Month: March 2021

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.

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