Deal to Watch: Personalized Banking through Artificial Intelligence

$8M

Key Stats: KapitalWise, Inc. on Microventures

Valuation Cap

$8M

Amount Raised

$97,600

Number of Investors

339

Minimum Raise

$25,000

Maximum Raise

$107,000

Likelihood of Max Unlikely
Start Date

03/19/2020

Stop Date

05/18/2020

Days Remaining

Closed

Security Type

Convertible Note

Investment Minimum

$100

Deal Analytics

Click Here

Summary

The KapitalWise team has been selected as a “Deal to Watch” by KingsCrowd. This distinction is reserved for deals selected into the top 10%-20% of our due diligence funnel. If you have questions regarding our deal diligence and selection methodology, please reach out to hello@kingscrowd.com.

The Problem

Valued at more than $22 trillion in 2019, it’s hard to overstate the influence of the global financial services market on the world’s economic health. But it’s also easy to forget that at its very core, this enormous, fiercely competitive industry is built on fostering relationships with individual clients. The financial services’ banking sector, in particular, is inherently personal.

 

It seems remarkable, then, that so many banks carry with them a stigma of impersonal business practices and clunky, frustrating user experiences. As a result of these frustrations, as many as one in four banking customers choose to switch financial institutions within their first year after joining a new bank.

 

That stigma only threatens to intensify in the coming years as a greater number of young, technologically savvy consumers begin to take control of their finances, only to experience the pitfalls of today’s largely impersonal digital-banking platforms. Banks today have a significant incentive to invest in technologies that appeal to these customers and reduce client churn.

According to recent research from Statista, more than 70% of senior banking executives cited changing “customer expectations” as the single most important factor disrupting the banking sector today. Just over half, 54%, were most concerned about increasing consumer demand for digital channels. Additionally exactly 50% of the same group pointed to disruption caused by emerging technologies such as artificial intelligence (AI) and blockchain.

The Solution

Incidentally, the KapitalWise customer relationship management platform uses cutting-edge artificial intelligence to address the issue of personalized banking.

 

KapitalWise’s core retail customer engagement platform utilizes AI to enable “hyper-personalized” customer engagement for banks. It sifts through transactional and credit data to automatically detect and predict over 60 significant credit, financial, and life events — things like planning a wedding, going on vacation, having a baby, shopping for a home, searching for a car loan, or even expecting a substantial tax return. Then, the platform turns these flags into a win-win scenario for banks and their customers alike by notifying bank relationship managers and offering recommendations for customers to adequately address those events and meet their financial goals.

 

KapitalWise also offers a similar platform for banks to improve small business customer engagement. This small business solution uses machine learning algorithms to monitor and alert bank relationship managers to more than 30 actionable small business events, such as seeking new office space, exploring mergers and acquisitions, selling a business, and making employee headcount changes.

 

Banks can further leverage the KapitalWise application program interface (API) — a framework created by the company for software developers — to build their own custom solutions for predictive marketing based on feedback from KapitalWise’s AI platform. Banks can also use the API to seamlessly integrate KapitalWise insights with their own mobile apps and online portals.

 

Going forward KapitalWise is planning to build additional products to expand its platform’s functionality, notably including new lead-generation tools, “consumer taste profiles” to more deeply analyze spending habits, and contextual plugins for Uber and Lyft to provide perspective based on customers’ real-time location and trip data.

 

What’s more, for potential clients that aren’t full-service banks, KapitalWise believes it could capture a supplemental revenue-sharing opportunity by building solutions to offer unsecured loans, insurance, and wealth solutions products to customers.

 

Finally, the company is also developing customer-facing solutions to address topics ranging from money management to robo-advisory tools, savings solutions, and budgeting to help tactfully push consumers toward achieving their financial goals.

 

The platform is garnering attention from some big names in the banking space. Though KapitalWise was only founded in 2017, the company has already secured paid pilot programs with three large banks including South Africa’s ABSA (formerly Barclays South Africa), a top-10 bank in the U.K., and one of the top three U.S. banks as measured by market capitalization at the start of this year (so likely JPMorgan, Bank of America, or Wells Fargo).

 

KapitalWise says two of these clients are still testing its platform, while the third has already exited its proof-of-concept phase and has entered early-stage discussions for a potential commercial implementation.

A Fast-Growing Niche within a Huge Market

The global financial services software industry is already immense, enjoying a five-year compound annual growth rate of more than 9% and representing more than $100 billion of enterprise spending in 2019.

 

There are favorable dynamics within that total that serve as tailwinds for KapitalWise, particularly as enterprise banking clients shift their attention to emerging technologies like AI solutions in order to reduce churn and gain an edge over rivals. 

 

According to recent IDC research, global AI spending is poised to increase at a CAGR of more than 28% for the next several years. To be clear, the bulk of that spending to date has been allocated toward banks’ automated threat intelligence and prevention systems. But KapitalWise has an opportunity to play a central role using its AI-driven “hyper-personalized” customer engagement tools to address banking executives’ aforementioned concerns for more effectively managing — and capitalizing on — clients’ changing expectations.

An Eye on Leadership

KapitalWise touts that its team has a combined 150 years of experience in the financial and technology industries.

 

Fifteen years of that total comes with KapitalWise founder, CEO, and Chief Architect Sajil Koroth. Koroth previously co-founded a pair of FinTech Startups in New York, led engineering efforts at both financial education specialist LearnVest and market data analytics company IHS Markit, and holds a Master’s degree in Computer Science from the University of Madrasas. Along the way he gained expertise in a breadth of relevant subjects ranging from wealth management to financial product development, machine learning, and data science.

 

Meanwhile, KapitalWise Chairman of the Board Paul Stamoulis brings a purer banking background to the mix, serving the better part of a decade as VP and Director at TD Securities, another decade as Managing Director and Head of multiple groups at Scotiabank, and most recently as a managing partner at private holding firm Clarim Holdings for the last three years. Stamoulis’ connections and deep banking-industry knowledge should lend themselves well to fostering KapitalWise’s success.

The Business Model

KapitalWise generated $79,000 in revenue last fiscal year, an increase of 156% year over year from $30,800 in 2018. But prospective investors should note this was non-recurring revenue that stemmed from three separate one-time payments made for a set period of use under the company’s paid pilot programs.

 

After accounting for just over $267,000 in operating expenses last year — nearly 70% of which went to developer compensation as KapitalWise continued to build out its platform — the company incurred an annual net loss in 2019 of $197,122.

 

That said, if KapitalWise can secure its first commercial implementation, that red ink could turn green quickly. Such implementations would incorporate a volume-based pricing approach ranging from $1.80 per year per customer (for banks with at least 50,000 customer accounts) to $3.00 per year per customer (for those with 10,000 or fewer customer accounts).

 

For perspective, the three large banks participating in KapitalWise’s pilot programs currently have an estimated base of more than 76 million customers.

 

KapitalWise also collects a separate fee of $1.50 per account for FICO and credit inquiry access, as well as a one-time onsite installation fee ranging from $150,000 to $300,000, depending on the scope of the integration work required.

 

Until a commercial implementation materializes, however, KapitalWise will remain reliant on a combination of capital raises (it had raised $251,850 in funding prior to this crowdfunding round), as well as less-predictable, chunky revenue contributions from any future pilot-program payments. 

The Rating

KapitalWise stands on the cusp of a mammoth opportunity to help banks address a key challenge in retaining their increasingly demanding customer bases.

 

But it’s not entirely alone in its ambition, and faces several notable competitors offering similar solutions. Namely Flybits, Kasisto, Personetics Segmint, and Strands, as well as any number of well-funded publicly traded companies like Q2 Holdings (NYSE: QTWO), could seek to develop their own comparable products.

 

At the same time, any notable progress with securing new pilot programs or commercial implementations could rapidly propel KapitalWise into the spotlight as an early leader in “hyper-personalized” banking engagement. As such, we’re designating KapitalWise as a compelling Deal to Watch for prospective investors today.

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About: Steve symington

Steve Symington is a Lead Advisor at 7investing Group, and previously wrote thousands of articles on publicly traded equities, personal finance, and investing while serving as an analyst for multiple real-money portfolio services at The Motley Fool. He holds a degree in Computer Science (with an emphasis in software systems and mathematics) from the University of Montana, and previously worked as a software engineer implementing machine-learning algorithms primarily for military and government clients.

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