The AIAX Initiative was developed to address the confusion surrounding entity identification, financial reporting items and transaction mapping, and improve the information content of data. AIAX uses a evolving object-oriented structure and machine learning tools developed by Rand Labs to map companies, securities, reporting standards, and security transactions into base objects. These objects are then grouped into emerging classes based upon the similarity of their attributes and integrated into axiomatized information architectures. This structure allows for the reanalysis of data, facilitating a constant improvement in the information content of data.
Since the mid-1990's, James Claus has worked to integrate complex and diverse information sources, initially utilizing advances deployed in XML-based taxonomies. Adapting these same taxonomies with asset managers, it became evident that such "top-down formats" could not accommodate the changes in the structure of data that technology was enabling and James began a shift to an evolutionary object-oriented structures. The 2007-2008 Financial Crisis demonstrated to the world the weaknesses of the top-down approach when it took months to determine how entities were related to each other and years to finally settle all transactions. Inspired by advances in bioinformatics and climatology, the ULISSES Project® launched the AIAX initiative to develop next-generation object-handling tools using bottom-up data to address data mapping, integration, and reanalysis issues.
The bottom-up methods and tools employed by the AIAX Initiative to create its information architectures rely on the diversity of the native data structures to determine emerging classes of objects. In finance, standards and taxonomies are traditionally fixed, imposed in a top-down way by professional institutions or individual data vendors. These standards include current GAAP, XBRL, or FIX. In contrast to this "top-down" method, AIAX looks to bottom-up information obtained from the analysis of base objects' attributes, creating information architectures that anticipate rather than react to the changes in the economy and the structure of industries.
The AIAX Initiative uses machine learning built on top of the base class-based information architectures that axiomatize financial data and allow the information content of the aggregate data to be continuously improved via a "reanalysis framework." Reanalysis frameworks have been used with great success in hard sciences. While reanalysis techniques are well-known and established outside the world of finance, they are radically different than the approaches traditionally employed in finance where users look to the "single best solution" from data vendors. In contrast to single best solutions, reanalysis integrates numerous data solutions that contain "orthogonal" or unique information. In tests, the ULISSES Project® has repeatedly demonstrated that even in areas as simple as historical financial statement information, a properly specified reanalysis framework with several merely good sources produces a solution far superior to any single best solution.