AI tools in finance are no longer just “nice to have,” yet they’re quickly becoming the backbone of operational efficiency, regulatory compliance, and decision accuracy. But not all tools are built for the same job.
Below, we break down seven AI tools for finance companies, each purpose-built for a specific type of task.
1. AgentFlow – Ideal for End-to-End Workflow Automation
AgentFlow by Multimodal isn’t just another automation tool. It’s an all-in-one platform for building, orchestrating, and performing work with agentic workflows—AI agents designed to replicate and improve mission-critical tasks across finance organizations.
Why it stands out:
- Automates entire workflows—not just isolated tasks—across lending, underwriting, fraud detection, compliance reporting, and more.
- Offers full deployment flexibility (private VPC, on-prem), meeting the strictest security and data residency standards.
- Built-in governance: role-based access, audit trails, confidence scoring, explainability, and process documentation.
- Designed for business users, not just engineers—SMEs can “coach” agents and embed institutional knowledge via no-code tools.
AI agents included in the AgentFlow platform:
- Unstructured AI: Transforms complex, unstructured documents into structured formats for downstream AI workflows and search applications
- Document AI: Extracts and organizes structured/unstructured data
- Decision AI: Executes high-stakes business decisions
- Report AI: Generates audit-ready regulatory documents
- Database AI: Pulls answers from complex internal databases
- Conversational AI: Delivers fast, accurate answers from internal knowledge bases and documents to support employees and customers
Thanks to the combination of these AI agents inside the platform, AgentFlow is already used in live environments to close loans, perform credit rating analysis, and manage compliance documentation.
It’s the most complete solution in this roundup and the only one designed to handle both IT and business stakeholder needs from day one.
2. SAS Viya – Ideal for Statistical Modeling and Risk Analytics
SAS Viya is the go-to platform for finance teams focused on deep statistical modeling, forecasting, and risk management. Built by SAS Institute, it’s trusted by major banks and regulators for its modeling rigor and transparency.
Key use cases:
- IFRS 9 and CECL compliance
- Credit scoring and capital adequacy modeling
- Market and liquidity risk assessments
Strengths:
- Extensive library of statistical functions and machine learning models
- Strong support for model governance and explainability
- Integration with Python, R, and REST APIs for custom applications
SAS Viya is particularly well-suited for quants and risk teams that need to embed statistical confidence into every decision.
3. ComplyAdvantage – Ideal for AML and Sanctions Screening
ComplyAdvantage uses AI to track and detect financial crime, making it a critical piece of the stack for compliance teams.
What it does well:
- Real-time AML screening
- Sanctions and PEP (Politically Exposed Person) monitoring
- Transaction risk scoring with explainable AI
It continuously updates from global watchlists and adverse media sources to ensure financial institutions don’t get blindsided by compliance risks.
Bonus: APIs allow seamless integration into onboarding and KYC workflows.
4. Abnormal Security – Ideal for Email Threat Detection and Account Protection
While not built solely for finance, Abnormal Security protects financial organizations from some of the most damaging threats: phishing, invoice fraud, and account takeovers.
Top capabilities:
- Behavioral AI that understands what’s normal for each user or vendor
- Detection of impersonation, BEC (Business Email Compromise), and lateral phishing
- Post-compromise analysis and response
Given the rising threat of fraud via digital channels, this is an essential layer of defense for finance companies managing sensitive customer and transaction data.
5. Daloopa – Ideal for Automated Financial Model Building
Daloopa streamlines one of the most repetitive and error-prone workflows in financial analysis: building and updating financial models from earnings reports, filings, and transcripts.
Standout features:
- Automates model population with structured outputs from unstructured data (e.g., 10-Ks, call transcripts)
- Accuracy-focused NLP with human-in-the-loop verification
- Spreadsheet-native outputs for seamless analyst adoption
Daloopa is a huge time-saver for equity research teams, private equity associates, and FP&A professionals who spend hours each week updating models manually.
6. AlphaSense – Ideal for Investment Research and Document Search
AlphaSense is the preferred tool for search and synthesis across large document sets—earning calls, news, filings, and internal documents.
What sets it apart:
- Smart synonym detection and semantic search
- Real-time alerts on relevant companies, sectors, or keywords
- AI summaries of complex documents
Investment firms, asset managers, and strategic finance teams use AlphaSense to speed up research and eliminate information blind spots.
7. Workiva – Ideal for Regulatory Reporting and Compliance Documentation
Workiva focuses on one of the most complex and high-risk parts of finance: reporting to regulators. From 10-Ks to ESG disclosures, it helps teams draft, review, and file documents while maintaining full traceability.
Key capabilities:
- Linked data updates across all reports
- Audit trails and approval workflows
- XBRL tagging and compliance automation
While not built on the agentic model, Workiva is deeply entrenched in the CFO tech stack and addresses core reporting needs that most AI tools overlook.
Final Thoughts
Finance leaders should evaluate AI tools based on fit for purpose, not just technical sophistication.
If your priority is scaling human expertise into full workflows, AgentFlow leads this list by a wide margin.
But depending on the job, such as screening transactions, analyzing risk, securing communications, all of the earlier mentioned specialized tools earn their spot in the stack.