Lakehouse-Integrated Graph Risk Scoring Architectures for Advanced Fraud Detection
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V7I6P107Keywords:
Fraud Detection, Lakehouse Architecture, Graph Analytics, Risk Scoring, Financial Crime, Network-Based Fraud, Big Data Analytics, Machine Learning, Transaction GraphsAbstract
The high rate of online transactions, real-time payment systems and interconnected financial ecosystems have, therefore, made fraud detection one of the most serious analytical issues faced in contemporary digital economies. Old methods of fraud detection which were mostly dependent on rule engines and isolated machine learning classifiers cannot rule out scale and velocity of fraud and more importantly the relationship complexity that are involved in modern day fraud trends. The frauds are reported to have started to appear as organized actions within multiple networks of users, devices, accounts, merchants, and transactions and no longer a single record analysis is enough.Simultaneously, lakehouse platforms are changing the nature of enterprise data architectures by integrating data lake scalability with the affordability with the governance, dependability and data warehouse level performance guarantees. Whereas lakehouses are well suited to handling a large sized structured and semi-structured data, they are not necessarily optimized to support graph-based reasoning that is critical in the detection of collusive and network-based fraud. This is the gap that has led to the combination of graph analytics and lakehouse architectures allowing scalable, interpretable and real-time fraud intelligence.In this paper, it is suggested to come up with Lakehouse-Integrated Graph Risk Scoring Architecture (LIGRSA) as a sophisticated way of detecting fraud. The proposed architecture will unify transactional information used in a lakehouse with dynamic graph construction, graph features extraction, and hybrid risk scoring models which are graph metrics combined with machine learning and statistical inference. The architecture allows batch and streaming fraud detection pipelines, which allows propagation of risks between entity networks in near-real time. Graph based risk aggregation and composite fraud scoring mathematical formulations are introduced, scalability, system level design, governance and explainability considerations.The experimental findings, such as representative workloads of financial transactions, prove that lakehouse-integrated graph risk scoring outperforms other models in terms of fraud detection accuracy, early fraud detection, and false positive reduction. In the conclusion, the paper addresses elements of deployment, constraints and future research opportunities, such as real-time graph learning, privacy-preserving analytics, and large language model (LLM)-aided fraud investigation
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