We built an admissions+credit decisioning stack that predicts admit likelihood, benchmarks university ROI, and powers an upskilling-loan platform—so lenders can fund potential, not just past credit.
Admit Predictor, University Comparison Engine, and Upskilling Loan Platform powered by ML on millions of data points
Eligibility now includes academic potential and employability signals—improving approvals without added risk
Decisions in hours (not days), 40% portfolio growth in first quarter, expanded access in tier-2/3 segments
Traditional scoring underweighted student merit; processes were fragmented across admissions, credit, and disbursal; and ops lacked real-time views into placement and salary outcomes for ROI.
Predict admission & eligibility with machine learning models.
Compare universities on 40+ years of outcomes data.
Launch an upskilling-loan platform with employability-aware scoring.
Cut decision time while holding credit risk flat.
Admissions, ranking datasets, placements, credit bureau, internal applicants
Logistic regression, gradient boosting, ensembles for admit probability + default risk
Low-code underwriting & eligibility (auditable, business-editable)
REST services for ops CRM and student dashboards
Modular microservices, scalable inference & reporting on AWS
Manual reviews of thousands of profiles per intake
ML-ranked shortlists + admit probability; faster, consistent triage
Narrow financial signals; slow, opaque decisions
Employability-aware scoring + rule packs with versioning and logs
Little transparency on program ROI
Outcomes-based comparisons (placements, salaries) informing both loans and choices
Approvals from days to hours via automation
Higher accuracy vs. manual counselor predictions
Upskilling-loan growth in first rollout quarter
Higher penetration in tier-2/3 regions at stable risk
Models and rules look at talent and employability, not only legacy credit
Auditable, low-touch underwriting accelerates disbursal
Real-time insights unlock new programs and geographies
Download the detailed upskilling loan case study with architecture diagrams, models, and underwriting playbooks.
All metrics and results presented are anonymized and represent aggregated outcomes across engagements in the education technology and financial services domains.