Using AI to Forecast the Impact of Military Spend on Business Demand Deposits
When the U.S. government raises defense spending, the effects ripple far beyond Washington. Billions of dollars flow into defense contractors, logistics networks, small suppliers, and local communities that orbit the military-industrial complex. Those funds eventually land somewhere else: in the business demand deposits that banks hold.
Military expenditure doesn’t just drive national security; it drives cash cycles. Each new federal contract triggers accounts receivable, payroll, and supplier payments that temporarily inflate business balances. And just as quickly, those balances evaporate when invoices settle, production slows, or procurement budgets tighten.
For small and regional banks, this pattern creates both opportunity and risk. Deposit inflows tied to defense outlays can boost liquidity, but they also make cash positions more volatile, especially in regions with defense clusters like Virginia, Texas, or California.
As defense budgets continue to expand, the question for banks becomes: can we predict how military spending reshapes business deposit behavior?
And more importantly: can artificial intelligence help us forecast those shifts faster and more precisely than traditional models?
The Link Between Military Spending and Business Deposits
Defense spending represents one of the largest fiscal levers in the U.S. economy. The Department of Defense’s FY2025 budget exceeds $850 billion, up 7% from 2024, with about 60% channeled into private-sector contracts. These can range from aircraft manufacturing to IT and cybersecurity.
That magnitude of government outlay produces measurable liquidity surges across multiple tiers:
- Tier 1- Prime contractors: major defense firms like Lockheed Martin, Northrop Grumman, and Raytheon receive lump-sum advances, often deposited at large banks or treasury divisions.
- Tier 2-Subcontractors and suppliers: smaller regional manufacturers and service providers, many banking locally, experience sharp but temporary spikes in working capital.
- Tier 3- Indirect participants: logistics companies, housing providers, and small retail businesses in defense-heavy communities see cash inflows as spending multiplies.
The Bureau of Economic Analysis (BEA) estimates that every $1 of defense spending generates $1.50–$1.80 in total output, depending on regional multipliers. Those flows translate directly into short-term business deposit inflows, followed by outflows once contracts stabilize or federal payments taper.
Historically, FDIC data show that business deposits in regions like Hampton Roads (VA) and San Diego (CA), both defense-dependent economies, exhibit higher volatility and faster decay rates than comparable metro areas with more diversified industries.
Why Traditional Deposit Models Fall Short
Most banks still forecast business deposit behavior using static or linear model regressions on interest rates, seasonality, and GDP growth. But military spending introduces episodic, nonlinear shocks that break those assumptions.
- Timing misalignment: Defense outlays follow congressional cycles, not macro ones. Deposit surges can occur off-calendar (e.g., when Congress passes supplemental funding).
- Sectoral asymmetry: Defense manufacturing reacts differently from software or logistics, the same fiscal dollar doesn’t move through the same channels.
- Regional concentration: A local bank serving 40% of defense suppliers in Huntsville, AL, will see far more pronounced liquidity swings than a bank in Kansas City.
Traditional regression models can’t capture these nuances because they assume stationarity, the idea that relationships between variables don’t change. In reality, they change with every procurement cycle, contract award, and geopolitical shock.
That’s where AI forecasting models come in: to detect, learn, and adapt to complex fiscal-to-liquidity patterns faster than any static model could.
How AI Enhances Forecasting Precision
Artificial Intelligence doesn’t just crunch numbers, it detects relationships that humans overlook. When applied to deposit forecasting, AI enables banks to integrate macroeconomic, regional, and behavioral data into one cohesive view.
Key Model Types
1 – LSTM (Long Short-Term Memory Networks)
- Excellent for time-series forecasting with lagged dependencies (e.g., delays between military payments and deposit changes).
- Can learn periodic behavior:such as deposit inflows every Q2 aligned with defense contract awards.
2 – Gradient Boosting / XGBoost Models
- Ideal for tabular, nonlinear relationships.
- Combine defense outlay data, supplier concentration, and local employment trends to predict deposit volatility.
3 – Hybrid Models (Econometric + AI)
- Combine the interpretability of macro models (like Vector Autoregression) with AI’s pattern recognition.
- These models can simulate counterfactuals, “what happens if DoD spending rises 10% next year?”
Practical Data Inputs
DoD outlays (monthly, by contract type and region)
BEA industry-level multipliers
Regional payroll data (BLS)
FDIC business deposit series
FRED macro variables (rates, GDP, inflation)
By training models on multi-source data, banks can anticipate when and where defense spending will translate into liquidity, rather than reacting after the fact.
Case Study 1:Huntsville, Alabama (Defense Cluster Capital)
Huntsville hosts over 300 aerospace and defense firms tied to Redstone Arsenal. Between FY2023 and FY2024, local federal contract awards rose 12.4%, totaling $9.8 billion.
A mid-sized community bank in the region noticed erratic business deposits during procurement cycles, inflows up 8–10% after DoD contract approvals, followed by sharp drawdowns once suppliers paid subcontractors.
After adopting a machine-learning forecasting model (LSTM trained on 10 years of contract and deposit data), the bank:
- Improved deposit forecast accuracy from ±14% error to ±4%;
- Adjusted liquidity buffers proactively before disbursement surges;
- Reduced funding cost volatility by 11 basis points over two quarters.
The AI model detected that procurement announcements were a leading indicator and that deposit growth peaked roughly 6–8 weeks after major DoD payment releases.
Case Study 2: San Diego, California (Defense-Adjacent Tech Hub)
San Diego’s defense economy includes naval shipbuilding, communications systems, and dual-use technology. During FY2024, naval contracts exceeded $24 billion, with strong spillover to local SMEs.
A regional credit union serving 1,500 small businesses saw deposit volatility tied to contractor payrolls. Using a hybrid AI + econometric model, the credit union integrated:
- DoD outlays by category;
- regional employment in manufacturing and R&D;
- and transactional data from business checking accounts.
The result:
- Forecast horizon expanded from 30 to 90 days with higher confidence.
- The institution aligned its liquidity strategy with upcoming defense cycles, improving asset-liability match.
- ALCO used model outputs to adjust FTP and internal liquidity transfer pricing.
This case showed that AI didn’t just forecast balances, it forecast behavior. The model learned that post-contract cash surges lasted, on average, 47 days before normalization.
Implications for ALCO and Treasury Management
For Asset-Liability Committees (ALCO), understanding deposit behavior is fundamental to managing:
- Liquidity Coverage Ratios (LCR)
- Net Interest Margin (NIM)
- Behavioral deposit decay
- Stress testing and contingency funding plans
AI forecasting offers several tactical advantages:
- Early-warning signals: models flag periods of likely deposit drain before they occur.
- Dynamic scenario testing: run fiscal-expansion vs. fiscal-tightening simulations.
- Data-driven behavioral assumptions: replace outdated static decay rates with adaptive ones.
Capital efficiency: better precision in deposit forecasts allows banks to optimize reserve allocation.
In short: by merging fiscal data (like military spend) with internal liquidity analytics, banks turn macro volatility into a managed variable rather than a surprise.
Integrating AI Forecasts Into BARK
At Jabuticaba, we built BARK to simplify this exact challenge: turning complex,policy-driven liquidity signals into clear, actionable insights.
With BARK, ALCO and treasury teams can:
- Import macro variables such as DoD budget projections, BEA industry multipliers, and regional payroll data.
- Run AI-enhanced deposit forecasts using pre-trained machine learning models calibrated for fiscal spending shocks.
- Generate dashboards showing deposit volatility bands, liquidity gaps, and scenario comparisons.
Export ALCO-ready reports with stress-test visuals and risk indicators.
Common Pitfalls in Military-Spending Forecasting
Even advanced models fail if the data or framing are wrong. Here’s what to avoid:
- Overfitting short historical windows. Military budgets move in multi-year cycles; you should train models across decades.
- Ignoring regional effects. Spending patterns differ dramatically between states so local context matters.
- Confusing correlation with causation. AI may find patterns that are real statistically but economically spurious.
- Neglecting macro-policy shifts. Fiscal tightening or sequestration can reverse trends overnight.
- Failing to integrate human oversight. AI provides probability, not judgment, and thus ALCO’s role is to interpret it.
The Strategic Advantage of Seeing Before It Happens
Forecasting the liquidity effects of defense budgets used to be a manual, backward-looking process. Today, AI enables banks to anticipate the timing, magnitude, and duration of fiscal impacts with unprecedented clarity.
For community and regional banks, that means:
- Fewer liquidity surprises.
- Better pricing decisions.
- Stronger compliance with regulatory expectations for behavioral modeling (Basel, IRRBB).
In a world where fiscal cycles are increasingly driven by geopolitics, the ability to “see around corners” isn’t just a data advantage, it’s a strategic moat.
Defense spending may start in Washington, but its liquidity effects end up in your balance sheet. By using artificial intelligence to forecast those patterns, banks gain a forward-looking view of how fiscal policy shapes deposit behavior,from Huntsville to San Diego.
And with tools like BARK, that kind of insight no longer takes months of modeling or complex integration. It’s fast, transparent, and built for banks that need to act, not just react.
Because in banking, the ability to forecast is the ability to prepare.
Curious how military spending could impact your deposit base this year?
Try BARK and simulate fiscal scenarios and forecast your liquidity with confidence.
References (Oxford Style)
Bureau of Economic Analysis (BEA). (2025) National Income and Product Accounts, Table 3.15 – Government Expenditures by Function. Available at: https://www.bea.gov/ (Accessed 9 Oct 2025).
Congressional Budget Office (CBO). (2025) The Macroeconomic and Budgetary Effects of Federal Spending. Washington, DC: CBO. Available at: https://www.cbo.gov/ (Accessed 9 Oct 2025).
Department of Defense (DoD). (2025) FY2025 Defense Budget Overview. Washington, DC: U.S. Department of Defense. Available at: https://comptroller.defense.gov/Budget-Materials/ (Accessed 9 Oct 2025).
Federal Deposit Insurance Corporation (FDIC). (2025) Quarterly Banking Profile, Q2 2025. Washington, DC: FDIC. Available at: https://www.fdic.gov/analysis/quarterly-banking-profile/ (Accessed 9 Oct 2025).
Federal Reserve Bank of St. Louis (FRED). (2025) Business Deposits and Defense Outlays Data Series. Available at: https://fred.stlouisfed.org/ (Accessed 9 Oct 2025).
International Monetary Fund (IMF). (2023) Fiscal Multipliers and Defense Spending Dynamics. Working Paper WP/23/214. Available at: https://www.imf.org/en/Publications/WP (Accessed 9 Oct 2025).
National Bureau of Economic Research (NBER). (2024) AI Applications in Macroeconomic Forecasting. Working Paper 31788. Available at: https://www.nber.org/ (Accessed 9 Oct 2025).
RAND Corporation. (2024) Regional Economic Effects of U.S. Defense Spending. Santa Monica, CA: RAND. Available at: https://www.rand.org/ (Accessed 9 Oct 2025).
U.S. Bureau of Labor Statistics (BLS). (2025) Employment in Defense-Related Industries. Washington, DC: BLS. Available at: https://www.bls.gov/ (Accessed 9 Oct 2025).
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