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INVESTING
September 26, 2019

Smart Machines—A Strong Fit for the Municipal Industry

By Vidhu Aggarwal

Technology is revolutionizing public finance and the municipal business by reshaping the way fiscal policies and municipal projects are designed and implemented. The municipal market is increasingly seeking out new sources of revenue—from technologically creative and cost-effective infrastructure designs to using natural language processing (NLP) and artificial intelligence (AI), both of which are proving quite beneficial for the industry.

Sifting Through Muni Debt Data and Info

With 65,000 issuers of municipal debt, monitoring the mainstream news for relevant updates is simply not sufficient. There’s a real limit on the human ability to read, digest and respond to information when trying to surveil a market of this size. To address this situation, the municipal market is now using AI technology called augmented intelligence, whereby NLP and AI tools scan countless sources of issuer information, official statements, news and alerts, and perform other such preliminary investigative work. To some extent, AI can be taught to think and search like a human, and when paired with an actual human counterpart, such AI technology can become quite efficient in seeking out the most relevant information to make better-informed decisions. Given the idiosyncratic, fragmented nature of the municipal bond market, these technologies are yielding even more benefit to municipal bond investors and issuers than they are assisting investors of other kinds. In particular, the potential to monitor holdings and flag surveillance is allowing asset managers to proactively reallocate and increase diversification, thereby pointing them to new directions in search of alpha.

Leveraging Available AI Tools

Traditionally, most muni investment professionals fall into the every-person-does-their-own-research models. Leveraging available AI services offered by some sophisticated third party vendors can now replace individual researchers with computers trained to detect hundreds of problems impacting general obligation and revenue bonds (with the intelligence to understand, for example, what impacts a hospital versus other sectors), and that are smart enough to discern the difference between short-term supply/demand dynamics and credit fundamental issues.

AI for Muni Projects and Bond Investors

Infrastructure projects are gaining immensely by embracing AI. Infrastructure developers are using 3D modeling and virtual reality to test new systems before construction to find cost-saving and more reliable design-build processes. As an example, some conventional water infrastructure projects that traditionally have posted a significant municipal energy cost are now using innovative, efficient, smaller-scale water treatment designs to create safe, affordable and resilient water and sanitation services.

Technological innovations are allowing global access to rich troves of available economic and government data. AI models can glean interconnections between a variety of metrics to develop strategies that give investors exposure to specific risk and return factors, based on their appetite. The performance of municipal securities can often be driven by technicals, influenced more by fund flows than credit fundamentals. As AI expertise expands and quality data sets proliferate, asset managers can create robust models to predict near- and long-term demand for municipal securities, size portfolios accordingly and make appropriate investment decisions.

Smart Cities Drive Efficiency

In addition, due to advances in fiber network technology, a trend of smart cities is rapidly emerging. These projects are addressing and improving core municipal functions: energy and resource efficiency, governance, healthcare, mobility and transportation, and safety and security. Newly devised concepts such as congestion pricing benefit citizens by improving transit speeds and reliability, and they help state and local governments by providing additional revenues for funding transportation and by retaining businesses and expanding the tax base. As an example, the Minnesota Pollution Control Agency is using AI for weather forecasting and Vermont is using AI to predict likely road repairs. Atlanta launched a smart corridor that uses sensors, adaptive signal timing and vehicle-to-infrastructure communication, which collectively have resulted in an accident reduction rate of 25%. To fund these initiatives, governments are relying on the municipal market as a source of financing, and since these projects result in positive benefits for residents, it makes these municipal bond issues inherently stronger credits.

Addressing AI Challenges

This technological revolution comes with its own challenges, including high initial capital expenditures, need for a particularly skilled workforce and disruption to already functioning processes. Further, such revolutionary technology offers no substitute for the basics of getting procedures and operations right, and has intensified concerns about privacy, confidentiality and cybersecurity. We have seen multiple cyberattacks into government systems, which give us a frightening glimpse of the vulnerabilities of some of the technologies described here. Further, it takes a significant amount of time and resources to learn and develop these systems and once in regular use, errors can go undetected for a long time.

Both the positive and negative effects of this technological revolution are likely to be profound, and will require focusing on solutions that address the most pressing priorities while taking steps to avoid the pitfalls. At a minimum, anyone who employs such technology will have to be adequately prepared to convey exactly how and where it is being used to secure trust and reduce skepticism.

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