What Happens If You Fall Behind

If you have heard me speak or conversed with me about disruptive technologies, you will often hear me reference incremental versus exponential advancements. Since, this will often be referenced throughout the blog posts in 2019, I thought it was worth clarifying up front, so there is no confusion what I mean.

Common Question

*graph from Harvard Business Review’s ‘How to Create an Exponential Mindset’

The most common question I get from bankers is, “Can’t we just purchase the technology in the future and catch up?” I don’t think that will be possible or at the very minimum extremely difficult and expensive.

In the following paragraphs, I will explain the difference between the two types of advancements and why I feel that it is not going to be easy if you fall behind. If you read my previous post, “Banking on AI: Conversation with a Community Banker,” you already have a glimpse into what I am going to discuss. Keep the graph to the right in mind as you read along.

Exploring Incremental

Let’s start with a definition of Incremental Change from the Business Dictionary, which states: “A small adjustment made toward an end result. In a business environment, making an incremental change to the way that things are done typically does not significantly threaten existing power structures or alter current methods.”

This is the type of change that is the most familiar to bankers. It is a gradually increasing linear improvement over time.

Let’s explore a very simple example in web banking. When a company comes up with a new UI or special functionality, within a few month either the web banking provider or the bank’s technology team will integrate the feature. The introduction of the new feature, levels the playing field within the industry, but increases the utility for all customers.

In an environment of incremental change, it a very hard to differentiate your offering from others for any length of time. If a popular feature is developed, soon after launch everyone else will have the same or a similar one.

Exploring Exponential

The definition of Exponential Growth from the Business Dictionary states: “Increase in number or size, at a constantly growing rate. It is one possible result of a reinforcing feedback loop that makes a population or system grow (escalate) by increasingly higher amounts. Compound interest is an example of exponential growth.”

The compound interest example, from the definition, is something we are all familiar with in banking. If you start saving at Age 25 versus Age 45, what happens if both want to have the same amount of money at age 70? The 45 year old will need to save over 3X, depending on rate of return, the amount of the 25 year old to catch up. This assumes the younger person never increases their savings amount.

Reality

What is really happening though, in an exponential technology change, is that the early adopters are leveraging their learning to increase asset gathering, reducing fraud, developing new products, reducing expenses, or some combination of the above. Every improvement provides them more data to improve/ refine even further, which accelerates their progress. In AI, this is referred to as reinforcement learning.

Thus, when you start later than other institutions, you have a substantially higher mountain to climb, but also less feedback to improve your models.

Let’s Examine Further

There is no shortage of companies that will allow you to purchase their learning algorithms, so “Yes” you can theoretically buy it to start. This could work as a short term solution.

Further, this is assumes that your customer bases are homogeneous, which we know they are not. You can put two banks, side by side, and depending on their customer demographics could have very different performance metrics, as evidence in saving ratios, default rates, efficiency ratios and many other measures.

So, outsourcing in this scenario moves everyone towards the mean. Inevitably, once the better performing institution realizes this, they split from pack and start building custom solutions.

When this happens, they will temporarily experience a decline as the solutions are developed, but very quickly thereafter, will begin to pass the pack, visualized in the earlier graphic, and we enter loop described in the Reality section. At the point of convergence, it is a race to catch up again, which I believe is highly unlikely.

Thoughts?

I am always interested in hearing others viewpoint on this topic. What do think is going to be the most likely scenario? Do you think it is possible to catch up if you wait?

Improving PFM with AI and Blockchain: A Thought Experiment

At our AI meetup last month, a small group of us started brainstorming the convergence of artificial intelligence and blockchain.

For a background the subject, this article, “Next Steps In The Integration Of Artificial Intelligence And The Blockchain,” does a nice job of explaining the current state of development and the challenges ahead.

One use case that I have been exploring conceptually is Personal Financial Management (PFM). Today, the bank either provides you an integrated service or you can use any one of the online services.

The challenge with PFM software, has been long term adoption and usage. Lets face it, looking at charts, alerts, category assignments and recommendations about spending and saving is just not that exciting.

This poses the question, is they are better way for people to have truly personalized financial management without the hassle?

The idea I was bantering around with the group, was how about using edgeai, similar to IOT, in the mobile wallets. The information it collects would be anonymized and shared with a decentralizedai, similar to a masternode in some blockchains.

As a single node within the decentralizedai ecosystem performed better on particular measures, by some threshold, than the consensus neural net, it would broadcast the update to all the other nodes in the network. This would require advances in unsupervised learning, but is a worthy thought experiment.

Simultaneously, the edgeai device would be completing similar work but balancing local with global optima. Perhaps, the edgeai is connecting to decentralized exchanges and trading tokens based on usage patterns, or moving money into and out of currencies or accounts.

For example, if you travel overseas, it immediately recognizes the local currency and finds that most efficient and lowest cost to convert from fiat to fiat or fiat to tokens to fiat, all based on personal spending habits.

Due to the early nature of the concept, there are gaps in this idea, and I encourage others to participate in developing it further or radically changing it. Conceptually I think we are on the cusp of truly personalized financial management that originates not from one organizations data about us, but from all of it.

What do you think? Is edgeai and decentralizedai even worthy of consideration at the current time?

Banking on AI: Conversation with a Community Banker

I recently had a casual conversation with a local banker about what I was working on, and I am sharing parts of the conversation.  This is not a story about AI products or specific applications, there are plenty of those available, including one on our website, so you will not see any charts, graphs or links.  This is a recognition that we, as consultants and technology providers, do not do a good job of cutting through the technical jargon and specific applications to explain how a local bank in Minneapolis, Omaha, or Louisville could be impacted by it.    

Note: Please suspend judgement of the buzz factor and think about how they will change the way we conduct business, acquire customers, and develop products. It is worth noting that this is only one of a thousand scenario’s that could transpire and was simply for discussion.

The conversation proceeded as follows:

Banker: So, what are you working on now? 

Me: I am working on how the use new technology like artificial intelligence, robotic process automation and blockchain will change the way we bank and how banks can respond. I think these will have wide ranging impacts on all banks regardless of size or location and embracing them will be vital to survival.

Banker: Are you saying they will put us out of business?

Me: No, I think banks will put themselves out of business, because they cannot afford to invest or will delay investing in the technology until it is too late (frog on the stove scenario).  Once, a few banks begin to use the technology, they will differentiate themselves from the others resulting in better products, improved recommendations, differentiated underwriting, and faster, more efficient customer acquisition and processing, which will result in one of two scenario’s: 1) the best of those left behind will be acquired and 2) the remainder will slowly see their assets drained away to the point that it is no longer profitable to remain in business. 

Banker: Can’t we just buy the technology to be competitive when it makes sense?  That has worked for us in the past. 

Me: In recent years, relying on others for technology, like a website or RDC has been an effective strategy to keep up with the competition, but I would classify those as “linear” advancements.  I see linear advancements as new technology that quickly becomes relatively homogenous in nature.  For example, it is possible to build better website functionality, but upon release the vendor that builds your websites will introduce the functionality within a few releases, bringing everyone back to the mean.  True differentiation in linear technologies is difficult and provides marginal short-term benefit. 

What we are going to experience with technologies like RPA, AI, and blockchain is what I would view as “exponential” advancement.  My view is that as these are developed and implemented, the banks that use them first will quickly begin to outpace the others thus producing a chasm that will be extremely difficult to overcome, unlike our earlier experiences with technological change.  Everyday, the initial gains, although small at the start, compound producing ever greater insights, capabilities and differentiation.  Do not misunderstand me, there will be vendors that provide the tools or full solutions, but I do not think this will be anywhere near as easy to outsource as web development. Even if they are robust and easy to use, the early adopters of the technology will still far outpace laggards. Depending on the time delay, it may be possible to catch up, but at what cost? In banking terms, think compound interest.

Banker: Give me an example of how this would work.  

Me: Let me walk through a simple example of how I envision this could transpire.  To focus this example, I am going to purposely ignore the blockchain.

First, we introduce RPA in the operational units for routine entry of information from structured documents or other platforms.  The technology increases scalability and input quality while lowering costs and it works 24x7.  The people that used to do this work are now free to focus on higher value activities.  Next, we layer in artificial intelligence for unstructured information.  The software is “trained” initially by the employee, in the routine course of business, by “observing” their activities and it begins to build the new capability.  As the quality of robot simulating the task increases to acceptable thresholds, the work is slowly transitioned to the robot, leaving behind just the highly complex work and substantially fewer staff performing it.

Next, we leverage the gains to focus on AI in marketing and the hyper-segmentation of customers and tailored campaign messaging. Today’s digital consumers have been “trained” by the best technology companies and expect us to know them, understand them, and provide personal recommendations and insights through their preferred channels, when they want it at the lowest possible cost.  To compound this change, we have a post millennial digitally native generation, Z, that is coming of age and has even higher expectations, is technology savvy, and does not display high brand loyalty. 

The response rates from the marketing programs begin to increase, taking deposits and loans away from banks that are waiting to see how it progresses.  (For insight, review the use of AI in social media marketing, where is has produced a 50% or more improvement in response rates over employee managed programs).  The new program, not only gathered more assets and increased loan acquisition, but also increased feedback loops, which enables us to further improve the models with each successive generation.  This cycle continues through advisory services, product development, fraud, underwriting, customer service, and the remainder of the bank functions.  With each implementation and generations of improvement, we further separate from the other banks, to a tipping point where laggards bank can no longer catch up and are faced with one of the two previous scenario’s.

What do you see happening?