3 Pillar Black Swan Predictive Analytics Risk Management

Perfect Storm events are increasing in frequency and strength. Harness data through active intelligence to prepare for the future.

The Barclays team of Michael Cohen, Dane Davis, Nicholas Potter, and Warren Russell "define a commodity 'black swan' as an extreme event or dynamic that market participants, including ourselves, are not currently pricing in."

http://www.businessinsider.com/barclays-black-swans-chaos-in-2017-2017-1

When looking at exposures, it is also important to look back at historical black swan events in terms of credit, market, and operational risk impacts to financial institutions. The effects can be compounded in “perfect storms” of events that can also be engineered by bad actors/humans in terms of large scale fraud for financial gain to manipulate market outcomes. The film “Money Monster” starring George Clooney (http://www.imdb.com/title/tt2241351/plotsummary?ref_=tt_stry_pl#synopsis) tells a story of a chain reaction set of events, that cover market, credit, and operational risk, with market manipulation and outcomes created by macro political “jiggering” and election – fixing. Could an AI engineered solution combined with Blockchain and Smart Contracts prevent this type of scenario and other variations of this scenario?

The required assessment to determine systemic risk exposure will require advancements in financial innovation for the global financial system, such as blockchain with smart contracts with the convergence of AI and predictive analytics to determine exposures before they happen. The sheer interconnectedness brought about by new business models and merging of industries will require a "better mousetrap" for identifying much larger complex systemic risk across these digital financial marketplaces.

The idea here is to develop a 3 dimensional or 3 pillar (Basel based as a starting point) predictive analytics risk framework and model to cover: 1. Credit Risk 2. Market Risk 3. Operational Risk. The model will leverage data from "black swan" events such as key results and outcomes from the 2008 Financial Crisis, and mortgage industry crashes, Hurricanes such as Floyd, Katrina, and Harvey in the US, the 2011 Japan Earthquake and Tsunami, and other major natural disasters.

The model would be constructed on a blockchain using smart contracts that are representative of normal transactional and network data that show impact to credit, market, and operational risk dimensions. It could potentially be developed and built to represent impact for large public institutions (public chain) and smaller institutions (private enterprise, "behind the data center firewall" sidechains). Ideally, existing archetype network topologies based on each institution type, along with transaction types could be used to set up the blockchain structure and nodes. Then by analyzing network data and traffic for the black swan events, as compared to normal traffic and processing (as represented in the smart contracts), outliers could be identified--for example--was there an overload of abnormal transactions outside the smart contract parameters that caused error conditions and faults? This information could be found and used by AI to update contracts on the blockchain, and predict with better potential certainty the impact of black swan events.

This solution will leverage new AI and Blockchain technologies, and can be further enhanced with IoT for additional insight. It leverages convergence of these technologies, as the challenge of Black Swan event preparation is based on unknowns that we have adequately analyzed data from, to prepare better for.

The combination of technologies will allow for accelerated, deeper, and broader insight and likely also introduce through AI other considerations for preparations we did not recognize or identify yet.

Humanity and society is very quickly entering the fourth industrial revolution as seen in the picture below. Connectivity is driving rapid change in all facets of our lives for the individual, home, and organizations. However, connectivity is not enough. We need digital intelligent connectivity through networks enabled through IoT+Blockchain+AI. This convergence and confluence of technologies will help to ensure we can foresee and predict what can impact us, based on what new insights that were not seen or known from past black swan events.

I'm evangelizing this and discussing it further with my R&D organization in my company. If there is not enough interest, I'd like to introduce it to the market for further research and investment to raise funding to develop a prototype. Right now I am in the analysis, research and next level of feasibility assessment. I have had a discussion with a David Shrier- MIT professor on this solution idea and he did think there was viability here. So, I'm looking for additional focus and interest on this to build a team, raise funding and move forward with building a prototype.

If we can harness the insights from digital intelligent connectivity for the good, to better predict alternative outcomes for the individual, home and organizations, and our society as a whole, we will be accelerating our preparation for the future. I strongly feel we cannot deliver this one or even two dimensionally in terms of output--it need to start at the operational, credit and market risk levels, as these aspects and dimensions affect and can impact hundreds, thousands or even potentially millions of lives over time. We can start by looking at preserving the environment and coastlines.

AI and Blockchain using smart contracts that are representative of normal transactional and network data that show impact to credit, market, and operational risk dimensions. Further developed and built to represent impact for large public institutions (public chain) and smaller institutions (private enterprise, "behind the data center firewall" sidechains).This information could be found and used by AI to update contracts on the blockchain, and predict with better certainty the impact of black swan events.
SW/HW and appliance stack, and offered through platform as a service. Ideally hosted in a Private or Hybrid Cloud model. Revenue through Consulting  Services.

Has not been developed yet into a prototype. Currently not serving the population.

◾Central Banks (reduce global risk exposure to the financial system)
◾Regulators (proactively craft regulation and set policy that is foreward thinking)

• G-SIBs (Globally Systematically Important Banks) (reduce risk exposures, avoid repeat of the 2008 financial crisis, Savings and Loan Scandal, global financial impact due to Brexit and Trade Wars)
  ◾Hedge Funds - (price futures and hedging)

◾Credit Unions (financial inclusion smaller communities to reduce impact for individuals who live in suburban or rural areas
◾Insurers (all aspects of the insurance industry)
◾E-commerce giants - As Financial Services becomes ubiquitous, risk exposure needs to be reduced exponentially.

My LinkedIn Profile: https://www.linkedin.com/in/wgenovese

25+ year industry veteran in Financial Services, Technology, and FinTech (Huawei Technologies, IBM, KPMG, Wells Fargo, Coldwell Banker, holding various senior management roles such as CIO, CTO, Principal Advisor and Executive Architect. 

PaaS, IaaS, Cloud orchestration, Integration, Open APIs, AI, BigData, Analytics, Advanced Networking, Blockchain, distributed ledger technology/ smart contracts.

Bill has a proven track record over 24 years comprised of leadership through consulting design, and implementation of Enterprise IT system on an enterprise scale for global banking and financial markets corporations. 

 I can put together the right team very quickly.

Monetization: Developed SW/HW and appliance stack, and offered through platform as a service. Ideally hosted in a Private or Hybrid Cloud model. Revenue through Consulting  Services.

I need to raise visibility and awareness to a large organization like MIT Solve to further kick start my idea. I have attend MIT EmTech Conferences and subscribe and use MIT Technology Review extensively. I know David Shrier and Sandy Pentland. The MIT Solve program is the next best place for me to advance my idea and solution from a resource, visibility, funding and investment perspective. It starts to make it more real, and helps me to prioritize this. It's been nagging me for years to make it real. Working with MIT and Solve makes it happen!

Biggest barrier is access to historical data and creating output that is meaningful enough across operational, credit and market risk parameters for a simulation to meet the targeted customer use case scenarios. I do believe, however, MIT Solve can enable this to help identify and harness selected black swan information from multiple sources to begin modeling.

And oh yes, people are another barrier that Solve can help with too.