ARTHASETU 2.0 is an adaptive financial inclusion platform built for the BUILD BANK 2026, IIT DELHI (Track 1: Financial Inclusion for the Underbanked). It addresses the challenge of 300+ million credit-invisible gig workers in India who lack traditional credit histories — despite having verifiable trust signals like rental payments, medical expenses, and bill payment histories. The platform uses a statistical user-profiling engine that dynamically adapts interface, guidance, and pacing for first-time financial users. It combines machine learning (XGBoost, 99.5% accuracy) with 10-layer post-quantum security (ZKP, FHE, PQC) to build trust while protecting user data.

The analysis encompasses 10,000 geo-spatial observations across India's complete geographic extent (8°N to 37°N latitude, 68°E to 97°E longitude). A multi-factor flood risk model incorporating rainfall intensity, river discharge, elevation, water levels, and population density was developed and achieved 96.7% accuracy when validated against 5,057 historical flood occurrences.

SpectraBDH is an interactive, sub-16ms vector explainer built for the DataForge 2026 Pathway Track.It proves why classical State Space Models suffer exponential associative forgetting ($ ho(A)^L$) and visualizes how Pathway's Dragon Hatchling (BDH) synaptic plasticity and BDH CQ continuous latent reasoning eliminate interference without KV-cache explosion.