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Financial technologies, or fintech, has evolved rapidly during the last decade with global network-based operations linking individuals, communities and companies to services to manage financial services and transactions. This has been made possible by the constantly falling prices of sophisticated digital devices, including hand-held mobiles, and the spread of global accessibility to the World Wide Web (W3) on the Internet backbone.

Fintech is variably described as integrating mobile payment platforms, spreading to the Internet of things, block chain applications, cryptocurrencies. Processing and management of these data flows include specialised algorithms and complex logic to take decisions and route data through what is sometimes referred to as Artificial Intelligence (AI).

Fintech is emerging as the source of funding for global economic development including Agenda 2030 Sustainable Development Goals with Green, Social and Sustainable Bonds representing vehicles capable of generating significant investment resources valued at $trillions in contrast to the global totals applied just five years ago (2005 )measured in a few hundred $billion. Fintech is therefore an important resource and this raises a large number of questions in relation to money and monetary management and national policies with regard to very large financial flows. Cryptocurrencies also add the promise of minimal transfer costs associated with real time transactions that are not routed through formal procedures controlled by financial intermediaries either private or government based. These changes in the controls over monetary flows are a concern for some monetary authorities who are in a transitional phase with respect to the relevance and possible structures of regulatory frameworks.

The digital systems-based investment decision analysis is part of the ecosystem within which different types of data are shared between devices. The effectiveness of any device in the system depends upon the quality of the data it requires to be able to process this data to generate useful information. In the field of project, programme and policy-related investments the datasets that are used to conduct audits, economic, financial, sustainability appraisals needs to be of the highest quality so that decisions on whether or not to invest are taken on the basis of reliable information. In each applications domain what constitutes critical datasets required to build the technical and quantitative relationships upon which to establish financial appraisals required vetting by experts in the applications domains in question.

This is the role of 3DP (the OQSI Due Diligence Design Procedure) which guides domain practitioners to take into account all critical factors and to give due consideration to each one. Domain practitioners can be aided by analytical tools to ensure validation of data used and provide high quality analyses according to each application domain. This helps ensure that the information and datasets used in completing a project, programme or policy design help combine lower risk with the highest probability of feasibility and beneficial outcomes.

Locational state is the specification of data elements (object properties) in terms of their location in time and space at the time of detection, observation and measurement. This approach, based on locational state theory, or LST1, is finding increasing relevance to all economic sectors in refining our understanding of cause and effect relationships that affect the operational charateristcs on all economic sectors. It therefore has a fundamental role in helping fintech operations through more transparent analysis and predictability of the outcomes of decisions so as to incur lower risk associated with decisions.

1 LST (Locational State Theory) was developed specifically for specifying more precise datasets and analyzing complex time-geographic-location-based relationships.

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