Rethinking Risk with Automated Blockchain Macroeconomic Indicators

Progress is underway to investigate and migrate many different parts of the banking, securities, and insurance industries to public and private blockchains. These operations include settlement and clearing, smart property digital asset registration and transfer of stocks, bonds, derivatives, private equity, and other instruments, and the structuring of more predictable insurance payouts. One next step is articulating how blockchains might be used more broadly across industries and economies for automated risk management and macroeconomic indicator generation. This could help meet the need for real-time knowledge about the health of financial systems, especially given their interdependence and global nature.

Automatic Macroeconomic Indicators
The pseudonymous property of blockchains could be a valuable parameter of transaction data structures that automatically relay meta-data as a semi-private input to large-scale open risk models [1] at the entity and macroeconomic level. Risk measurement and macroeconomic indicators could thus be produced automatically in real-time with tremendous aggregate transparency. The functionality could be built into fintech blockchains as a standard, with other organizations (like smart contract DAOs) to blend the data into macroeconomic statistics. Fintech standards bodies analogous to IEEE working groups could recommend protocols. Transaction meta-data aggregation could also engender a new class of economic indicators granularly measuring sophisticated parameters such as interlinkage, complexity, value-at-risk, and country-level inflows/outflows, and prediction markets and derivatives could run over these.

Hayekian Market Signaling
For automatically generated macroeconomic indicators, there would need to be a willingness to disclose exposure, whether pseudonymous or not, and whether on public or private blockchains. This could be compelled by regulatory entities, or better, volunteered as a market-signaling technique, just as the smart contract industry may fork into legally-compliant and a-compliant contracts. Prediction markets could be a further layer to elicit anonymously-voted opinions regarding data quality. This could facilitate the concept of markets as discovery in the Hayekian competitive currencies model and address systemic collusive tendencies and the predictive avoidance of collapses.

Immanence Philosophy of Risk
One effect of having granular, precise, real-time automated economic indicators and risk measurement systems is that it could enable more fundamentally our definition of risk to shift. As traditionally conceived, we have what is conceptually and emotionally a scarcity relationship with risk. Risk is something to measure, avoid, manage, and control, as exemplified by traditional finance and insurance models. There is the begrudging position of ‘no risk, no reward’ and ‘nothing ventured nothing gained,’ but this view is conceived in the scarcity of trade-offs, not in the abundance of making new bigger spaces for opportunity. Instead, risk could be reconceptualized as ‘taking a step,’ taking a step into an unknown of immanence, from an unknown yet supported downside and into a completely open upside. Immanence risk models could be realized through societal shared trust and the willingness to share information in comfortable ways to create the underlying layer supporting the open upside. A concrete example of this could be deploying open source FICO scores and decentralized credit bureaus with blockchain-based reputation structures where the global shared information trust model facilitates the local open upside possibility.

[1] Open Source Risk Model resources:
Hwang, J.H. Proposal for an Open-Source Financial Risk Model
Papadopoulos, P. OpenRisk.eu, Open Risk API (White Paper, Github)