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High-debt borrowers are facing plan uncertainty (e.g., SAVE), fluctuating income (1099), and difficulty deciding between aggressive payoff vs. minimum payments plus retirement saving. The user is already building detailed repayment spreadsheets and is distressed by interest accrual, indicating recurring, high-stakes planning needs that change with policy and income swings.
Student Loan Scenario Engine
A rules-based web app that models federal student loan repayment outcomes under multiple policy scenarios and income volatility, producing a clear action plan: which loans to target, how much to pay, and what to save for taxes/retirement. It tracks borrower inputs over time and re-runs projections when policies, servicer terms, or income changes occur—without giving investment advice.
US federal student-loan borrowers with high debt-to-income ratios, especially 1099/variable-income earners, who need to make repayment decisions amid plan uncertainty.
Borrowers are currently forced into error-prone spreadsheets and forum advice to compare strategies (avalanche/snowball, minimum payments, forgiveness paths) while rules and incomes change. This product provides reproducible, auditable scenario comparisons and decision checkpoints that reduce costly mistakes (overpaying when forgiveness would apply, under-saving for taxes/retirement, or choosing a suboptimal repayment path).
Free downloadable "Debt-to-Income Stress Test" + 3-scenario quick calculator (no login) showing monthly payment ranges and interest trajectory.
$29 one-time "Repayment Strategy Report" (PDF) for one borrower profile with 3 saved scenarios.
$15–$25/month subscription for ongoing tracking, unlimited scenarios, alerts, and annual recertification checklist.
Add-on $5/month for spouse/household modeling + document vault + annual timeline reminders.
Employer/union licensing for member benefits ($5–$10/employee/month) with anonymized cohort dashboards (no individualized advice).
MVP is feasible for 1-2 engineers using deterministic amortization + configurable rule tables (no AI). Key risks are correctness and keeping policy rules current; mitigate via transparent assumptions, versioned calculations, and links to primary sources. Avoid regulated advice by focusing on math/what-if projections and user-directed choices rather than prescribing investments.
US has ~43M federal student loan borrowers; an estimated 8–12M are on or eligible for IDR plans, with several million in high DTI or variable income. A reachable niche of 1–2M high-DTI borrowers paying $15–$25/month implies a $180M–$600M/year TAM for the niche, larger if expanded to broader repayment planning.
Good baseline but limited for ongoing scenario management and personal workflow; not built for tracking decisions over months/years.
No robust variable-income seasonality modeling; limited scenario comparison; no alerts when assumptions/policies change for the user.
1099 workers; borrowers who want ongoing monitoring rather than one-off simulation.
Optimized for simple debt payoff; less suitable for federal plan nuances and forgiveness uncertainty.
Weak handling of IDR/forgiveness paths and program rule changes; limited document/checklist workflow.
Federal borrowers evaluating minimum payment vs forgiveness vs payoff under changing rules.
Flexible but requires high effort and is error-prone; users must maintain formulas and update rules manually.
No standardized policy rulesets; no automated re-projection or alerts; difficult side-by-side scenario governance.
Borrowers who already tried detailed planning but want less manual upkeep.
Win with a narrow wedge: "policy uncertainty + variable-income" scenario management, using versioned repayment rules and re-projection alerts. Build trust via transparent calculations, exportable reports, and an audit trail (what changed, when, and why) rather than generic budgeting or advice.
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