate & Construction Finance
Construction draw reviews — photo evidence, inspection verification, full document processing. The proof case.
Use Cases
Credit & Risk Evaluation
Parallel analysis. Not sequential
Loan Underwriting Automation
Credit analysis, document review, decision packaging
Construction Draw Review Automation
The proof case. Construction draws in production
Compliance Monitoring & Reporting
Continuous policy evaluation. Audit-ready
Intelligent Document Processing
Classification, extraction, validation. Any format
Industry workflow map
How financial services automation works in MightyBot
MightyBot helps financial services teams automate regulated workflows such as underwriting, credit-risk evaluation, document processing, and compliance monitoring while preserving policy citations, source evidence, and audit trails.
Last updated: October 4, 2026| Inputs | Loan files, statements, policies, bureau data, financial spreads, compliance rules, borrower documents, and transaction records. |
|---|---|
| Execution | Agents classify documents, extract evidence, apply policies, calculate risk signals, route exceptions, and assemble decision-ready packages. |
| Outputs | Credit memos, exception reports, compliance findings, document indexes, decision rationale, and reviewer-ready summaries. |
| Audit trail | Every output links back to the source document, extracted data, policy rule, calculation, reviewer action, and model decision path. |
| Best for | Financial institutions that need faster regulated execution without losing explainability, governance, or human oversight. |
See it in production
Real workflows, real transactions, institutional scale. Not a demo. In production.
FAQ
Frequently Asked Questions
What can AI agents do in financial services?
AI agents can classify, extract, and validate financial documents, apply institution-specific policies, monitor compliance, and route uncertain cases to reviewers. MightyBot executes inside existing loan origination, servicing, and claims systems, with every result backed by evidence chains.
How do AI agents stay compliant in regulated financial workflows?
Every decision includes a why-trail with the full evidence chain. Regulatory requirements are enforced as first-class rules. SOC 2 Type II certified. Human-in-the-loop controls for high-risk decisions.
What accuracy do AI agents achieve in financial services?
Specialized agents handle classification, extraction, and validation independently within policy boundaries. Confidence thresholds route uncertain cases to reviewers. Every result is backed by evidence chains. MightyBot reports 99%+ extraction accuracy in a production lending deployment.
Do AI agents replace existing systems in financial services?
No. MightyBot executes inside your existing infrastructure: loan origination, servicing, claims management. The integration is the product. No rip-and-replace.
How quickly can AI agents be deployed in financial services?
Weeks, not months. A pre-built policy library supports institution-specific policies in plain English. No engineering required.
What document types can AI agents process in financial services?
Any format: PDFs, scans, spreadsheets, digital forms. Financial statements, tax returns, appraisals, inspection reports, and lien waivers. Many document types are already in production.