$1 Trillion Impact from Artificial Intelligence on Financial Services -- via Autonomous ?NEXT

Hi fellow futurists -- this week is special. We have just finished up a year-long dive into artificial intelligence and its impact on financial services. 

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Here's what we learned:

$1 Trillion in Exposure from Artificial Intelligence on Finance.  

We looked at the applications of AI across the front, middle and back offices of banks, investment managers and insurance companies, highlighting a rich ecosystem of sophisticated software. The outcome is Augmented Finance -- an investor’s guide to how AI is pulling apart and breaking down the financial services industry. We estimate the economic impact of AI on financial firms globally, finding nearly 20% of costs potentially reduced through implementations, equivalent to $1 trillion by 2030.

AI is not a panacea nor a single thing. It's math, data and software, searching for the right use case. In this dive, we looked at conversational interfaces, biometrics, workflow and compliance automation, and product manufacturing in lending, investments and insurance. In the front office, the most promising applications focus on integrating financial data and account actions with software agents that can hold conversations with clients, as well as support staff. In the middle office, as regulations become more complex and processes trend towards real-time, artificially intelligent oversight, risk-management and KYC systems can become very valuable. And in product manufacturing, we see AI used to determine credit risk using new types of data (e.g., social media, free text fields), take insurance underwriting risk and assess claims damage using machine vision (e.g., broken windshield), and select investments based on alternative data combined with human judgment.

In the US alone, 2.5 million financial services employees are exposed to AI technologies. There is potential cost savings of $490 billion in front office, $350 billion in middle office, $200 billion in back office, totaling $1 trillion across banking, investment management and insurance. Not surprisingly, many firms talk about AI, but very few actually hold intellectual property in the space. And the best performer -- Bank of America -- is still leagues behind the GAFA. Talk about Black Swan risk!

In mapping out the future of AI in financial services, we saw several routes. One potential path is that AI tech companies like Amazon and Google continue to add skills to their smart home assistants, with Amazon Alexa sporting over 20,000 skills already, outcompeting finance companies and stealing their clients. Another potential path is the example of China, where tech and finance merge (e.g., Tencent, Ant Financial) to build full psychographic profiles of customers across social, commercial, personal and financial data. And last, but increasingly tangible, is the path is towards decentralized autonomous organizations that are built by the crypto community to shift power back to the individual, with skills made from open source component parts. 

Source: Autonomous NEXT

Will AI Be Exponential, Creative and Emotional? 

To answer this question, we looked at the fundamental drivers of the space to put together Machine Intelligence. The work is a primer that explains how artificial intelligence works, what is motivating the revolution in neural networks, provides examples of technology and top players, and highlights the potential future of the software. If you're yet not a client of the firm, we've combined the primer and financial services analysis into a single comprehensive document for free download. If you are a client of our research, then we offer two separate analyses that go even further than the public version.

One key takeaway is that the science behind today's software is haalf a century old, seeing several peaks and valleys of excitement, investments, and disillusionment. We've been here before, but not with the needed hardware (there are 20 billion smart devices) and software to make it work (cloud computing is a $100B market). Many of the math concepts underlying current advancements come from prior academic work, powered by the massive computing power and data sets with millions of data points across various types of human activity. All courtesy of the web.

Further, designing software by automating a process top-down is fundamentally different from using machine learning and neural networks, which create probabilistic models that change in response to new data. What is most surprising is just how creative the outcomes can feel. In this way, AI can also be used in a creative capacity to explore a space of ideas quickly or to do emotional tasks.

The growth and potential of Artificial Intelligence is a massive challenge for the traditional economy, and its development is likely to only accelerate. There are several sources of exponential growth: open academic archives, open source code, some form of Moore's law, increasing interest from students in AI, and ample venture capital. But it is important to be grounded -- today’s narrow Artificial Intelligence is not a panacea and does not have general reasoning capacity. Still, there are many practical applications of automated human judgment. And those applications are subject to myriad ethical and existentialconcerns with which we must engage before it is too late.

Source: Autonomous NEXT

  Uncanny Examples of AI applications. 

This edition is heavy enough, so we want to highlight a few fun and unexpected outcomes of AI technology. The first is a textbook example of how to manipulate public opinion, get celebrities to talk about ICOs and stocks publicly, and power propaganda bots. Just fake a video! In this example, actor Jordan Peele provides a vocal track of his impersonation of Barack Obama, saying various outlandish things. A deep fake AI does the rest.

What does that actually mean? First, hours upon hours of footage of Obama were fed into a machine learning algorithm, which is taught to recognize how sound data correlates with visual data. Next, the process is reversed to generate images instead, with the original algorithm acting as a gatekeeper to assess whether the generated image is good enough to pass for Obama. And with a bit of magic dust for interpolation, we have the uncanny outcome where we can make anyone say anything that we want.

Another odd example is this -- a dog was wired up with various sensors that used machine vision to its daily activity. From this data, researchers were able to isolate the dog's ability to choose on which surfaces to walk. This involves quite a bit of judgment, understanding whether the path is too high or uncomfortable. After this experiment, an AI has a statistical layer that represents a dog's pathfinding in the world based on visual data. Perhaps we'll see this making its way into a Boston dynamics death critter.

How do we translate this back to financial services? The short term answer is this -- anything that humans do in a rote manner, where the task is a result of human intuition of reasoning but has a fairly stable decision process, can be done by machine learning. Full stop. 

Source: The Verge

Thanks for reading!

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