Financial compliance requires banks to maintain sophisticated customer screening and transaction surveillance systems that pose data quality and data availability challenges. Effort is concentrated on data collection and data consolidation, leaving less time for higher-level analysis. NA platform leverages the power of semantic technologies in three different areas (Domain, Regulation, Process) creating a cognitive system that is aware of the controls, assessments, and approvals needed in each case, which data to integrate and share and what surveillance, monitoring and reporting to perform.
learning algorithms; and ontologies drive efficient data alignment, curation and validation processes across sources, on and off the web, to meet its data demands. Linked data provides a single unified view of the customer for analysis and enables the automation of the compliance officials’ data gathering and assessment process. The flexible design of NA platform allows for the modular implementation of various solutions. NA platform leverages the power of semantic technologies in the areas described below.
Domain ontologies; whilst FIBO provides a comprehensive abstract layer for the financial domain, using these models in a practical application at the enterprise level requires extended and more granular models. NextAngles builds on FIBO and provides in-depth ontology models of the compliance domain.
Regulation Ontologies; all compliance regulations are modeled using a combination of OWL modeling and SPARQL/SPIN rules. The combination of regulation and domain models allows the system to understand the nature of the alerts that are being generated, identify the relevant sources and extract the relevant data from those sources. The extracted data is then mapped to the existing models, thus providing an interlinked and integrated view of the data for the investigators. Semantic modelling enables a “rules to data approach” where only relevant rules are triggered, hence optimizing rule processing. Regulation models are also linked with structural models of the financial institutions enabling an agile and speedy response to any regulatory changes.
Process Ontologies; besides the domain knowledge, NA also models the investigation processes, compliance policies and controls, capturing, e.g., workflows for investigation, reporting and information sharing. This allows the creation of an automatic audit trail while increasing the cognitive capability of the overall system regarding the tasks that need to be performed and the data required to complete them. 2
NextAngles in Use – The Trade Based Anti-Money
Laundering (TBAML) Solution
TBAML is one of the solutions built on top of NA platform. Challenges lie mainly
in the process being manual, required data residing in various sources in different
formats both internal and external to the bank, lack of live data integration (i.e. ship routes)
and difficulty benchmarking market prices, specifically for non-commodities [