AI for Finance
AI is turning finance from backward-looking reporting into real-time forecasting, control, and research.
Sound familiar?
These are the problems AI can solve for finance businesses this week — not next quarter.
Earnings calls take hours to distill
You listened to a 90-minute call. Now you need to pull out the 5 things that matter for your thesis. Your notes are scattered.
AI extracts key metrics, guidance changes, and management commentary from earnings transcripts — organized by what matters.
Free step-by-step tutorial
Use AI To Summarize Earnings CallsAbout 5 minutes. Turns a 90-minute process into a 10-minute review.
Client reports are the same template with new numbers
You update the portfolio numbers in the same Word doc every quarter. The narrative hasn’t changed in 3 quarters. Clients notice.
AI generates personalized portfolio narratives — performance context, allocation rationale, and market commentary specific to each client.
Free step-by-step tutorial
Use AI To Personalize Client ReportsAbout 10 minutes per client. Gets faster as it learns your style.
Model documentation is always "I’ll do it later"
Your financial model works. You know what the assumptions are. Nobody else does. When you’re out, nobody can use it.
AI reads your model and generates assumption narratives, methodology notes, and a documentation page — so the model outlives your memory.
Free step-by-step tutorial
Use AI To Document ModelsAbout 15 minutes for a full model. Well worth the bus factor reduction.
Get Started in Minutes
Four steps. No consultants. No multi-week rollout.
Pick your AI
Download it
Grab your skills
Start working
Detailed Setup Guides
Pick your AI assistant and follow a step-by-step guide built for finance.
Finance AI Skills Toolkit
16 ready-to-use AI skills, prompts, and a knowledge base built specifically for finance. Clone it, point your AI assistant at it, and start getting real work done with Claude or ChatGPT.
What’s in this toolkit
Build a rolling 13-week direct-method cash flow forecast that consolidates AR collections, AP disbursements, payroll, debt service, capex, and tax payments into a weekly liquidity view. Output includes opening-to-closing cash reconciliation, minimum-cash-cushion tracking, variance commentary versus prior forecast, and early warnings for covenant or liquidity stress.
Compare actual financial performance against budgeted targets, identify material variances, explain root causes, and recommend corrective actions. Produces narratives suitable for management reporting and board presentations.
Build a peer-group trading-multiples analysis ("public comps") that frames where a target company trades relative to its cohort on revenue, EBITDA, earnings, and growth-adjusted multiples. Produces a clean comp set, a multiples table with central tendency statistics, and a defensible implied valuation range.
Produce a defensible discounted cash-flow valuation from a target's historical financials, management guidance, and comparable market inputs. Output includes an explicit free-cash-flow build, WACC derivation, terminal-value treatment, sensitivity grid, and a per-share (or enterprise-value) implied range with commentary on the primary valuation drivers.
Extract key financial metrics, guidance revisions, management commentary themes, and analyst concerns from earnings call transcripts. Produces structured summaries suitable for investment research, portfolio monitoring, or internal briefings.
Write clear, structured documentation for existing financial models — covering assumptions, methodology, data sources, sensitivities, and limitations. Produces documentation suitable for model audit, team onboarding, investment committee review, or regulatory compliance.
Structure deal notes, research, and financial data into a formatted investment memorandum with a clear thesis, risk assessment, valuation summary, and comparable analysis. Produces memos suitable for investment committee review, partner distribution, or client presentation.
Compile a structured sector or sub-industry brief that sizes the market, maps the competitive landscape, catalogs recent transactions and capital flows, surfaces regulatory and macro catalysts, and synthesizes 3–5 actionable investment or strategic implications. Produces a brief suitable for coverage initiations, deal-sourcing memos, pre-meeting prep, and pitch-book appendices.
Model downside financial scenarios — market corrections, revenue shortfalls, interest rate shocks, client attrition — and quantify their impact on portfolios, cash flow, or business performance. Identifies vulnerability points and suggests mitigation strategies.
Prepare financial advisors for client and prospect meetings by organizing key data points, generating tailored talking points, anticipating client questions, and creating a structured agenda. Reduces prep time and ensures no critical topics are missed.
Draft a clear, compliant, client-ready portfolio update that covers period performance, allocation and positioning changes, benchmark context, contributions and withdrawals, and forward outlook. Produces a narrative that an advisor can review, personalize, and send without rewriting it from scratch — with the disclosures and tone required for client-facing wealth-management communications.
Scan a client's taxable portfolio for positions trading below cost basis, identify candidate harvest trades that realize losses without triggering wash-sale rules, and propose replacement securities that maintain target exposure. Output includes a ranked harvest list, lot-level tax impact, wash-sale guardrails, and a compliant reinvestment plan.
Review a draft regulatory filing against the requirements for its specific form, flag missing or inconsistent disclosures, cross-check internal consistency (numbers, dates, counterparties), and produce a reviewer checklist with severity ratings and suggested remediation language. Covers the most common SEC, FINRA, state, and international filings encountered in RIA, broker-dealer, fund, and corporate-finance workflows. Output is decision-support for a compliance officer, CCO, or filing specialist — not a substitute for legal review.
Turn rough notes into a professional email matching your company's voice and tone.
Summarize meeting notes into action items, decisions, and follow-ups.
Craft professional responses to online reviews — both positive and negative.
Auto-synced from KRASA-AI/finance-ai-skills. Updated daily.
AI Guides by Role
Find the AI setup guide built specifically for your role in finance.
AI for Financial Analysts
AI builds models, summarizes earnings calls, and generates investment memos from raw data.
View guideAI for Financial Advisors
AI generates portfolio reviews, drafts client meeting agendas, and creates retirement projections.
View guideAI for Investment Bankers
AI drafts pitch books, builds comparable company analyses, and summarizes deal terms.
View guideAI for Portfolio Managers
AI monitors position exposures, generates risk reports, and drafts investor communications.
View guideAI for Credit Analysts
AI reviews financial statements, calculates key ratios, and drafts credit memos with risk assessments.
View guideAI for Treasury Analysts
AI forecasts cash positions, tracks bank fee analyses, and generates daily liquidity reports.
View guideAI for FP&A Analysts
AI builds budget models, generates variance commentaries, and creates board-ready financial decks.
View guideAI for Wealth Managers
AI personalizes portfolio updates, drafts estate planning summaries, and generates tax-loss harvesting reports.
View guideAI for Compliance Analysts in Finance
AI monitors regulatory filings, drafts compliance reports, and flags policy violations.
View guideAI for Fund Accountants
AI calculates NAVs, reconciles positions, and generates investor-facing performance reports.
View guideFree Step-by-Step Tutorials
Each workflow takes minutes, not months. Pick one and start.
Use AI To Summarize Earnings Calls
About 5 minutes. Turns a 90-minute process into a 10-minute review.
- 1
Download Claude or ChatGPT and open the Earnings Call Summarizer skill
- 2
Paste the earnings call transcript (most are available on SeekingAlpha or the company’s IR page)
- 3
AI generates a summary: revenue/EPS vs. consensus, guidance changes, key management quotes, risk callouts, and segment highlights
- 4
Review the summary, flag what matters for your coverage, and move to the next name on your list
Use AI To Personalize Client Reports
About 10 minutes per client. Gets faster as it learns your style.
- 1
Open the Client Portfolio Update skill
- 2
Input the client’s portfolio: holdings, returns for the period, benchmark comparison, any changes made
- 3
Add context: "Client is 62, conservative growth, worried about inflation, asked about gold last quarter"
- 4
AI generates a narrative that explains performance, addresses their concerns, and frames next steps — specific to them, not boilerplate
Use AI To Document Models
About 15 minutes for a full model. Well worth the bus factor reduction.
- 1
Open the Financial Model Documenter skill
- 2
Paste or describe the key tabs: revenue build, cost assumptions, WACC inputs, sensitivity ranges
- 3
AI generates documentation: assumption descriptions, data sources, methodology notes, and a change log template
- 4
Attach as a cover sheet or separate tab — now anyone can understand and update the model
Real-World Use Cases
AI cash forecasting for treasury
Treasury teams are using AI to predict cash positions, compare forecasts to actuals, and surface the drivers behind misses. This replaces one of the most manual recurring finance jobs: reconciling cash views across banks, entities, and planning models.
Tools:
Impact:
Bank of America said CashPro Forecasting helped 3,000+ companies save more than 250,000 hours in 2025; results arrive in minutes instead of days.
Source: Bank of America / The Asian Banker coverage, 2025, GTreasury treasury AI article, 2026
Investment research and due diligence compression
Buy-side and corporate development teams are using AI search to scan filings, transcripts, expert calls, broker notes, and news, then jump directly to the relevant paragraph. The job changes from document retrieval to thesis testing.
Tools:
Impact:
AlphaSense says a $50B AUM fund cut research time by 75% while improving output quality.
Source: AlphaSense case study, 2025
Advisor enablement in wealth management
Wealth teams are using AI copilots to retrieve research faster, summarize complex market moves, and prep client responses during volatile periods. The practical win is more advisor time for client conversations and less time spent hunting for internal content.
Tools:
Impact:
Morgan Stanley said AI could save financial advisors 10-15 hours per week; JPMorgan said AI helped support faster service and expects advisors to expand client loads by 50% within five years.
Source: Reuters, June 2024 and May 2025
Management reporting and commentary drafting
FP&A teams are feeding monthly decks, BU notes, and actual-vs-plan exports into LLMs to produce first-pass variance summaries, executive commentary, and board-pack language. Humans still edit, but the blank page is gone.
Tools:
Impact:
Practitioners in r/FPandA report using ChatGPT to consolidate monthly commentary; EY describes reporting workflows where AI cut manual workload by 60%.
Source: r/FPandA thread 'GPT use cases', 2026, EY FP&A report, 2025
Month-end close acceleration
Finance teams are using AI and workflow automation to handle reconciliations, report prep, and exception spotting so close work shifts from repetitive assembly to review. This is one of the clearest paths to measurable ROI because cycle time is easy to track.
Tools:
Impact:
AidKit cut month-end close from 15 days to 4 days and saved 40-50 hours of manual work monthly.
Source: Rillet customer case study, 2025
Accounts payable and spend control automation
Finance ops teams are using AI to extract invoice data, match receipts, code spend, flag policy violations, and reduce manual follow-up. The immediate benefit is fewer touchpoints per invoice and cleaner close support.
Tools:
Impact:
Ramp says 50,000+ businesses have saved 27.5 million hours; Concur reported more AP teams now spend under 10 hours per week processing invoices than a year earlier.
Source: Ramp G2 profile, 2026, SAP Concur AP automation trends, 2025
Natural-language querying of finance data
Teams are building internal copilots that let users ask plain-English questions like 'show legal expense for Q1 by department' instead of waiting for analyst turnaround. This is becoming the fastest way to widen access without handing everyone raw ERP tables.
Tools:
Impact:
Gartner says knowledge management is now the most common finance AI use case at 49% among adopters.
Source: Gartner AI in Finance Survey, 2025, r/FPandA thread on custom ChatGPT for financial data, 2025
Scenario planning and rolling forecasts
Finance teams are using AI-assisted planning tools to run multiple scenarios faster, update assumptions in one place, and generate narrative around what changed. That matters most when demand, pricing, or working capital moves faster than a quarterly planning cycle.
Tools:
Impact:
BCG found the strongest finance AI ROI is showing up in risk and forecasting; Pigment users on G2 describe significant time savings from centralized scenario modeling.
Source: BCG finance AI ROI study, 2025, G2 Pigment reviews, 2026
Fraud detection and payment-risk monitoring
Banks and payments teams are using AI to score transactions in real time, spot synthetic identity patterns, and reduce false positives that frustrate customers. This is not experimental anymore; it is core operating infrastructure.
Tools:
Impact:
Mastercard says AI is helping banks save millions by improving real-time fraud detection and approval decisions; Feedzai says more than half of fraud now involves AI or deepfakes.
Source: Mastercard insights, 2026, Feedzai fraud trends report, 2025
Excel model building and audit support
Practitioners are using AI to write formulas, refactor old models, document logic, and update yearly commission or planning calculators. In audit-heavy environments, AI inside Excel is also speeding up evidence extraction and tying workpapers back to source documents.
Tools:
Impact:
r/FPandA users report rebuilding calculators and formula logic with Claude; DataSnipper says it delivered more than $1.4B in productivity savings across audit and finance workflows in 2025.
Source: r/FPandA 'How I've used Claude for Excel', 2026, G2 DataSnipper profile, 2026
Top AI Tools for Finance
Datarails
Spreadsheet-native FP&A and reporting platform used by finance teams that do not want to abandon Excel. Practitioners use it for consolidation, budgeting, cash visibility, month-end reporting, and AI-assisted insights without forcing a full workflow rewrite.
Contact for pricing
Ramp
Finance automation platform for expense management, cards, AP, procurement, and software spend. Finance teams use it to reduce receipt chasing, speed close, and get AI help on coding, policy checks, and vendor spend visibility.
Free plan available; paid tiers available
AlphaSense
AI research platform for investment, strategy, and corporate finance teams. Used to search filings, earnings calls, broker research, expert interviews, and news, then summarize the relevant evidence fast enough to change how diligence actually gets done.
Contact for pricing
Pigment
Modern planning platform for finance teams that need live scenario modeling, workforce planning, and cross-functional planning. Best for organizations moving beyond spreadsheet sprawl and wanting AI agents in planning workflows.
Contact for pricing
Vena
Excel-first planning platform used for budgeting, forecasting, financial close, and reporting. A strong fit for finance teams that want structure, workflow, and collaboration while keeping spreadsheet familiarity.
Contact for pricing
Cube
AI-powered financial intelligence platform built around the spreadsheet habits finance teams already have. Useful for planning, reporting, and cross-functional reviews when you need fewer version-control problems and faster forecast cycles.
Starting at $32K annually
Planful
Mature FP&A platform used for budgeting, continuous planning, close, and management reporting. Finance teams choose it when they need stronger workflow, data centralization, and enterprise controls than ad hoc spreadsheets can provide.
Contact for pricing
DataSnipper
Excel-based intelligent automation for audit and finance teams. It is used for document matching, evidence extraction, reconciliation support, and faster workpaper preparation in controlled environments.
Contact for pricing
Expert Service Providers
Accenture
enterpriseLarge-scale AI strategy and implementation partner for banks, insurers, wealth managers, and finance functions. Best for firms that need operating-model redesign, governance, and production deployment across multiple business lines.
Quantiphi
mid-marketAI services firm with finance-specific delivery across conversational AI, fraud workflows, customer service, and document-heavy operations. Good fit when you need a custom solution rather than a packaged SaaS tool.
Slalom
mid-marketConsulting and build partner for financial institutions modernizing data, cloud, and AI workflows. Especially relevant for firms that need to move from pilot projects to governed deployment.
Deloitte
enterpriseEnterprise advisor for finance transformation, controls, risk, and AI deployment inside regulated environments. Strong when the AI program has to satisfy CFO, audit, risk, and compliance stakeholders at the same time.
Frequently Asked Questions
People Are Searching For
Recommended Reading
How finance teams are using ChatGPT for monthly commentary in 2026
Datarails vs Cube vs Vena: which AI-friendly FP&A stack fits your team?
What finance leaders should automate before they buy an enterprise AI platform
How to pilot AI in FP&A without breaking controls
7 finance workflows where Claude for Excel is already useful
What treasury teams can automate first with AI cash forecasting
How Ramp changes the close for lean finance teams
What AlphaSense actually replaces in the research workflow
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