Predictive Risk Analytics in Banking Using Blockchain-Validated Translational And Data AI

Abstract

Being an era of unforeseen financial cottonwool, risk management has thus become a pillar of stability and competitiveness in banking. On one side, worldwide digital-transaction growth-oriented customer behavior poses its own unique aspects, while, on the dark-and-sinister side, evolving fraud methods demand banks' transitioning from old static insulted bank rules modeling on risk assessment to a smart system that learns dynamically. This budding AI, birthed in predictive analytics, thus invites newer vulnerabilities, such as those regarding the reliability of the data, the inability to explain models, and the inability to comply with regulations. While working infinitely well, these models have gone, quite metaphorically, and have been touted as "black-box" models, trained on datasets whose very provenance is questionable, opening grounds for questioning trust, bias, and auditability.
Given the multidimensional nature of the problems at hand, this paper puts forth a new hybrid framework comprising blockchain-validated data pipelines and a bi-layered AI system consisting of data-centric and translational AI modules. This architecture aims to provide secured, explainable, and adaptively predictive risk assessment in its very design, catering to high-stake banking environments. Blockchain is considered as a decentralized trust infrastructure that guarantees the immutability, timestamping, and cryptographic verification of financial datasets (customer transaction logs, loan portfolios, or KYC documents), providing verified data input, protection against any form of hacking, and hence an audit-worthy AI pipeline.
On approval, the financial datasets undergo scrutiny in the data AI layer for credit risk patterns, early signs of fraud, and financial stress signals through time-series neural networks with LSTM and Transformer models. The translational AI layer further aids generalization of these insights over banking units and customer cohorts by transferring the acquired knowledge to new domains without requiring retraining, striving toward a unified risk measure over regions and financial products. The intelligence pipeline is benchmarked using several datasets, including opensource credit default datasets, simulated transactional logs, and simulated blockchain-audited trails of transactions.
The experimental results showed a statistically considerable gain in predictive capability, where blockchain-validated models showed up to 6.5% higher F1-score and 7.2% higher AUC score than their non-validated counterparts for risk types. Moreover, the system's fraud detection latency remains under a satisfactory 1.5 seconds while maintaining traceability of the data. The application of blockchain also boosts trust in the AI predictions while simplifying compliance reporting under Basel III, GDPR, and the FATF AML framework. Most importantly, the hybrid approach supports explainable risk scores and traceable decision paths, which are necessary in upholding consumer trust and institutional accountability.
In essence, the study presents a scalable, secure, and regulator-oriented architecture for AI-powered risk assessment in financial services. Further extensions intend to investigate the possibilities of incorporating privacy-preserving technologies such as zero-knowledge proofs and federated learning, that would allow cross-bank collaborations without violating data sovereignty agreements and/or customer confidentiality.