Houston startup uses artificial intelligence to bring its clients better business forecasting calculations

AI on the mind

Houston-based Complete Intelligence was just recognized by Capital Factory as the "Newcomer of the Year." Photo via completeintel.com

The business applications of artificial intelligence are boundless. Tony Nash realized AI's potential in an underserved niche.

His startup, Complete Intelligence, uses AI to focus on decision support, which looks at the data and behavior of costs and prices within a global ecosystem in a global environment to help top-tier companies make better business decisions.

"The problem that were solving is companies don't predict their costs and revenues very well," says Nash, the CEO and founder of Complete Intelligence. "There are really high error rates in company costs and revenue forecasts and so what we've done is built a globally integrated artificial intelligence platform that can help people predict their costs and their revenues with a very low error rate."

Founded in 2015, Complete Intelligence is an AI platform that forecasts assets and allows evaluation of currencies, commodities, equity indices and economics. The Woodlands-based company also does advanced procurement and revenue for corporate clients.

"We've spent a couple years building this," says Nash. "We have a platform that is helping clients with planning, finance, procurement and sales and a host of other things. We are forecasting equity markets; we are forecasting commodity prices, currencies, economics and trades. We built a model of the global economy and transactions across the global economy, so it's a very large, very detailed artificial intelligence platform."

That platform, CI Futures, has streamlined comprehensive price forecasting and data analysis, allowing for sound, data-based decisions.

"Our products are pretty simple," says Nash. "We have our basic off the shelf forecast which is called CI Futures, which is currencies, commodities, equities and economics and trade. Its basic raw data forecasts. We distribute that raw data on our website and other data distribution websites. We also have a product called Cost Flow, which is our procurement forecasting engine, where we build a material level forecasting for clients.

completeintel.com

"Then we have a product that we'll launch next year called Revenue Flow, which is a sales forecasting tool that will use balance of both client data and publicly available data to forecast client sales by product, by geography and so on and so forth. So we really only do three things: revenues, costs and raw data forecasts."

Forecasting across industries

Complete Intelligence's Cost Flow and Revenue Flow products are specific to direct clients. They are working with clients in the food and beverage sector, the energy sector, the chemical sector, and the technology sector.

"Anybody that manufactures a tangible good, should use our product," says Nash. "Because we can take their historical data we can configure their bills of material and they can see the exact cost and exact revenue of those products by month over time."

CI is not a consulting firm, so they offer their clients an annual license, which allows them to receive updated forecasts every month to understand how markets will iterate over time.

"We're integrating with the client's enterprise data," says Nash. "Whether it's their ERP system or their procurement system or their CRM, we're integrating with client's enterprise data, and we're creating forecast outlooks that are perfectly contextually relevant for client buying decisions."

Called out by Capital Factory

As a business solution, CI has garnered widespread industry confidence and accolades, such as Capital Factory's coveted "Newcomer of the Year" award, which recognizes innovative companies from a pool of 110 startups in Texas.

"Honestly, I couldn't believe it because with a startup like ours, there's so much hard work that goes into it, there's so much time, there's so much persistence," says Nash.

"And the types of startups that Capital Factory attracts are very competitive startups, so for us to receive this award, it's given us a huge amount of credibility in the market and it's really encouraged the team inside the company to understand that what we're doing is being recognized, it's meaningful and we're really going places."

From consulting to billions of monthly calculations

Nash is no stranger to going places. Before setting up shop in his native Texas, he lived in Singapore for 15 years where he started his career in sourcing and procurement for American retail firms.

"I became very sensitive to costs, cost inflections and I got very involved in global sourcing and international trade and then I did a couple of corporate turnarounds and start ups and so with that you see costs as an issue with those types of firms," Nash says.

He then worked with the Economist running their global research business. There, he grew familiar with how clients and customers use data. At IHS Markit, a global information provider.

"When I was working with those firms, those firms helped companies with planning," says Nash. "The problem is that those firms have very large errors in their forecasts. It is not just the internal forecasts that have a 30 percent or higher error rate in their forecasts, even the industry forecasters typically have around a 20 percent error rates in their forecasts.

"Even the people who should actually know where prices are going are not very good forecasters. With Complete Intelligence, we wanted to use data and use artificial intelligence to machine learning to create a better way to identify where costs and revenues will go for companies."

Every month, CI runs billions of calculations. They test their error rates and record them for clients that request them. With 700 assets that they show publicly, CI their average error rate is 3.7 percent, which is dramatically lower than both corporate procurement professionals and industry experts.

"With us doing billions of calculations, it allows us to run simulations and scenarios that your average analyst just can't do and most companies haven't even thought of. We're able to run a comprehensive view of activities in the world to understand how things directly and indirectly affect a cost. In Houston, for example, that could be crude oil or natural gas or something like that."

Proving its value

Last year, the company tested its platform with a natural gas trader. After reviewing the data, CI revealed to the client that natural gas would fall by 40 percent over the next year.

"They looked at our forecast and said they couldn't work with us because it didn't make sense," says Nash. "A 40 percent fall didn't make sense, so they didn't subscribe to us. That was 2018. What has happened over the past 12 months? Natural gas prices had fallen by 49 percent. You would look at our forecasts and say, 'Wow, that's a dramatic drop over 12 months.' But reality was even more dramatic than that and there weren't analysts out there saying what our model was telling us."

That natural gas trading company never admitted its faux pas, but if they had listened to CI, they could have positioned themselves to negotiate their vendors down for their cost base, which helps the margin of their business.

"Nobody ever admits mistakes," says Nash. "But when you think about the numerous materials that require natural gas, especially things that are manufactured in Houston, it affects a lot of costs."

Houston roots — by way of Asia

The missed opportunity with the natural gas trader notwithstanding, Nash is happy that he brought Complete Intelligence to Houston.

"I went to Texas A&M and grew up in Texas, so I moved back to Texas knowing how good Americans are with planning, with math and with data. I like Houston because people make stuff in Houston," Nash says. "We just found Houston to be perfect after spending 15 years in Asia given the global centrality of Houston. The industry's here and there's a lot of diversity in Houston."

Nash's expectation was that he would be able to work with Western multinationals to improve their analytics and their artificial intelligence processes because he has learned that there is a lot of pressure in American financial markets and analysts communities to really know what is happening within companies.

"We want companies to be able to really tightly plan their costs so they can better improve their profitability," says Nash. "That's what I wanted to do when we moved to the U.S. and we're finding that there's a lot of interest from companies."

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New CEO brings strategic vision to Houston co. advancing neurodegenerative disease treatments

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Coya Therapeutics has named a new CEO. As of Nov. 1, Arun Swaminathan replaced Co-founder Howard Berman in the role. Berman has assumed the title of executive chairman, in which he will still remain active with the company.

Swaminathan started with Coya two years ago as chief business officer. This transition was planned, says the PhD-holding scientist and businessman.

“(Berman's) intent was that it was the right time to put in place a CEO that, as we move into the operational phases of the company, that can take the reins from him,” he tells InnovationMap.

Coya Therapeutics is a publicly traded biotechnology company that is working on two novel treatments for Alzheimer's disease. Coya's therapeutics, which are currently in trials, use regulatory T cells (T regs) to target both systemic- and neuroinflammation in patients.

InnovationMap: Berman has been a very visible CEO. Will you follow suit?

Arun Swaminathan: I think it's part of the CEO’s job to be visible and to communicate the value of our company to all the stakeholders out there. So yes, I do plan to be visible as well. Obviously, Howard as the founder had elements that he talked about, the foundational stories. I obviously will be doing less of that.

IM: What was your journey from the lab to the boardroom?

AS: I have a PhD from the University of Pittsburgh. I like to say that I grew up at Bristol Myers Squibb, so I started in a clinical pharmacology group at BMS, running clinical trials, but in the cardiovascular and metabolic space.

What happened was, as I was the study director on a diabetes trial there, and the data starts coming in for these early diabetic trials, and I got highly involved with the commercial folks at BMS in starting to plan out “What does the target profile look like? How is this going to play out in the real world?” You know, the marketing teams and commercial teams start engaging when clinical data is available, because they're starting to plan for the eventual launch of the product.

That gave me a lot of exposure to the commercial side of things, and I also got a lot of experience presenting to opinion leaders and others through that role. And I said, “What I really love is that intersection between science and business.” And so I think that was my moment.

Then I moved to business development and licensing, where I helped scan the universe for assets and talk to CEOs of companies like Coya as a junior person, trying to understand if there's something that we can bring into BMS to strengthen the pipeline of BMS. So that gave me exposure to deals, how deals are structured, how you negotiate a lot of that kind of stuff.

Then I said, “Look, if I want to be a complete person in biotech, I do need to go into more true commercial roles.” So I went into commercial strategy. I was involved in the commercial strategy for what is now known as Eliquis. Was back then known as apixaban. That’s still the generic name.

Then I led marketing for Orencia, a rheumatoid arthritis drug. So I went and got both strategic and tactical marketing experience at BMS, and then I used all of that experience, rounded up. I eventually ended up co-founding a company, and that's led me to the last nine years with smaller biotech companies. So that's my evolution and path. But I think my true moment of realization was about three years into my clinical role at BMS, when I said, what I really enjoy is translating good science into commercial value, and I think that's what excites me.

IM: Why is Houston an important part of Coya's success?

AS: It is important that Coya stays in Houston, because we have a very close association with Houston Methodist, we get a lot of our work, our early research work still done through Houston Methodist, through Dr. [Stanley] Appel's lab and through other experts. We absolutely have a special research agreement with Houston Methodist, so we have a very strong reason to be in Houston. So, we do not anticipate moving out of Houston.

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This conversation has been edited for brevity and clarity.

California femtech leader integrates Houston-founded conversational AI tool

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Willow users, meet Ema — you're new best AI-enabled friend.

Houston-founded Ema (née SocialMama), an AI resource for maternal health support, has been loaded onto the Willow Innovations Inc. app, a platform for breastfeeding mothers.

"We are thrilled to integrate our conversational AI platform into the Willow App and leverage the power of advanced technology to meet women where they are with the information they need," Amanda Ducach, founder of Ema, says in a news release. "Ema and Willow have a shared mission to make lasting improvements in women's health and we built a one-of-a-kind solution that addresses the unique challenges mothers face regularly.

"Our partnership represents a critical advancement for today's mothers and demonstrates how responsible AI can improve the maternal care experience," she continues.

Now, users on the app can utilize Ema's HIPPA-secure tool that pulls from a comprehensive, proprietary database of expert-backed information. The conversational AI chatbot responds prioritizing speed, accuracy, and empathy and compassion for users. Ema reports that their users "will feel like they are talking with their favorite postpartum (labor and delivery) nurse."

"New moms don't have enough support — for their feeding and parenting journey, or in their postpartum care. We're thrilled to partner with Ema to offer moms personalized information along this journey," adds Sarah O'Leary, CEO of Willow. "AI tools, when implemented thoughtfully, can help close gaps in delivering personalized guidance at an efficient scale and I'm excited about the impact Ema can have on the well-being of mothers in our Willow community."

Founded in 2018 as a way to connect new moms, Ema evolved with a major rebrand and pivot to AI-backed tools last year.

Willow was launched in 2014 and released the first wearable, in-bra breast pump in 2017.

Houston researchers find alternate data for loan qualification

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Millions of consumers who apply for a loan to buy a house or car or start a business can’t qualify — even if they’re likely to pay it back. That’s because many lack a key piece of financial information: a credit score.

The problem isn’t just isolated to emerging economies. Exclusion from the financial system is a major issue in the United States, too, where some 45 million adults may be denied access to loans because they don’t have a credit history and are “credit invisible.”

To improve access to loans and peoples’ economic mobility, lenders have started looking into alternative data sources to assess a loan applicant’s risk of defaulting. These include bank account transactions and on-time rental, utility and mobile phone payments.

A new article by Rice Business assistant professor of marketing Jung Youn Lee and colleagues from Notre Dame and Northwestern identifies an even more widespread data source that could broaden the pool of qualified applicants: grocery store receipts.

As metrics for predicting credit risk, the researchers found that the types of food, drinks and other products consumers buy, and how they buy them, are just as good as a traditional credit score.

“There could be privacy concerns when you think about it in practice,” Lee says, “so the consumer should really have the option and be empowered to do it.” One approach could be to let consumers opt in to a lender looking at their grocery data as a second chance at approval rather than automatically enrolling them and offering an opt-out.

To arrive at their findings, the researchers analyzed grocery transaction data from a multinational conglomerate headquartered in a Middle Eastern country that owns a credit card issuer and a large-scale supermarket chain. Many people in the country are unbanked. They merged the supermarket’s loyalty card data and issuer’s credit card spending and payment history numbers, resulting in data on 30,089 consumers from January 2017 to June 2019. About half had a credit score, 81% always paid their credit card bills on time, 12% missed payments periodically, and 7% defaulted.

The researchers first created a model to establish a connection between grocery purchasing behavior and credit risk. They found that people who bought healthy foods like fresh milk, yogurt and fruits and vegetables were more likely to pay their bills on time, while shoppers who purchased cigarettes, energy drinks and canned meat tended to miss payments. This held true for “observationally equivalent” individuals — those with similar income, occupation, employment status and number of dependents. In other words, when two people look demographically identical, the study still finds that they have different credit risks.

People’s grocery-buying behaviors play a factor in their likelihood to pay their bills on time, too. For example, cardholders who consistently paid their credit card bill on time were more likely to shop on the same day of the week, spend similar amounts across months and buy the same brands and product categories.

The researchers then built two credit-scoring predictive algorithms to simulate a lender’s decision of whether or not to approve a credit card applicant. One excludes grocery data inputs, and the other includes them (in addition to standard data). Incorporating grocery data into their decision-making process improved risk assessment of an applicant by a factor of 3.11% to 7.66%.

Furthermore, the lender in the simulation experienced a 1.46% profit increase when the researchers implemented a two-stage decision-making process — first, screening applicants using only standard data, then adding grocery data as an additional layer.

One caveat to these findings, Lee and her colleagues warn, is that the benefit of grocery data falls sharply as traditional credit scores or relationship-specific credit histories become available. This suggests the data could be most helpful for consumers new to credit.

Overall, however, this could be a win-win scenario for both consumers and lenders. “People excluded from the traditional credit system gain access to loans,” Lee says, “and lenders become more profitable by approving more creditworthy people.”