Risk awareness is critical for ensuring successful business results, and regardless of whether risk-averse or not, enterprises strive to identify, manage and mitigate all risks associated with the various entities they encounter.
While judging the risk associated with an entity based on profiling may provide statistically viable outcomes, in various situations it results in unwanted false-positive and false-negative results, which business impact may be unbearable.
True risk needs to take into account the ability to dynamically define what is an entity of interest, how it relates to other entities, and more importantly, how do these entities impact the risk associated with the original entity. For example, John Smith may be a perfectly legitimate business person, but being the son in law of a notorious Mafia boss, is likely to significantly increase his indirect risk. A bank may want to be informed of such a relationship, so guaranties can be used to mitigate that risk.
ELEMENT™ of Risk was designed to account for multiple dimensions of risk. Examples of common risk dimensions include the direct risk associated with an entity based on its profile (metadata), its relations to other relevant entities (depending on degrees of separation and relationship strength, etc.) and the behaviour of each entity (e.g. historical financial activities, international travel).
Facts processing can consider the context (age may have relevance for a mortgage but not for investment), temporal aspects (divorcee reputation may be considered less relevant 5 years after separation), geographical aspects (rapid changes in location may indicate undesired volatility), corporate bias (how the enterprise perceives the risk associated with each of the collected facts), absence of information and more.
ELEMENT™ of Risk supports:
- Dynamically defining what entities to monitor (people, organizations, assets, structures, accounts, etc.)
- Identifying and quantifying direct risk of any monitored entity (customer, supplier, individuals, payment instruments, etc.) using customer specific weighted knowledge graphs
- Detecting reported and unreported relations between entities, quantifying their quality, and analyzing their indirect risk implications on monitored entities
- Identifying clusters and centrality of entities in networks, and analyzing their aggregated risk impact
- Analyzing risk associated with specific transactions and events, qualifying the aggregated risk of each entity, network, transaction, event, or process according to the organization policies, automating risk
- Recommending policies for risk avoidance, mitigation or management, and promoting offerings to address them. and more.
This forensic accounting application allows tracing assets hidden in piles of data, and supports their recovery
A complete framework to support the identification, preservation, collection, processing, review, analysis and production of digital data in order to sustain mission critical business processes.
This application covers the entire underwriting process from identifying and quantifying the risks and exposure associated with potential clients, through negotiating with the customer on coverage cost, to analyzing complete markets, and beyond