Design a Study-Abroad ROI and Education-Loan Payback Tool for Indian Students

A recent engineering graduate in India holds a B.Tech in information technology from an ordinary, non-premier college and has a job paying INR 4 lakh. He wants to do a master's degree abroad in computer science, AI, data science, finance or management, but several things worry him: - whether he will find work abroad, and how long repaying an education loan would take after the degree; - which country offers the best prospects of a higher salary; - whether a foreign degree still makes a difference if he comes back to India after about two years and joins an Indian company. He wants to understand how studying in each of these destinations would change his earning potential, and by how much: the USA, the UK, Ireland, Germany, Canada and Australia. Suppose he borrows INR Y for the degree, for example INR 20 lakh, at an annual interest rate of 12%, give or take one percentage point. If things go normally and he lands the kind of job he is aiming for abroad, when would he have repaid the loan and broken even while keeping a normal standard of living there? Any answer has to take three groups of factors into account: - salary statistics for the target college, country and field, as published by salary-report websites and professional networks; - loan amounts, interest rates and repayment terms from Indian banks, non-bank lenders and international education-loan providers; - living expenses and taxes for the target city, region and country. You are asked to work out the calculation and to design a framework from which developers can build a tool. For every course in the USA, the UK, Canada, Ireland, Australia and Germany, the tool should compute the earning-potential difference and the loan payback period, so that students can choose where and what to study. Its output should also give banks more confidence when they approve education loans. The product launches for Indian students but needs infrastructure that supports expansion to other countries later. ### Clarifying Questions - Is the INR 4 lakh salary a yearly figure, and should the stay-in-India comparison assume it grows? - Does interest build up while he studies, and does repayment start right after the course or after a grace period? - Do the "two years" before a possible return to India count from the start of the course or from his first job abroad? - Who is the primary customer for the first release: students deciding where to study, or lenders deciding whether to approve a loan? ### Part 1 — The payback and breakeven calculation Lay out the calculation that turns a course choice into a loan payback period, a breakeven point and an earning-potential difference compared with staying in his current job. Work it through for the INR 20 lakh loan, show how the 11% to 13% rate range moves the answer, and show what changes if he returns to India after two years. ```hint What sets the clock Two quantities drive almost everything: how large the balance has grown by the time his first salary arrives, and how much he can put toward it each month after taxes and living costs. ``` #### What This Part Should Cover - A payback formula whose inputs are each named, with where each value would come from - Interest that builds up before repayment begins, the rate band, and a check that repayments exceed the interest - A breakeven and earning-gap comparison against a stay-in-India baseline, including the return-after-two-years path - An illustrative worked example with every number labeled as an assumption ### Part 2 — The blueprint for developers Describe what the developers should build: the data the tool needs and where it comes from, the data model, the calculation engine, how results and their uncertainty are shown to students and to banks, and how the system takes on new countries and currencies later. ```hint Plan for missing data Salary figures for one specific course at one specific college are often thin or missing. Decide in advance what the tool falls back to, and how it tells the user that it did. ``` #### What This Part Should Cover - Data sources, refresh cadence and fallbacks when detailed data is thin - A calculation engine that is reproducible and auditable for lenders - Separate student and lender experiences, and a design that adds destinations and home countries without a rewrite ### Part 3 — The A/B test strategy Design the experiments that show whether the tool is working for students and for banks. ```hint Decide what may vary Ask which parts of the product can safely differ between test groups, and whether a short experiment can tell you if the estimates are accurate at all. ``` #### What This Part Should Cover - What is randomized, at what unit, and what must stay identical across arms - Primary metrics and guardrails for students and for lenders, with a sample-size estimate - How the accuracy of the estimates is validated beyond A/B tests ### Part 4 — The scale-up roadmap Lay out how the product grows from its first release to global coverage. ```hint Expand along two axes Growth can mean more destinations and courses for Indian students, or students from new home countries. Each direction needs different data and partners. ``` #### What This Part Should Cover - Phases with their scope and exit criteria - The data, partner and infrastructure work each phase depends on ### What a Strong Answer Covers - Correct, transparent arithmetic, with assumptions labeled and every input traceable to a source - Honest treatment of uncertainty: ranges instead of a single promise, and the stress cases a lender would ask for - A fair comparison across countries that accounts for taxes, living costs, course length, tuition and post-study work rules, not gross salaries alone - Trust: data freshness, privacy and consent before sharing with a lender, and no steering toward a particular lender - A design that treats the home country and currency as configuration, so global expansion does not need a rebuild ### Follow-up Questions - A partner bank wants to use the tool's payback estimate as an approval cutoff. What safeguards would you require? - Rules for working after graduation suddenly change in one destination. How does the tool absorb that, and what do students who already ran estimates see? - How would you collect real outcomes from graduates to check and improve the estimates over time?