ApolloRisk

Felonious machinations and human errors have exceedingly dire business consequences.
ENRON, Mike Leeson, Siemens or Societe’ Generale are prime examples for the increasing impact on businesses world-wide.

At the same time, recognizing and preventing such disasters becomes increasingly difficult. The problem is that in each of these cases the pattern of behavior was previously unknown and therefore undetectable. Monitoring for established patterns would not have prevented any of these cases.

An automated system for the early detection and prevention of such cases would lead on the one hand to the avoidance of economic losses. It would on the other hand enable top executives to make sure that they cannot be held liable for the consequences of actions that are beyond their control. ApolloRisk is the solution for exactly this problem.


Apollo Detects Qualitative Anomalies

All of the cases that came to light have exactly one thing in common: the behavior of certain employees deviated from the norm; the problem is that it was not discernible which patterns follows in which time sequence and which details deviate. There was no way to determine which deviations to search for!

Apollo is a next-generation artificial intelligence application that for the first time empowers you to identify such deviations even in highly complex environments.

Apollo does not need structured information about the qualitative analysis to be performed, it uses a reverse approach: Apollo recognizes (“learns”) behavior patterns and tasks of a role (e.g. a procurement manager). Apollo then automatically extracts relevant criteria and characteristics, and uses this information to analyze the working pattern of this same person, or of another person with the same or similar role, for significant deviations. Taking the banking scandal of the Societe’ General as example, Apollo would have detected this admittedly ingenious exploitation of chinks in the security system and would have classified and flagged it as relevant.

This ability to automatically extract and evaluate patterns from workflows is based on a very sophisticated and unique technology. More than 250 man-years and more than 20 million Euros of interdisciplinary development went into this patented technology; it uses its own descriptive language and is therefore practically copy-proof.

Apollo can incorporate both quantitative data like accounting or statistics and qualitative data like online behavior, type of processed documents or their contents; the amount of data is not limited. To use the full range of Apollo’s capabilities, all relevant data of a targeted employee should be made available. This might include all documents, transactions, use of the internet, business decisions and other electronically available information down to business trips and vacations.

If desired (e.g. because conspicuous behavior was detected), a voice analysis and also an analysis of gestures and facial expressions can be included as the second step.

Implementation

Apollo does not modify any files or other IT data or processes, it operates one level higher and uses compressed copies of extracted data (interfaces to existing servers and applications may have to be built).

ApolloRisk runs permanently in monitoring mode and does an online analysis and evaluation of live activities. The invaluable advantage is that pattern disruptions or deviations are identified in real-time, and are flagged and brought to notice instantly.

ApolloRisk identifies even seemingly erratic and ingenious circumventing strategies and flags these for further analysis. Apollo makes use of the fact that any new and “creative” exploitation of gaps in the security systems is in itself an unusual behavior and therefore identifiable as deviating from the previously monitored patterns of behavior. Only relevant cases are brought to the supervisor’s attention – Apollo recognizes non-relevant pattern changes automatically.

Due to its learning capabilities, Apollo is always up-to-date. Regular changes and improvements in business processes are recognized as such. Although generally not necessary, additional training cycles can easily be added manually; these run very quickly.

Personal rights: The complete process can be implemented without violating any personal rights. The collected and analyzed data is only passed on to an authorized entity if suspicious deviations are detected. This ensured that unauthorized persons authorized cannot access the data and cannot use this information to backtrack to the individual in question. Authorized entities can be supervised in turn to avoid abuse.

Perspective

superWiseTechnologies AG intends to develop the world-wide market of corporate compliance solutions intensively. One big advantage of the approach of layering Apollo on top of the existing IT infrastructure and software architecture instead of modifying it isthat neither operating departments nor the Information Services department will have any grounds for objections to implementing Apollo as there is no additional risk.

As numerous meetings and discussions with corporate compliance officers across all branches  showed, there is a high demand for such tools. The risks for top executives to be held liable for manager’s or other personnel’s misconduct is definitely on the rise. So what possible reasons are there for a chairman of the board or a board of directors not to acquire such a tool? This investment can be seen as a sort of risk insurance; the license fees are adjusted accordingly.

Extensive world-wide market research showed that Apollo is unique on the market.