Oracle Financial Services Investigation Hub

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Oracle Financial Services Crime and Compliance Investigation Hub turbocharges financial crime investigations for financial institutions using graph technology. It links customers, accounts, external entities, transactions, and external data, to provide a holistic graph representation of data that aids uncovering of any otherwise hidden suspicious patterns. Investigators can simply click their way through the entities and theirs connections, represented as nodes on the graph model, to analyze networks and suspicious activities

Key Features

  • Pre-built user interfaces for case investigation, special and adhoc investigations and sanctions
  • Inbuilt correlations and scoring algorithms that provide consolidated risk score for any focal entity, evaluating transactions, accounts, related parties
  • Exploration of the financial crimes global-graph using an interactive and visual graph explorer tool
  • Configurable red flags and risk factors to highlight key areas for investigation
  • Case summary in narrative format and case recommendation
  • Built on Oracle Financial Service Crime and Compliance Studio which includes a highly scalable in-memory Oracle Graph Analytics Engine (PGX), AI and machine learning
  • Utilizes proven Enterprise Financial Crimes Graph model which accelerates financial crime investigation use cases
  • Readily usable across the enterprise financial crimes data lake
  • Integrates fully with Oracle Financial Crimes Application Data and external data sources such as watchlist and company hierarchy data

Key Benefits

  • 360 degree of a customer, external entity or account for a holistic view of alert, transactions and external data of interest. Richer, more contextual data on cases and case focal entities
  • Eliminates manual laborious collation of information from disparate sources for ad hoc investigations. Immediately search full Enterprise Financial Crime Graph model for persons or entities of interest.
  • Increased investigator productivity by recommending focus areas based on risk and historic decisions
  • Accelerated investigations by aggregating relevant data and automatically generating case narrative and insights
  • Fuzzy matching and transliteration to identify links within the graph model and with external sources of data such as watchlist information, company hierarchy and beneficial owner details and external investigations.

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