Top 10 AI Prompts and Use Cases and in the Financial Services Industry in Joliet

AI is reshaping how Joliet banks, credit unions and local fintechs handle high-volume tasks - automating document processing, speeding account onboarding, surfacing anomalies for fraud teams and personalizing customer offers - so a single smart deployment can cut manual back-office hours while improving compliance and speed; Lucinity's guide outlines how generative AI strengthens compliance and investigations (Generative AI in financial services: Lucinity guide) and Google Cloud maps practical uses from anomaly detection to Document AI and contact-center automation (AI applications in finance: Google Cloud).

Illinois policy also matters: HB1859, signed by Gov. Pritzker, prohibits community colleges from using generative AI in place of a primary instructor (effective 2026), so Joliet organizations should pair tool rollouts with instructor-led upskilling and local pathways to reskill workers into FP&A, modeling and data roles (Local training and upskilling pathways for Joliet financial services).

Bootcamp	AI Essentials for Work - Key details
Length	15 Weeks
Focus	Use AI tools, write effective prompts, apply AI across business functions
Cost	$3,582 early bird; $3,942 afterwards; paid in 18 monthly payments
Syllabus / Register	AI Essentials for Work syllabus • AI Essentials for Work registration

"I found the written assignment useful in that you researched AI in financial services, and were encouraged to use a LLM to complete the assignment."

Selections began with business outcomes: each prompt or use case had to map to a clear Joliet pain point - faster onboarding, safer fraud detection, or clearer executive summaries - and to technical realities shown in real-world PoCs.

Criteria were drawn from Zartis' tech‑stack checklist - align with company strategy, confirm compatibility with existing data sources and deployment environments, and weigh capabilities, cost and scalability (Zartis AI Tech Stack & Tools Selection Guide) - then validated against industry pilots: Akbank's Jasper AI tests cut content creation time by 40% across ten campaigns, while Ingosa's conversational banners drove 6,322,452 impressions with a 0.11% engagement uplift, illustrating how measurable PoC metrics decide which prompts scale (Akbank Generative AI Marketing PoC Results).

Emphasis on data quality, monitoring for model drift, and local compliance ensured each top‑10 entry is both practical for Joliet teams and ready for a small, fast proof‑of‑concept that delivers a clear ROI.

Selection Criterion	Why it matters
Strategy alignment	Ensures prompts solve business goals
Compatibility	Works with existing data sources and deployment
Capabilities & Cost	Right model for the task at sustainable cost
Scalability & Monitoring	Supports growth and drift management
Data privacy & Compliance	Meets regulatory and local governance needs

"Embracing Innovation with Ingosa"

Prompt a model to turn Joliet income and expense ledgers into an actionable finance report: ask for cleaned monthly P&L inputs, then run trend‑line, moving‑average and year‑over‑year (YoY/CAGR) analyses, flag seasonality and anomalies, and output a one‑page executive summary with line charts, a cash‑runway calculation (example: 120,000/20,000 = 6 months) and prioritized next steps - price testing, expense cuts, or tapping alternative capital sources.

Revenue trend analysis techniques like trend lines, moving averages and seasonal decomposition reveal whether a revenue dip is structural or seasonal and improve forecasting accuracy (Revenue trend analysis guide from 180Ops); pair that with lender and cashflow context - OnDeck's cash‑flow research shows small firms shifting to non‑bank capital when banks tighten (OnDeck small business cash-flow trends report) - and local service organizations reporting weaker revenue growth underscores why the prompt should surface early warnings so managers can act before margins erode (Survey of Small Business Resource Organizations 2025).

Deliverables: clean charts, a 3‑point risk rating, recommended tactical moves, and a short narrative tying trends to payroll, tariffs or local demand so leaders know the “so what” immediately.

Analysis Method	Purpose
Trend lines	Show long‑term direction of revenue/expenses
Moving averages	Smooth short‑term volatility to reveal underlying trend
YoY / CAGR	Compare performance across same periods to detect real growth or decline
Seasonal decomposition / Regression	Isolate seasonality and test drivers (marketing spend, tariffs)

Turn cash‑flow anxiety into a clear, testable target: prompt an LLM to generate a SMART cash‑flow goal that ties a specific closing balance to Joliet operating needs (for example, a reserve equal to three months of payroll and fixed costs), shows how to measure progress with QuickBooks' Cash Flow Planner and automated imports (use SaasAnt to keep data current), explains why the target is achievable using a rolling cash‑flow forecast, and sets a time bound for hitting the goal (e.g., improve runway from 1 to 3 months in six months); include the exact formulas and a one‑line check (closing balance = opening balance + total inflows − total outflows) and ask the model to output checkpoints and contingency steps so managers know when to delay spend or seek short‑term financing.

Use a downloadable template and projection examples to seed the prompt (Smartsheet cash flow projection guide), tie measurement to forecasting tools (SaasAnt cash flow forecasting for QuickBooks), and frame the goal as realistic local planning to protect Joliet payroll and seasonally variable revenue streams (Grow America cash flow forecast goal-setting).

SMART Element	Action	Tool / Reference
Specific	Target closing balance = 3 months operating expenses	Smartsheet projection template
Measurable	Weekly/Monthly runway calculation and variance vs forecast	QuickBooks Cash Flow Planner + SaasAnt
Achievable	Base on rolling forecast scenarios (best/worst/likely)	Rolling forecast guide / Excel template
Relevant	Protect payroll and supplier payments during seasonal dips	Local business patterns - Joliet planning
Time‑bound	Increase runway to goal within 3–6 months with checkpoints	Monthly reviews and template updates

Prompt an LLM to produce a like‑for‑like year‑over‑year (YoY) comparison by first freezing the comparison cutoff with a Max Date level‑of‑detail (LOD) (radar‑analytics' 7/11/2018 example shows this in practice), then filter out dates after that cutoff so each year's YTD window matches; in Tableau, place YEAR(Report Date) and MONTH(Report Date) as discrete fields (blue) so the Quick Table Calculation for Year Over Year Growth is enabled and not greyed out, and if your analytics tool lacks a native YTD option follow Maximizer's approach of building YTDSUM/PASTYEAR widgets or set the indicator source to monthly as discussed in Performance Analytics threads - these steps produce a true “this year to this date vs.

last year to this date” view instead of misleading full‑year comparisons. The payoff: a clear, actionable chart that surfaces real seasonal shifts so Joliet finance leaders can decide whether a dip is timing‑related or needs corrective action now (Tableau year-to-date versus year-over-year technique, Maximizer compare year-to-date to last year widget formulas, Light Blue Software compare this year's sales with last year's up to this point).

Key step	Purpose
Max Date LOD	Freeze comparison date (e.g., return 7/11/2018) so YTD windows align
Discrete YEAR/MONTH	Enable Year Over Year Quick Table Calculation in Tableau

Prompt an LLM to run a Joliet‑specific risk assessment that ingests P&L, AR aging, bank covenants and contract clauses, then classifies and scores risks by likelihood, severity and estimated cash impact - for example, flag liquidity shortfalls under a three‑month payroll runway, surface concentrations in a single revenue source, detect rising credit‑risk accounts, and list missing insurance or compliance gaps; pair each finding with prioritized mitigations (shorten terms, diversify funding, buy business‑interruption/cyber liability, or automate invoicing) and an implementation checklist for local vendors and accountants.

Use templates from NetSuite's catalog of small‑business financial risks to enumerate credit, market, liquidity and operational exposures (NetSuite: 21 Financial Risks for Small Businesses), and align threats with Country Financial's Top‑10 checklist - property, interruption, cyber and supply‑chain - to make recommended actions auditable and insurance‑ready (Country Financial: Top 10 Threats to Small Businesses).

The deliverable: a one‑page ranked risk map, estimated worst‑case cash hit, three tactical steps, and a 90‑day monitoring schedule so Joliet managers know exactly when to pause spending or tap emergency credit.

Risk	Recommended mitigation
Liquidity shortfall	Maintain 3‑month reserve + line of credit
Credit risk	Conduct credit checks, shorten terms, tighten collections
Cybersecurity	Third‑party security service + employee training
Business interruption	Buy interruption insurance; contingency plan
Operational inefficiency	Process audits and automation to cut costs

“For the last four years, small business owners have struggled with historic inflation, tax pressures at all levels of government, and uncertainty of what's going to happen next,” said Holly Wade, Executive Director of the NFIB Research Center.

Turn budget debates into data-driven decisions by prompting a model to score every budget request on three metrics - estimated payback, expense‑savings potential, and strategic alignment - and then rank items into Sliwa's four priority buckets (Revenue Growth, Expense Savings, Strategic, Unmet Needs) so leadership funds Category 1–2 investments first; use the framework's payback discipline (examples range from rapid six‑month paybacks in tight times to two years in calmer cycles) to make tradeoffs explicit and auditable (Sliwa budget prioritization framework for data-driven budgeting).

Combine that ranking with a reforecast trigger: Drivetrain recommends reforecasting when material changes occur and many fast‑moving firms actually budget just six months ahead, using interim reforecasts to stay agile (Drivetrain guide to budget reforecasting and rolling forecasts).

Finally, automate scoring, consolidation and scenario testing with FP&A tools - Limelight and similar platforms cut cycle times and enable rolling forecasts so the model can surface a short list of high‑impact items (e.g., hires or software with <12‑month payback) that free cash quickly; the result is a prioritized plan that protects Joliet payroll during seasonal dips and converts requests into clear, fundable business cases (Limelight FP&A tools for faster budgeting and rolling forecasts).

Category	Type / Working definition
1 - Revenue Growth	Investments that yield incremental revenue within an acceptable payback period
2 - Expense Savings	Investments that reduce costs with measurable payback
3 - Strategic	Spending that supports the adopted strategic plan
4 - Unmet Needs	Operational improvements, productivity or customer‑experience items

Prompt an LLM to generate a concise executive dashboard summary tailored for Joliet leadership: a one‑page view that surfaces 5–7 priority KPIs (total revenue, burn rate, cash runway in months, gross margin, AR aging, CSAT) with trend lines, a red/amber/green health score, and the top three recommended actions tied to local priorities (protect payroll during seasonal dips, accelerate collections, or pause discretionary hires); include drilldowns and the exact formulas for runway and YoY growth so the dashboard is auditable and reproducible in Power BI or Tableau.

Use design rules from executive‑dashboard guides - limit metrics, show trend vs. target, and highlight anomalies - so C-suite and board members get the “so what” immediately: spot a falling runway or rising churn and decide whether to tighten spending or tap short‑term credit.

For metric selection and sample layouts, see the Geckoboard executive dashboard metrics guide (Geckoboard executive dashboard metrics guide) and Upsolve financial dashboard examples and KPIs (Upsolve financial dashboard examples and KPIs).

KPI	Purpose
Total revenue	Track topline trend and YoY growth
Burn rate / Cash runway	Measure liquidity in months to inform hiring/financing decisions
Gross margin	Monitor profitability pressure from costs
AR aging	Identify collection risks that threaten cash flow
CSAT / retention	Signal customer health and revenue sustainability

"CFO dashboards enable CFOs to identify areas for improvement, optimize financial resources, and make strategic decisions that align with the company's growth goals", explains a financial advisory expert at Phoenix Strategy Group.

Prompt an LLM to first calculate baseline forecast accuracy (Forecast Accuracy = (1 − |Forecast − Actual| / Actual) × 100), then produce a 90‑day improvement plan tailored to Joliet's seasonal revenue patterns: audit and clean CRM/ERP pipeline data, select and test 2–3 forecasting methods (time‑series, bottom‑up driver models, AI ensembles), run 30/60/90‑day rolling forecasts, and incorporate external factors (local demand, economic indicators, weather/seasonality) with automated variance reporting and reforecast triggers; include exact formulas, validation checks, and a clear KPI: target a 10–25% accuracy lift (Forecastio and Abacum case studies show AI and ML yield ~12–25% gains and pipeline hygiene can add up to 25%), explain tradeoffs, and output an executive one‑page scorecard showing current accuracy, target, expected cash‑runway improvement and the “so what” - for example, a 10% accuracy gain often converts to materially better resource allocation and can preserve payroll during Joliet's seasonal dips.

Deliverables: baseline metric, weekly data‑quality checklist, model comparison table, and a monthly review cadence linked to actionable triggers (Forecastio's 8-step framework to improve forecast accuracy, NetSuite's 10 tips for improving forecast accuracy).

Key action	Immediate output
Measure baseline	Accuracy %, MAPE
Clean data	Pipeline hygiene checklist
Test methods	Model performance comparison
Implement AI	Probability scores, anomaly flags
Review cadence	Weekly checks, monthly reforecast

“Before implementing our new forecasting process, we were essentially guessing. Now, we have data-driven confidence in our numbers, which has transformed how we run the business.” - Sarah Johnson, CRO (Forecastio case study)

Prompt an LLM to ingest invoice‑level AR aging, recent cash receipts and net credit sales, then calculate core KPIs - Accounts Receivable Turnover, Days Sales Outstanding (DSO), AR aging buckets and a Collections Effectiveness Index - to segment customers by payment behavior and revenue concentration and surface the small set of late‑paying accounts that drive most cash risk; Mosaic's AR guide spells out the formula and interpretation for turnover (AR turnover formula and guide - Mosaic financial metrics) while Upflow shows why tracking that ratio over time reveals shifts in customer payment habits worth investigating (How to track accounts receivable turnover over time - Upflow).

The model should output prioritized actions - automated reminders, multiple payment options, early‑payment discounts and, when immediate cash is needed, invoice factoring as a tactical liquidity tool (Accounts receivable analysis and invoice factoring explained - altLINE) - plus an explainable ranking of at‑risk customers and expected cash impact; for example, improving AR turnover from ~6 (≈61 days) to ~12 (≈30 days) roughly halves the cash conversion lag, stabilizing cash flow and helping Joliet firms protect payroll during seasonal dips.

Metric	Formula / Use
Accounts Receivable Turnover	Net Credit Sales ÷ Average Accounts Receivable - measures collection frequency
Days Sales Outstanding (DSO)	365 ÷ AR Turnover - converts turnover to average collection days
AR Aging	Bucket open invoices (Current, 1–30, 31–60, 61+) to target collections and forecast cash

Prompt an LLM to turn Joliet profit and loss statements into a clear cost map: classify each line as fixed, variable, or mixed; compute fixed cost per unit and total variable cost curves; and run a break-even calculation (Break‑Even = Fixed Costs ÷ (Price per Unit − Variable Cost per Unit)) so leaders can see how price, volume, or cost shifts move the breakeven point.

Surface quick wins such as swapping fixed salaries for variable contractor pay or commission-based plans and quantify cash impact (ORBA's field example shows swapping fixed salaries for variable ones reduced labor costs by $240,000/year).

Then test scenarios that trade operating leverage for flexibility to protect local payroll. Include recommended actions - negotiate leases, move subscription fees to usage tiers, or introduce early-payment discounts - and attach the exact formulas and per-line classification so the output is auditable for Joliet accountants and lenders.

For definitions and examples to seed the prompt, reference ORBA's fixed vs. variable cost guide and Block Advisors' variable cost calculations: ORBA Cloud CFO guide to fixed versus variable costs (ORBA Cloud CFO - Fixed vs. Variable Costs Guide) and Block Advisors variable cost resource (Block Advisors - Variable Cost: Definition and Examples).

Category	Examples	Purpose in analysis
Fixed costs	Rent, insurance, salaried payroll, leases	Baseline obligations that determine operating leverage
Variable costs	Raw materials, shipping, commissions, hourly labor	Scale with production and affect unit margins
Key formula	Break‑Even = Fixed ÷ (Price − Variable/unit)	Shows sales needed to cover all costs

Prompt an LLM to calculate a concise Financial Health Score (0–100) that combines liquidity, solvency, profitability and operating‑efficiency metrics into one auditable index: ingest the balance sheet, P&L and cash‑flow, compute standard formulas (current & quick ratios, net margin, debt‑to‑equity, AR turnover), show each sub‑score and the overall Calqulate‑style index, and output a one‑line “so what” plus three prioritized actions tied to Joliet realities (e.g., flag immediately if runway falls below a three‑month payroll reserve so managers can pause hires or accelerate collections).

Require the model to include exact formulas and data checks, a ranked list of the top three drivers behind a weak score (low liquidity, rising leverage, slow AR), and exportable recommendations - shorten terms, negotiate a line, or run a variable‑cost scenario - so local finance teams can act within 30–90 days.

Use the four core assessment areas from Investopedia and the concrete ratio examples in the BDC guide to seed the prompt, and return both a human‑readable summary for executives and a CSV of all computed ratios for auditors (Investopedia: Four Financial Health Assessment Areas, BDC: Financial Ratio Categories and Examples, Calqulate: Financial Health Index Documentation (0–100)).

Category	Example ratios
Liquidity	Current ratio, Quick (acid) ratio
Profitability	Gross profit margin, Net profit margin, ROA/ROE
Activity / Efficiency	Inventory turnover, Average collection period / DSO
Leverage / Solvency	Debt‑to‑equity, Debt coverage / interest coverage

“When conducting a financial analysis using health metrics like these, remember that numbers can tell us a lot about an organization's finances, but they also have their limitations.”

For Joliet banks and credit unions, conversational AI chatbots offer an immediate way to improve customer experience and staff efficiency by handling routine, high‑volume tasks - balance inquiries, card locks/unlocks, payment reminders and simple loan status checks - around the clock so human agents can focus on complex cases and exceptions; industry guides show chatbots reduce wait times, enable personalized responses by pulling account history, and scale without linear headcount increases (Chatbots in Banking: Trends and Innovations, Banking Chatbots: Benefits and Use Cases).

Deployments should be paired with clear escalation paths and compliance checks: CFPB research warns that chatbots perform well on basic queries but can fail on complex disputes, so a human fallback and audit trail are essential for consumer protection (CFPB Report on Chatbots in Consumer Finance).

The clear payoff for Joliet teams is practical - automate the top routine inquiries and reclaim staff time for higher‑value work that preserves service quality and reduces costly call center overload.

Primary chatbot benefit	What it delivers
24/7 availability	Immediate answers to routine queries, fewer after‑hours calls
Scalability & cost savings	Handle many interactions simultaneously without proportional staffing
Employee enablement	Automate menial tasks so staff focus on complex cases and training

"better business outcomes, including higher balances for current account savings accounts, lower cost-to-income ratios, increased customer acquisition and retention rates, and faster time to market."

Next steps for Joliet beginners: start small, measure fast, and upskill locally - run a 90‑day pilot on a high‑volume internal use case (onboarding automation or AR reminders) that has clear cash‑impact metrics so leaders can see whether the experiment protects payroll and shortens DSO; pair that pilot with board‑level alignment and data fixes so results are auditable.

Follow a five‑step adoption playbook - define clear objectives, get expert support, use cloud infrastructure, ensure compliance, and monitor adaptively - using Avaloq's practical checklist (Avaloq five‑step AI adoption guide) and benchmark expectations against industry readiness from the EY survey on AI adoption.

For Joliet workers seeking concrete skills, consider Nucamp's AI Essentials for Work bootcamp (15 weeks) to learn prompt design, tool workflows and workplace applications before scaling pilots (AI Essentials for Work - registration).

Action	Nucamp option / detail
Upskill staff for prompt‑driven workflows	AI Essentials for Work - 15 weeks; AI Essentials for Work syllabus & AI Essentials for Work registration: AI Essentials for Work syllabus • AI Essentials for Work registration
Build a founder/operator pilot	Solo AI Tech Entrepreneur - 30 weeks for productizing AI use cases; Solo AI Tech Entrepreneur details and registration

“Blind optimism and hype can be counterproductive. An ‘innovation intelligence' approach - planning, education, and agile test-and-learn strategies - is imperative to harness AI's benefits.” - David Kadio‑Morokro, EY Americas Financial Services Innovation Leader