Privacy-Preserving Blockchain-Integrated AI Architecture for Adaptive Risk Assessment in Next-Generation FinTech Systems

Even as financial tech moves ahead, systems built to measure danger keep tripping over twin hurdles: guarding personal information without slowing down choices. Outlined here is a de- sign with levels — mixing unchangeable ledgers via blockchain and self-tuning algorithms powered by AI, both cloaked in methods like shared training and noise-based masking to pro- tect identities. Known as PPBIA, which stands for Privacy-Preserving Blockchain-Integrated AI, it divides duties into four stages: gathering inputs with secure digital fingerprints stored on- line, pulling out traits without exposing raw facts, adjusting scores through evolving logic, then saving every move in an untouchable record trail. Trials ran inside a made-up money-handling setup, processing 125,000 fake trades shaped after actual patterns seen during fraud tests. Right after testing against centralised AI, blockchain-only checks, and plain federated learning, PP- BIA reached 96.3% on catching fraud. False alarms dropped by 38.7%. Transaction delays never crossed 1.8 seconds. Leakage of private details stayed beneath 0.03 epsilon, matching strict privacy rules. Under load, performance held firm until hitting 3,200 transactions each second. Then things began slipping. Blending secure ledgers with smart models focused on secrecy didn’t only guard information — decisions around danger zones got clearer. Num- bers like these matter deeply to financial firms, money transfer systems, oversight bodies, plus coders shaping tools where trust meets precision.