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What’s the Real Promise of Artificial Intelligence (AI)?

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In our blog of December 7, 2017, we asked the question, “How long do you believe it will be before AI will impact your business model and strategy?”  With options of less than 2 years, 2-5 years, 5-10 years, and greater than 10 years, the responses were heavily weighted toward the shorter end of around 2 years.  Also of interest were the comments, which touched on a range of topics, from the number of jobs that will be lost, to how far down the road it will be before we see artificial intelligence (AI) taking over high-level tasks.

What is the impact of artificial intelligence (AI) on a credit union's business model and strategy?

Some made the point that AI is already here, and we interact with it every day in the form of Apple’s Siri and Google Assistant.  Along those same lines, a credit union recently announced that its members will be the first to have the ability to converse with Alexa, Amazon’s voice-activated virtual assistant, to conduct their financial transactions (Source:  Alexa Becomes Enrichment FCU Members’ New Banking Buddy).

The reality is that AI is a broad term encompassing natural language processing (like Siri), the ability to see patterns and relationships in data and derive insights, and far beyond.  In fact, financial institutions are already using AI for fraud detection, investment robo-advisors, help with regulatory requirements, and more.  It holds great potential for lowering expenses, improving the member experience, and helping to create deeper “relationships” as the definition of relationships evolves in the digital world.

To get a better feel for what might be next, here are a few examples of what some large financial institutions have been up to recently in the AI arena:

COIN – In June of 2017, JP Morgan Chase introduced COIN, short for Contract Intelligence. Using machine learning technology, COIN has exponentially reduced the time it takes to review the 12,000 commercial-loan agreements the bank processes each year and resulted in fewer mistakes.

Chatbots – Also called virtual assistants, chatbots are designed to use artificial intelligence to enable customers to interact via natural language (voice or text).  Bank of America’s erica is one example.  Erica will be incorporated into the mobile app, allowing customers to conduct routine transactions as well as providing financial guidance in the form of smart recommendations.

RPA Bots – Robotic Process Automation (RPA) bots are software applications that utilize artificial intelligence, and are programmed to automate tasks that are typically performed by staff but are repetitive in nature.  RPA technology mimics a human worker, logging into existing applications, entering data, completing tasks, and logging out.  It is designed to eliminate the need to reprogram underlying systems for speedy implementation.  Bank of NY Mellon Corporation has been using this technology to improve efficiency and reduce costs (Source:  BNY Mellon’s Automation Efforts Draw Industry Accolades).

Artificial intelligence can clearly improve efficiencies, but holds real promise in making it possible to know members better.  It can process vast amounts of data from multiple sources, and make connections with the end goal of better understanding members.  There’s a good chance that back when your credit union was founded, staff knew every member personally.  Those days are gone for most, but AI could make it possible to “know” members like that again.

For many credit unions, the member relationship is part of the differentiator that drives their business models.  Having deep, individual knowledge of members available to the front line and in marketing could propel relationship-building to new levels.

For now, just like other developing technologies, it’s best to keep artificial intelligence on your radar screen.  Spend some time imagining how various applications of AI might change your credit union, and continue to broaden your thinking on what it could mean for your membership going forward.

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180-Second Exercise: No Commitment Car Ownership With Fair

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180-second exercises are a great way to practice brainstorming and thinking creatively about the different questions, concerns, and changes in the economy and consumer behavior – fast!  Use 180-second exercises as an icebreaker, team builder, or way to start thinking about a particular topic or trend on the horizon.

180-second exercise to practice brainstorming and thinking creatively about questions and situations.

In this exercise the focus is on Fair, a new entrant to the digital car-buying market looking to change the way consumers think about vehicle ownership.

In a group, watch the following video at www.fair.com.  After watching the video, set a timer for 180 seconds and identify 20 ways Fair can impact the credit union positively or negatively.

Remember, the goal is to think creatively and brainstorm so go for quantity, not quality.

If you need ideas for other 180-second exercises, feel free to call us.

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Is The Yield Curve Flattening?

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Going back to the end of 2015, the Federal Reserve has lifted the Fed Funds rate up from its zero lower bound to target a range of 1.25% to 1.50%, with additional tightening anticipated in 2018.

Often, when places model risk, it is assumed that when short-term rates move, long-term rates will move parallel.  Thus, when short-term rates increase, it is often expected that long-term rates will also increase—but this is often not the case.  From 2004 to 2006, the Fed raised the Fed Funds rate from its target of 1.00% up to 5.25%.  Short-term rates responded, but long-term rates did not move very much.  This led to then-Federal Reserve Chairman Alan Greenspan’s “conundrum” comment and an inverted yield curve:

A graph showing the compression of yield curve, 2004-2006

Since longer-term rates influence yield on assets, and shorter-term rates influence cost of funds, the difference between short- and long-term rates is important for credit union earnings.  When the difference is larger it can help credit union margins, and when short- and long-term rates are closer together, it can squeeze margins.  A sophisticated model should automatically change the shape of, or “twist,” the yield curve with every simulation and what-if scenario that is modeled.

The importance of twisting the yield curve on every simulation and what-if scenario cannot be overlooked.  During the tightening from 2004 to 2006, for example, cost of funds for credit unions $1 billion to $10 billion in assets increased 1.27%, while the yield on earning assets increased just 0.88%, according to NCUA data.  This move took about a 40 bp bite out of these credit unions’ net interest margins.

As we look at recent history, we see that, once again, as the Fed is tightening its rates, the yield curve is compressing.  Will it flatten out or invert as it did the last time the Fed tightened?  No one knows, but it is compressing:

A graph showing the compression of yield curve, 2015-2017

Assuming a parallel increase will generate a higher yield on assets and will result in a higher simulated margin than may be experienced with yield curve compression.  Often, twists of the yield curve are incorporated into modeling once per year as part of stress testing.  Compression of the yield curve as the Fed tightens is not a stress test.  History has shown this to be a common expectation.  We run thousands of simulations and what-if scenarios every year, each one of them testing a wide range of rate environments and yield curve shapes.  We encourage every institution to incorporate the real risk of yield curves changing in every simulation.

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Sharing Economy Ripe For Disruption By Blockchain Technology

It turns out that the internet is a great matchmaker—even beyond dating sites.  eBay matches buyers to sellers, Airbnb matches rentals to renters, and LendingClub matches borrowers to lenders.  Now, these stars of the sharing economy, many of whom were disruptors, are ripe for disruption themselves.

A definition of sharing economy

These businesses still have a lot in common with traditional business models.  The platform owner ensures that business is conducted as agreed, provides a level of safety, and takes a (sometimes hefty) cut as profit.  This is where the use of blockchain technology has the potential to foster enormous change.

A definition of block chain technology

Take ridesharing as an example, and imagine drivers and riders connecting directly via an app.  They are negotiating their own transactions without a middleman like Uber or Lyft.  (There are already startups doing this.)  Some view this as a more authentic sharing economy where individuals are not beholden to big corporate entities.  Blockchain makes this possible through its ability to provide things like digital identities linked to a publicly available reputation system and cryptocurrency payments—with no intermediary.

While the financial services industry has so far survived the “LendingClubs” of the world, the emergence of blockchain could change the game for better or worse.  Blockchains don’t have to be public.  There is a lot of investment and development underway on permissioned blockchains that can be utilized privately by a financial institution.  These could be used to make the infrastructure far less expensive, create efficient and secure ways to automate contractual agreements, track financial transactions, log asset ownership, etc.

And for once, regulation might be a good thing.  The highly regulated nature of the industry makes it more complicated, time-consuming and expensive for new entrants to carve out niches in this space, especially using new underlying technology.

These are just a few examples of how blockchain could disrupt the sharing economy and financial services.  The possibilities are endless.  It’s like going back to the 1990s and asking what we would be able to do with the internet—even though most of the answers hadn’t even been thought of yet.

*Definitions sourced from Oxford Dictionaries.