Risk Thinking in Business Forecasting: Rethinking Risk

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Risk Thinking in Business Forecasting: Rethinking Risk

Business Forecasting, as every manager knows, is essentially a guessing game. But now, in the age of IoT and AI, the ancient art of predicting the future based on past experience is being replaced by forward-looking strategies and scenarios. The magic phrase is “risk thinking”.

by Gordon Feller

Every decision we take to day affects our future. Yet, whether we are a bank, corporation, or an individual, our decision-making is primarily guided by our attempts to forecast our future. This is the case despite the fact that we know most forecasts do not actually come true. This predicament spans nearly every commercial and noncommercial sector from finance, insurance, energy, and transportation to local and federal municipalities. We know that we cannot predict the future based on past experience and until now scenario generation was a guessing game.
There is a need to replace forecasting with risk thinking and to compare risk under alternative forward-looking strategies and across institutions, stress-testing decisions under a well-defined and consistent set of scenarios. This will save money, time, resources, and potentially lives. This is especially relevant in today’s highly volatile world.

AI is particularly suited to situations where there is significant uncertainty.
Ron Dembo, Riskthinking
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This idea is not new, but it certainly was when Ron Dembo started Algorithmics (now owned by IBM) 30 years ago with a vision that a bank should be able to measure and manage its risk at an enterprise level. Stress-testing was not in the dictionary then and no banks had an enterprise risk function. He pushed for stress-testing at a system-wide level even then.

Risk Thinking: Looking Ahead

Today, it has been widely accepted that stress-testing is necessary to determine the systemic health of banks. Many regulators have adopted it in a form similar to the Federal Reserve CCAR stress tests that are carried out annually today. Unfortunately, this has not filtered down to corporations. Just witness how this could have helped GE (as well as their investors) had they tested their financing strategy.

Risk Thinking in Business Forecasting - U.S. Federal Reserve

Looking Ahead: Regulators, including the U.S. Federal Reserve, have adopted stress-testing to replace conventional forecasting methodologies.

It is also accepted by the Financial Stability Board of the G20 which has initiated a very successful initiative, the Task Force on Climate-related Financial Disclosures (TCFD), to get companies, banks, sovereign funds, fund managers, and others to measure their financial risk due to climate change. Theirs is a perfect case for stress-testing because of the complexity of measuring exposure in a setting where past data is very sparse and potentially not useful, the horizons are far out, and the impacts profound.
Royal Dutch Shell was using scenario analysis in the 1980s to help form and test their strategies and many companies tried to emulate it However, there was one fatal problem in their methodology, which could explain the poor pickup from others – the stress tests were designed by individuals in the company, with all their biases, and could not be applied systematically across industries. Without a systematic, consistent algorithm for generating scenarios, this meant it could not spread easily.

Spanning the Gap

That is where Ron Dembo has reappeared today, with an algorithm that can generate a “spanning set” of scenarios, one that covers both very good and bad events. He claims to have solved the problem of automated, consistent scenario generation in an elegant way which can be easily explained to boards and nontechnical management in a wide variety of institutions.
Dembo says his nascent company, Riskthinking.ai, will soon be offering a product that proves the concept. The power of this is that it could offer a way to compare companies within industry sectors against each other over a wide variety of consistent stress tests without any single company exposing its proprietary business secrets.

The Mood of the Market

The key missing ingredient in risk management and stress-testing is the need for an effective way to capture market sentiment, which is often a precursor to a risky event. Essentially, it’s about judging the moods of markets. Dembo’s algorithm and risk thinking methodology captures sentiment using artificial intelligence (AI). The amount of data an AI can gather worldwide and analyze using automatic, natural-language-processing translation is staggering.

Risk Thinking in Business Forecasting - Hurrican Harvey - ource ©: riskthinking.ai

Weather Report: AI is particularly suited to situations where there is signifcant uncertainty, the data is sparse, distributed worldwide, and the outcomes are potentially huge – for example, as encountered in weather forecasting and predicting climate change.

Dembo’s proposed methodology aggregates this data from all available sources and uses machine learning models to capture the sentiment in voice recordings, video, and scholarly articles from respected institutions, in order to build a picture of the risk factors that affect a specific industry. This data is analyzed and interpreted and is used to construct the data required to generate forward-looking risk scenarios. This could not have been done even a few years ago and is a result of the tremendous progress that has been made in developing deep-learning models in AI.
AI is particularly suited to situations where there is significant uncertainty, the data is sparse, distributed worldwide, and the outcomes are potentially huge – for example, as encountered in climate change and cyber risk. There is every reason to believe that these problems will be exacerbated by a very volatile climate and that risk systems will undergo significant evolution in the face of AI. Dembo says his company aims to capitalize on this.

Risk Thinking in Business Forecasting - TCFD-Status-Report

Far Horizons: Big Impact The G20’s Financial Stability Board has initiated a Task Force on Climate-related Financial Disclosures (TCFD) which provides a perfect case for stress-testing of the complexity of the financial risk due to climate change (click to resize).

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