We Need To Talk…About DSCR

“We need to talk”.

I know they’re not words you want to hear, but I promise this time it’s worth your while.

About what exactly? Nope, you’re not getting fired, and we’re not breaking up. Today, we’re talking about DSCR — or debt service coverage ratio and why it’s basically the Holy Grail of CRE metrics.

DSCR is a calculation used to see the cashflow of your property after you’ve removed all of your annual debts, but what you really need to know is that it’s important. Like really important, and it’s generally the first thing lenders look at in the borrowing process. Why? Because it can determine the structure of a loan. For example, if someone isn’t even close to the minimum DSCR they are looking for, then the lender will decline the loan right away. If someone has a DSCR that comes very close to the minimum DSCR amount, then a lender might cut back the total loan amount which will then require a larger down payment. This will increase the DSCR amount AND lower the LTV amount which will mitigate the risk in two ways. See what I mean? Important.

How Do You Calculate It?

To calculate the DSCR of a given property, you’ll take your Net Operating Income (NOI) (more on that here) and divide it by the total annual debt.

Typically, a CRE loan has a business listed as the primary borrower. So, in this case the annual debt includes all of the current year’s business debt obligations (or borrowers’ debts). This would include debts such as line of credit payments, minimum credit card payments, business lease payments or any type of business loan payment (this can even include business equipment loans). The most common mistake I see in calculating DSCR is not including all of the borrower’s debts. Some people only account for the loan payment but forget to include all other business debts, so be sure to keep an eye out for this.

Let’s Practice!

In this case, let’s say you’re looking at a property with an NOI of $150,000 dollars and there’s a $10,000 dollar monthly payment. Over the course of the year (annual, or x12), that monthly payment would amount to a total Annual Debt of $120,000.

Divide the NOI by the total annual debt and that gives you a Debt Service Coverage Ratio of 1.25. So, what does that mean exactly? A DSCR of 1.25means that you have 1.25x the amount of cashflow on that property than you do debt (FYI, that’s good).

It’s important to note that preferences and requirements on what makes a ‘good’ or ‘bad’ DSCR value can vary from lender to lender. Generally, a lender will create a covenant saying that the property must have a “minimum DSCR of X” and then they track it through the life of the loan to ensure that the property is still making an adequate amount of money to make the loan payments. If the borrower doesn’t meet the minimum DSCR requirement at any point during review (typically tracked once per year at annual review) then the lender might start reviewing that loan more frequently (like every quarter, for example). Or, if this trend continues for long enough, they may possibly force the borrower to pay off the loan entirely.

While there is no ‘set range’, here’s generally how DSCR gets broken down:

No matter how ‘good’ the DSCR is at the outset, it always gets tracked throughout the year and here’s why: it fluctuates, and sometimes, a lot.

The DSCR value can change because the NOI can fluctuate, too, based on changes in things like vacancies and expenses. So, if your NOI decreases then your DSCR will decrease as well. Also, the total debts can increase as the borrower takes on more debt. If the borrower’s debts increase, then the DSCR will decrease.

Here Is What A Borrower Can Do To Work On Increasing Their DSCR:

  1. Increase their NOI by increasing their revenue (which they can do by raising the rent, for example).
  2. They can also decrease their expenses to increase NOI. For example, they can analyze their spending and cut down any unnecessary expenses.
  3. Lastly, they can pay off business debts. By paying off as much debt as possible it will decrease their overall total debt expense and in turn increase their DSCR.

We’ve established that DSCR is important, but today, it matters more than ever.

With market changes brought on the by the pandemic, we’re looking at a level of instability that makes lenders wary — and for good reason. In most cases (and pre-COVID), a ‘good’ DSCR value would be anywhere between 1.25 and 1.5. Today, you can expect that lenders are likely looking for DSCRs in a higher range than usual because of that volatility in the market. Raising the bar on DSCR helps lenders mitigate risk in uncertain times (like now) where expense rates and vacancies are higher than normal. As such, it’s extremely important for lenders to be able to consistently monitor the DSCR of a property with real-time data. This way, they can get the most accurate picture of performance possible and get ahead of unfavorable changes that may be coming.

And there you have it: DSCR. Hopefully this gives you a good overview of why it matters, how it’s calculated, and how to stay on top of it. Your takeaway for today? Keep your blood pressure down and your DSCR up! Doctor’s orders!

Until next time,


Still have questions? Drop me a note: laurabohlmann@blooma.ai.

Let’s Make Some (NOI)se For NOI

NOI. This powerhouse metric is a staple in CRE lending. And today, we’re breaking it down to the basics for you.

  • What is it?
  • How do you calculate it?
  • Why does it matter?

These are some of the questions I often get asked on the job – and for good reason. NOI, which stands for Net Operating Income is a critical measure of whether an income generating property is going to be profitable or not. Which, if you’re reading this and are in the world of commercial real estate, you definitely care about.

NOI measures a property’s ability to produce income based on the income from its operation and is often confused with cash flow. But make no mistake, these two things are not the same. NOI is different than cash flow because cash flow includes debt service for the property (debt service = debt payments). Cash flow is the income generated after the debt is taken out. Basically, that means the cash that the owner makes off the property after all the expenses are factored out.

If you want to understand what the NOI of a property you’re considering is, you’ll need to know how to calculate it:

First, we need to determine the Total Revenue of the property. So, for example, let’s say you have an investment property that makes $15,000 per month in rental income, after you subtract out any of the vacancies and concessions (more on that in a moment), you’ll multiply that by 12 – which puts you at $180,000 per year (your Total Revenue).

Next, you’ll need to subtract out any of the operating expenses. What falls into that category? Things like repair fees, maintenance fees, property management fees, insurance, etc. Now, I know what you’re probably thinking: where’s the mortgage payment in that list? Surely that would be included in your list of expenses for the property, right? Not exactly.

Technically, we don’t consider your mortgage as an operating expense, but rather, what’s known as a ‘finance expense’. Here’s why that matters: finance expenses are incurred by the owner/investor of the property rather than the property itself. (This is important to note because NOI is only intended to capture the income produced by that property. Finance expenses, on the other hand, can vary from borrower to borrower depending on how much they are financing their loan for and the structure of that specific loan. In other words: It’s not relevant in this case.) So, for this example, let’s say you have $50,000 of operating expenses each year. You’ll subtract that from your $180,000 which leaves you with $130,000. Your NOI.

The math is simple enough, but the result matters. A lot. Not only is NOI a really simple and reliable way to get at the value and potential of an income producing property – it’s also used to calculate lots of other critical CRE metrics as well. For example, you’ll use NOI to determine things like:

  • DSCR (NOI / Annual Mortgage Debt)
  • Cap Rate (NOI / Current Market Value)
  • Property Value (NOI / Cap Rate)

A Brief Interlude on Vacancies and Concessions:

Let’s get back to vacancies and concessions for a moment. Vacancies take into account the % of rental space at a property that is not currently leased, or is currently vacant. We remove vacancies out of the calculation because we currently aren’t collecting any rental revenue from that space. Concessions are typically given to someone as an incentive to sign a lease. Typically, this would be in form of free rent for a new tenant. For example, many places will give you one month of free rent if you ‘sign today’. In cases like these, you’ll want to make sure that the one month of free rent is not included in total revenue because it was not technically collected.

…Aaaaand that’s a wrap on NOI! Hopefully this gives you a better understanding of how it’s used and its importance to CRE lending. Still have questions? Drop me a note: laurabohlmann@blooma.ai.

Until next time!

Connect with Laura on LinkedIn

The Power of Curated and Consolidated Information

Answer to any question: 10 cents. Correct answer to any question: 10 dollars.

The information revolution has brought access to data to the masses. “Knowledge is power” is not specific enough anymore – the true challenge is in making sure the information used in decision making processes is reliable.

If you want to purchase a product, you spend time scouring the web, learning about the product’s characteristics, comparing it to its competitors, finding different suppliers and their pricing, etc. In order to make an informed decision you know that you must become a mini expert on that specific product. Businesses make this process more efficient by building information harvesting systems which automate a portion of the research, but these still require employees to invest their most valuable asset – time. Time to make sense of the information, to make sure that it is relevant to specific business needs, etc.

First, every piece of information has a finite lifespan. When you check the weather, you know that the information you just gathered is good for today only. The value of a commercial real estate property fluctuates as market conditions change, and a year-old credit score may not reflect the current financial status of a borrower. Therefore, it is important to identify the age, rate of accuracy degradation and expected expiration of each piece of data that you harvest.

A successful information harvesting and processing system is characterized by the user spending little to no time gathering and processing information and most of their time making decisions.

Next, you need to identify the credibility of the source of the data. Did you get the borrower’s net worth valuation from the borrower or a third party impartial source? Try reading about the same event at two different news outlets. The same story looks totally different if you experience it through different sources. You may have to read multiple sources and find the truth yourself somewhere in between them. This is fine when objective truth is not so important, but when information needs to be consumed in a path that leads to the success of the person or business entity, truthful data is crucial.

The situation gets even more complex when we consider the concept of infobesity. Information obesity is just what it sounds like – an epidemic that exposes humans to an overload of information, leading to a situation where we can’t see the forest for the trees. When an underwriter is onboarding a loan, they put a lot of effort into harvesting huge amounts of information, and at some point they need to make a decision based on a forest of data points. It’s like telling your doctor that you are not feeling well and explaining your symptoms, and in response he or she simply gives you a list of the hundreds of possible causes for those symptoms rather than telling you their qualified diagnosis. That’s the experience we are used to today when we use search engines, and it’s very similar to what decision makers face when they need to decide based on thousands of data points around a loan.

So, in our era, rather than saying “knowledge is power,” it is more precise to say that “curated and consolidated information is power.” The most valuable information must be curated by an expert (human or AI) to fit the needs of the business, automatically harvested by a computer system, and continuously checked for its lifespan, credibility and accuracy. And when all of that is done, the business must take the next step of consolidating the information in a form that will still deliver the powerful insights without causing an outbreak of infobesity.

Today, decision makers spend most of their time preparing and processing information and only a small fraction of it making business crucial decisions. A successful information harvesting and processing system is characterized by the user spending little to no time gathering and processing information and most of their time making decisions. Such users can conduct much more business in the same amount of time, which leads to a higher chance of business success and a lower “cost of doing business.” Such a system allows its users to look at information like a judge on “America’s Got Talent” might look at a contestant. They can watch just a few minutes of a performance and make an informed final judgment without further investment.

Now that’s power.

How to Build a Disruptive Product for a Conservative and Highly Regulated Industry

Seven tips for success.

I’ve been designing, building, and commercializing enterprise software products in several industries for the past 20 years. When Blooma was founded, I faced the new challenge of building a disruptive commercial lending platform for one of the most conservative and regulated industries – banks and financial institutions. Looking back at the last 18 months, it was an exciting and enlightening journey. We are getting great feedback on our product, particularly on our ability to utilize artificial intelligence to automate data collection, calculations, and valuation models, while enabling lending professionals to use their experience and talent in the art of their work.

I have learned a lot along the way so wanted to share my 7 best practices for anyone planning to build and launch advanced technologies to conservative industries that may just be catching up on disruptive innovation.

1. Build the product with customers for customers. We developed our product together with to-be-users, by partnering with future customers and hiring professionals from their specific industry. This is especially critical when the product team lacks experience in the industry that they are building for. The combination of views from both inside the targeted industry (for better product fit) and outside of that industry (for the “art of the possible”) typically leads to better innovations.

2. Combine user-centered and vision-centered innovation principles. Start building your product by applying user-centered innovation methods. Strive to understand the needs of users through deep analysis of their behaviors and how they interact with existing products. Later, vision-centered innovation methods should be added. This is required in order to focus both on doing the same things better, and also on doing things differently. During our development process, I pushed the team to bring up ideas and concepts that customers did not specifically ask for or did not think were technically possible, and some of those ended up being key parts of our solution.

I have learned a lot along the way so wanted to share my 7 best practices for anyone planning to build and launch advanced technologies to conservative industries that may just be catching up on disruptive innovation.”

3. Build flexible workflows using multi-services architecture. Each customer has their own way of doing things and requires the technology to adapt to their own workflows. This is simple when you build a custom solution, but much harder with a SaaS platform that is used by many different customers, where typically “one size fits all.” Early on, we decided to build a flexible architecture, allowing users to configure their own scoring algorithms, workflows, and how they would like to consume the asset and borrower analysis (in our intuitive UI, a summary report, or even directly feeding their own Excel spreadsheet models).

4. Build your human/machine interaction to be mutually beneficial. No matter the level of sophistication of the artificial intelligence and automation that you develop, it must be clear that the human users are always in control. AI can enable customers to make scientifically informed decisions, but it should not make decisions for them. Our product provides ways for users to validate AI-driven information gathering and to easily correct it where needed. For example, we use machine learning models to select the best sales and rent comparables for an accurate valuation, but users can still include or exclude comparables based on their specific experience and knowledge of the area. Not only do users end up getting better data that exactly fits their needs, but these interactions help train our AI models and make them even better.

5. Pay attention to the unique needs of the sector you’re serving. If you’re working with AI, the type of models that you develop must fit the needs of the specific industry that you’re building for. In more conservative and highly regulated industries there will always be a need to know exactly how the machine came to the results that it did. You should avoid deep learning models which typically don’t provide reasons or explanations. Think of the machine learning-based movie recommendations in Netflix. Based on a list of movies that you’ve watched, they tell you that you’ll probably like a certain recommended movie. If Netflix recommended a movie for you without explaining why, you might be less likely to pay attention. And in a heavily regulated industry, a conclusion like that without an explanation would not be acceptable at all.

6. Keep it simple and avoid developing complex technology where it’s not needed. Technologists many times are so focused on the art of the possible that they build features that will never be used just because they’re cutting edge or the latest trend. I say, where advanced technology doesn’t make sense, don’t use it! For example, you can spend months building complicated screens and models that are expensive to build and hard to maintain, when what users really want is a simple download to an Excel spreadsheet. When I review each proposed new feature, I always consider usability, simplicity, and maintainability in equal measures.

7. Build the “leap” disruptive innovation in parallel. Sometimes customers (or industries) are just not ready for truly disruptive innovation. So, while adhering to the previous recommendations, I always maintain a product innovation track where we research and develop truly disruptive innovation that might not be accepted today but could be eased into in the future. For example, today users would probably not accept making loan decisions based on an AI model, but we can build this model in the background while still allowing users to analyze risk the way they are used to. Then sometime in the future when we can show them how accurate the AI model would have been in predicting a loan default, they might be ready to adopt it.

Hopefully you’ll find something helpful in these 7 tips. Introducing something new to a sector that has been satisfied with doing things the old way for a long time can be daunting. But it can also be fun, especially when you know that you’re truly making a difference.

The Art of Shaping the Story

CRE service providers and the current technology revolution.

When I bought and refinanced my home, I opted for a lender who provided an online experience, and it was pretty painless. It was clear to me during the process that the world of residential real estate has undergone a technological transformation, and I think most consumers have embraced that change or at least benefited from it whether they noticed or not (and maybe that is the best measure of an industry’s success at implementing technology).

Commercial real estate? We have some catching up to do. Why have we not embraced technology at the same pace as our residential peers? Well, like many things in life, it’s a numbers game. To oversimplify, our residential counterparts have large numbers in the amount of transactions and we tend to have large numbers in the dollar volume of transactions. Technology can do wonders for process and efficiency, and the residential world, with its long tail of high transaction volume, had more to gain from early investments in technology. Most in CRE believe that the personal touch of an old-fashioned phone call or in person lunch (pre-2020) carries with it a sort of cache. And I tend to agree, but technology is entering our industry at a rapid pace and it’s not replacing the old ways as much as allowing everyone to elevate their game (lenders and brokers included).

“No deal is the same, and every deal has a story. I work for a commercial mortgage brokerage, and our goal is to find the most efficient way to get a property’s (and borrower’s) story to the widest audience of lenders possible.”

There’s no doubt that CRE loans will never be as easily standardized as residential ones. I like to say that in the world of CRE the true value of a building really comes down to 3 things: what it costs to build, what the neighboring building sold for, and what the tenants are paying in rent. No deal is the same, and every deal has a story.

I work for a commercial mortgage brokerage, and our goal is to find the most efficient way to get a property’s (and borrower’s) story to the widest audience of lenders possible. Remember the pre-pandemic days when we all used to shop around for airfare to travel somewhere? You weren’t just looking for the cheapest price to fly. Based on departure dates/times, airlines and even airports, it’s likely that you decided on a flight based on more than just price. In our industry, there are a lot more variables than just destination and travel dates. A borrower may in fact be able to walk into a bank, advocate for their deal and get a loan quote, but a broker will give them a whole matrix of options with different features – the majority of which they wouldn’t have found on their own due to time constraints and access to capital providers.

Therefore, a broker’s job is to look at a loan and put it into a format that best tells its individual story. As we know, much of that work is done very manually today and involves pulling reports, formatting data, and constantly updating our analysis as new information arrives. Brokers also have to do the soft marketing, with analysts using design software to format everything into official presentations and loan packages. The point is, it’s very manual, and we’re often starting every story over each time with “once upon a time…” with a pen on a blank notepad.

But it doesn’t have to be that way. It’s a great time for the commercial side of the real estate industry to embrace the technological advancements of the last few years. Many of us have pushed PowerPoint templates, Excel macros and PDFs to their limits, after all. But I’m not just looking for an automated process for creating nice looking presentations – it’s a lot more than that. Integrations with data partners through API’s and financial spreading through document recognition truly are game changers in my mind. When the info, like comps, market narratives, and property data can be gathered and entered via machine, the analysts can focus on analyzing that data and shaping it into a compelling narrative.

In other words, an analyst’s job can move from pulling reports, entering data and formatting documents to truly analyzing deals. When I was working as an analyst this would have been a game changer, and my productivity would have multiplied (not to mention my job satisfaction). I’m sure my team would have loved the gains in productivity, and I know our clients would have loved rapid turnaround times. When it comes to technology, I think everyone can be a winner.

Still, technology isn’t going to revolutionize the industry overnight. Not every client wants to use their phone to get a CRE loan while in their pajamas, but that’s ok – I believe some will. In the meantime, my team is embracing the latest advancements in a lot of ways, but we still prioritize a personal phone call when it comes to getting deals done. It’s just that we can make a lot more of those phone calls and tell the individual stories of a lot more deals when technology has given us so much time back.

Industry Adaptation and Privacy in the New Era of AI

How data security is changing everything.

In a collaboration between BCG and MIT from 2018 (Ransbotham et al., 2018), researchers found that when it comes to artificial intelligence, companies and organizations can be classified into 4 groups: 18% Pioneers, 33% Innovators, 16% Experimenters and 34% Passives.

Pioneers are enterprises with an extensive understanding of AI tools and concepts, and embrace AI in significant ways. Innovators have a good understanding, but still display little actual application of AI in their business. Experimenters are using AI for their business, but without seeking an in-depth understanding of the AI methods. Finally, Passives lack both in-depth understanding and application of AI technology.

Interestingly, all four groups agree that AI will change their business model in the next few years. This means that sooner rather than later, AI applications will penetrate the entire corporate landscape. This will be important as AI becomes a pillar of competitiveness, allowing companies to get things done faster and more accurately and to reduce time spent on less desirable work.

“Private AI technology allows a company to use data to train highly performing models without exposing or sharing confidential data.”

In the past, laws around data privacy have hampered the evolution of AI applications in many industries which deal with private data, such as sensitive or personal information. Typical examples are the healthcare and financial sectors. In recent years however, emerging technologies have enabled the secure use of private data to build and use AI. These private AI technologies are accelerating the quantity and nature of novel AI applications, and transforming the AI landscape. Thus, Pioneers, Innovators, and Experimenters in the financial sector have started to use AI on their private data.

Private AI technology allows a company to use data to train highly performing models without exposing or sharing confidential data. Although the field of private AI is very cutting edge, there are a few notable methods already, including differential privacy, homomorphic encryption, federated learning, and data anonymization. For example, federated learning is an advanced, state-of-the-art method used by Google and the healthcare industry (full disclosure: Blooma uses it too). This method involves making use of client data, without the client having to share their data with anyone.

In a typical set up, a model is deployed to those client locations and trained on the data there. After training, the model is sent back to a central location where it is merged with the models of other clients. After this merge, you end up with a model which has learned from data across all clients without sharing the data in one central place. Federated learning, if done right, ensures that there is no exchange between each individual client’s data. Thus, federated learning allows a company to learn from confidential data while keeping the data secure and private.

Meanwhile, the introduction of AI into the corporate landscape has led to changes in legislation which continue to develop. The General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA) are two statutes that have recently come into play in the attempt to promote and regulate data privacy and security in the European Union and California, respectively. Additionally, Brazil passed the General Data Protection Law that came into effect in February 2020. According to Richard Koch, Managing Editor of the GDPR, EU, these legislations that have begun to emerge have some common principals, including the importance of defining personal data and certain fundamental rights relating to data subjects (2018). Private AI allows us to obey those laws while capitalizing on private data. Whether you consider yourself or your business closer to the Pioneer or the Passive end of things, your data is already a part of this changing landscape, and security will continue to be the driving factor in the future evolution of private AI technology. Despite the many differences between the statutes mentioned above, we can only assume this is just the beginning of the movement toward an increase in legislation to govern data privacy.

Philomena Lamoureux
Philomena Lamoureux

Philomena Lamoureux, Head of AI at Blooma, is a published PhD specialized in ML, NLP and privacy-preserving AI solutions.

The Life and Times of a Former CRE Underwriter

How the traditional approach can lead to more distractions than deals.

As Blooma’s Director of Customer Experience, I work a lot with lenders and brokers, especially their commercial real estate underwriting teams. I’d like to think I’m familiar with the motivations and struggles of our clients, since in a former life I used to be a CRE underwriter as well. The truth is, CRE underwriting and analysis processes are very fragmented, which leads to inefficiency.

Much of my job as an underwriter involved the management of many disparate data sources. For as large and mature as the CRE lending and investment space is, underwriting processes and innovation have not evolved or kept pace.

Got a new deal coming in? You’ll start by pulling up a model you used on a prior like-kind deal, and then overwriting the data. You then begin searching for sales and lease comparables on multiple sites, speaking with brokers about transactions and trends in the area, reviewing maps and street views to analyze the area surrounding the property, rounding up and reviewing title data, creating proforma cashflow models, the list goes on. Not to mention requesting and analyzing borrower financials, all in different mediums. You become a data aggregator, pulling in files, documents, and data from numerous sources and saving them all into a shared folder, drive, or system where they come together and start to make some sense. All of this work ultimately culminates in a final Excel model and a credit report/memo drafted in Microsoft Word. This is a painful process, and no matter how good you are at it, by its nature it is prone to errors.

“You become a data aggregator, pulling in files, documents, and data from numerous sources and saving them all into a shared folder, drive, or system where they come together and start to make some sense.”

An underwriter’s true job is to come to a binary decision as accurately as possible. Are we funding or passing on this? That’s it. But don’t get me wrong. Underwriters do not have a simple job even if their end goal is to make a decision one way or the other. And the “noise” of the process described above doesn’t help to get there – in fact I think it can mostly distract from this goal.

I think technologically we’re at a bit of a crossroads. Advancements in artificial intelligence have made it possible to take the more monotonous and time-consuming parts of the job of a CRE underwriter and hand a lot of this to computers. Tasks like parsing through borrower, financial spreading, searching for comps, etc. This allows underwriters to be much more efficient, and frankly frees them up to review each deal more analytically. To use their vast experience to engage in a strategic conversation about a deal and whether it should happen, which is what they do best.

If we hadn’t developed this approach at Blooma, someone else would have because its time has come. The only question now is how quickly lenders and brokers will embrace it. I’m excited working with all of the lenders and brokers that already have. It feels good to make the jobs of CRE professionals a little more enjoyable, by helping transition them to a newer way of doing things. It wasn’t that long ago that I was one of them.

Your Computer Gives You an A for Effort Today

Using AI to help you make decisions, not make decisions for you.

I demonstrate our product to many people, so I get to hear a lot of feedback and questions. One of the most potentially confusing aspects of what we do is using artificial intelligence and machine learning to generate a “score” for a certain commercial real estate property or proposed deal. It’s sometimes hard to explain what this really means, so I’ll try again here.

People sometimes think of the scores that are awarded an Olympic gymnast after a routine – we’ve all wondered about what can sometimes appear to be an arbitrary system of judging something. But we have to remember that this kind of interpretive scoring of a performance is not what is happening in the world of artificial intelligence.

When we engage with a CRE lender, our AI technology learns their own specific ideal lending profile. Over 3,000 data points are summarized into a “score” for them. The real value is in the automated collection and analysis, of course, and the score itself that is generated at the end is really just an interpretation. A computer can keep track of 3,000 data points and see the result in the data, but a person needs it to be streamlined and summarized. The artificial intelligence doesn’t start out with any idea of what is a good or bad deal, it simply compares all of the data to the lending profile that was preset by the user. By learning what kinds of deals you like, in other words, it can tell you how good a new deal should look to you. Our different customers might score the same transaction in wildly different ways, and perhaps they should.  

“You may think you know all of the factors that determine whether something fits your ideal profile, but sometimes the AI gets to know them even better than you do.”

Often times, our customers want the score explained to them. This makes sense – if your child came home with a C on their report card, you might ask them why it is a C rather than a B (or a D). The score itself doesn’t tell the whole story. This is one reason we use the type of machine learning that we do, so that we can go back and explain every data point that led to a conclusion. In other words, we can tell you exactly why your child got a C instead of an A. Some deep learning models (neural networks) don’t really allow for that kind of going back and parsing out exactly where each piece of data contributing to the whole originated. This would obviously not work in regulated environments such as banks where an audit trail is always necessary.  

Finally, there’s another advantage to teaching AI what you like and then letting it score opportunities for you. You may think you know all of the factors that determine whether something fits your ideal profile, but sometimes the AI gets to know them even better than you do. Data doesn’t lie, and I have definitely seen examples of an AI system being able to “teach” users that there may be certain patterns that they are looking for (or avoiding) that they weren’t even aware of. For example, AI might look at a large number of previous loans and be able to find patterns in the data that led to late payments or even loan default, that you might have not been aware of. Now you can simply use this insight to update your lending profile. It can be disconcerting to learn that the way you’ve been articulating your ideal profile might not be as accurate as you thought. But those of us who are open to learning something new can benefit from this greatly.

Bank Director Interview

Selections from a Q&A focused on Strategic Planning In A Rapidly Evolving Digital Environment.

Al Dominick, CEO of Bank Director, recently put some questions to our CEO, Shayne Skaff.

Al: What have you noticed post-PPP about the banks’ interest in bringing in new technology ideas – what have you learned over the last few months having heard from your clients about what they went through but also what they’re aspiring to become?

Shayne: Obviously, origination stopped in March, so it was really about asking how is their current loan portfolio being impacted in the space. As a technology company, we’re helping them understand with their existing portfolio, what does this new market look like for them, how are these assets that they’re managing being impacted by this new environment, and then our system is analyzing assets every day and our banking customers can see how the environment is actually impacting the current loans that they’re holding on the books. So, as the people were pushed to handle the PPP loans, the technology kind of took over, and it was still showing exactly what status their current business was in during this difficult time.

Al: As we think about planning for the future and the need to be agile, how are you starting to see banks frame that concept to solve specific business issues?

Shayne: So much of agility comes from the ability for humans to make quick business decisions. Data isn’t static. It’s constantly changing, and it’s important that we as humans are seeing the results that are taken into account with this dynamic state of our business. A good decision today can turn into a bad one tomorrow unless you are actually managing to that dynamic data on a real time basis. What we’re helping our customers do is really present the results of what real time data means for their business, so that when they see those results, they can make decisions on the spot.

“There’s an answer for me in my computer right now to a question I have not asked yet, and that’s where we’re going and we need to embrace that because that’s here in 2021.”

Al: When you think about individual expectations and how they’re shifting, help us to contextualize AI as it is now being used to address some of those customer confusions, or those things that are happening where you really want the machines to be crunching the data to get a better picture of the world in which you live.

Shayne: When most people think about AI in the workplace, they don’t initially think about the customer experience, they think about replacing jobs within companies, which is an easy place to go to. The way we think about AI is as a way to actually improve the employees’ work experience. We ask how do we move the employee to the art of their job and use the machine to do much of the science, really the work that the humans don’t like to do. If we can come in and use the machines to do a lot of the tedious work and number crunching and move the human to the relationship and the art of their work, that’s really where we’re focused. The way we look at AI is from the perspective of how to improve the workplace, and that generally translates into speed for the customer.

Al: When you’re helping banks think about what is possible, how much of a goal could you realistically put in front of them for the next 12 months or the next year and a half?

Shayne: When you have a heavily regulated industry like commercial banking, the first question is can I do that safely and will I be breaking any regulation. From a technologist’s perspective, you have to be patient in taking the bank through the process and help educate where we’re at with the technology, what it means from a security perspective and that everything is safe. All you need to do is look at other industries to see where banking needs to go because there are others that are so much further along. We’re going there, it’s just a matter of time and willingness to take those steps. Not coming out of traditional banking, I was told going in when we launched Blooma in 2018 that it was a very difficult space to sell technology into, but I’ve actually been pleasantly surprised with the reception that the banks have given us, not just community and regional, but some of the largest national banks in the country. I think there’s less fear now, and with every month there is less and less fear as people start to really understand that they need to produce speed, their customers are going to expect speed.

Al: Should early stage fintechs be judged by a different measuring stick as compared to established or traditional longer term vendors when it comes to vendor management?

Shayne: It’s super easy to implement a cloud solution now. It’s not like you have to pay a million dollars to implement and a huge on prem licensing fee. With a lot of providers you can try before you buy – they can launch their platform and allow the bank to get comfortable with it for 30 or maybe more days and then go into a commercial agreement. The flexibility with how technology is being consumed and purchased is like never before, it’s a whole different game today. As an early stage tech company, we’ve been super flexible with our clients in terms of how we engage with them commercially, the reality is that we’ve taken really all risk off the table. We know we don’t have a decade of customer references behind us that they can pull from, so just as a vendor make it super easy, take as much of the risk off of our clients hands as possible, allow them to do things like pilot and just not make it such a stressful decision for them.

Al: What helps them to say I’m doing something I’m comfortable with, but now I know I need to shift gears to something I’m not as familiar with but I think is the right thing. Is there a trigger that you’re seeing that others might appreciate?

Shayne: For us, we really try and come at a new client with how we’re going to use technology to improve their employees’ lives, which ultimately will resonate in better service for their customers. The workforce is changing, the people that have been doing the underwriting and all of the analysis for the last couple of decades are now moving out, and there’s a new workforce moving in that wants something different and so we generally talk about that first, that human element, and that seems to work well.

Al: How are you envisioning the first 6 months of 2021 shaping up?

Shayne: I’m generally an optimist, I won’t try to do this from a banking perspective but will do it from a technology perspective. I think the market itself has some big opportunities in my world on the CRE side and some extreme difficulties, and it will take more than a year to see where everybody lands. From a technology perspective, we’re seeing the end of monolithic technology. There’s so much modularity that exists out there with cloud platforms and the ability to plug in to various systems and integrate with various systems, and being able to utilize all of the data that exists out there to provide very quick decision making for business is going to be super important. Where AI and ML and some of these technologies are going to take us is really to a place where we’re going to have answers before we’ve even asked the question. There’s an answer for me in my computer right now to a question I have not asked yet, and that’s where were going and we need to embrace that because that’s here in 2021.

What Does A Valuable AI System Mean To Me?

Engaging your neural network and decision signature in time for breakfast.

You are standing in line at your favorite deli and you see that they recently changed the entire menu. There is a long line of people behind you. You feel a slight increase in your stress level as you tell yourself that you have about 20-30 seconds to decide what to order, to be considerate to those waiting behind you.

Your analytical mind immediately strikes out a few items for being too expensive or not in line with your diet, but you are still left with quite a few items to choose from. In the context of your life, this decision is far from “existential” and therefore not very important. You use your gut/intuition/your fast brain (See Daniel Kahneman’s great book Thinking, Fast and Slow) and decide.

Who knows why you might order one thing one day and something else the next. You may not even be sure yourself. Much of your day is spent making those non “fight or flight” decisions, at work, at home, and whenever you’re interacting with the world.

“In reality, to me, a valuable AI system is one that is able to ‘record’ and digitally store a portion of the experience of a specific human, so as to be able to mimic their decision-making process digitally.”

Without you being aware of it, these sets of small decisions can be made into a “signature” that is unique to you. When asked about you, people may say things like “well, that person is conservative in their choices” or “when buying clothing I should be asking that person, they have the best fashion sense,” etc. This “signature” defines you as a person as much as your physical features, knowledge or abilities do.

How do you make decisions? The neural network in your brain uses its vast experience, having been exposed to years of life where events continuously impact it. The sum of your senses “teach” your brain to avoid certain things and crave others. The same process takes place in your professional life. Your accumulated experience means that you know how to approach a problem and determine a course of action without a lot of hesitation and without having to spend too much time thinking about it.

This accumulated knowledge is priceless. We all have these “hidden gems” of knowledge that are unique to us.

In the movies, artificial intelligence is often a self-aware, auto-evolving entity that craves knowledge and is able to harvest data and make sense of it by itself. Often with a British accent, for some reason. In reality, to me, a valuable AI system is one that is able to “record” and digitally store a portion of the experience of a specific human, so as to be able to mimic their decision-making process digitally.

In other words, what if we could have the ability to forecast what Albert Einstein would do given a choice of approaches to solve a problem? What if we could consult Andy Warhol on what color to choose for a school art project? Or ask Sun Tzu how to strategize our next Fortnite game?

A valuable AI system is able to focus on a certain “vertical” of a human’s experience. By using a “fly on the wall” paradigm along with a feedback loop, it should be able, in time, to have the ability to mimic that person’s decision-making process.

In the not so far future you will walk into your office craving breakfast, and your favorite deli’s delivery drone will meet you at the entrance. It will have one of those new menu items it chose for you based on your unique “digital” decision signature. While you enjoy the delicious item you will probably say to yourself “wow, how did they know exactly what I wanted?”

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