Merchant Onboarding and Risk Scoring: Data Governance, Master Data, and Golden-Record Strategies. Below the Content is Description
DOI:
https://doi.org/10.63397/ISCSITR-IJSRIT_04_01_002Keywords:
Golden Record, Master Data Management, KYC/AML, Entity Resolution, Graph analytics, data governance, Beneficial ownershipAbstract
Financial institutions involved in merchant onboarding relationships entail intricate data environments that include Know Your Customer (KYC), Anti-Money laundering (AML), underwriting, Merchant Category Code (MCC) categorization and beneficial ownership information. These systems have fragmented records that usually result in the creation of duplicate entities and inconsistent risk assessment and a long turnaround time (TAT) when onboarding. This paper suggests a data governance model based on golden records to centralize onboarding and risk scoring processes with the help of graph support entity matching, survivorship rules, and feature-based risk modelling. The offered solution will combine structured and semi-structured materials in order to form one, authoritative perspective on every merchant and probabilistic linkage and graph analytics to define the latent ownership relations and conductive relations. The major risk characteristics- which are transaction velocity, geography, and BIN/ device finger prints are designed in a manner that they optimize early-life fraud detection and scoring precision. It has been proven in experimental assessment using anonymized merchant data that, onboarding TAT has decreased by 50 percent, early-life fraud by 57 percent, and volumes of manual review have decreased by more than 59 percent, relative to traditional, rule-based systems. The results explain the role of master data cum entity graph fusion that can significantly enhance the operational efficiency, compliance accuracy and data quality. The proposed framework creates a template of data-driven risk governance and automation of intelligent onboarding to the world of fintech and payments.
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