ArtInvest – (Rebel Alliance) – Integrating Blockchain and Machine Learning Analytics in the Alternative Asset Space – $200K

Background – Art Industry Overview  

88% of wealth managers believe arts and collectibles should be offered as part of their wealth management services. In order to understand how art trading fits into the wealth management services we must first understand how the art market works and its key players:


  • Art Galleries: represent artists and sell their art, they determine and manipulate price in the primary market
  • Buyers: museums/galleries or high-net worth individuals who collect and sell art for both personal and investment related reasons
  • Auctions houses: provide appraisal experts and venues for selling art to the public
  • Wealth Managers: art buying/selling is increasing in popularity among wealth managers as another investment option for clients

There are two segments in the art market, the elite end (galleries) and low tier (small unknown local galleries outside urban areas where prices are listed and observable). Galleries represent the primary market in the elite end segment, they determine the price (which is hidden from the public) and maintains control of the price by manipulating the secondary market comprised of auction houses who make price public and sell to the highest bidder, and owners who sell their art previously bought from galleries or auction houses.

Online art market

The global online art market reached $5.4B in 2017, accounting for 8% of the value of global sales. Auctions represent 47% of sales and dealers 53%. Most of them surveyed in 2017 recognized the online channel as a key area of growth for the next 5 years.

Source: The Art Market Report 2018 by Art Basel and UBS

Opportunity – Where are there pain points in the current system?

Currently pain points exist in every segment of the art trade value chain:

  1. Authentication & proof of ownership: The lack of a central title agency to track past ownership, as well as the reliance on human judgement, make establishing authenticity difficult and prone to many errors.
  2. Price valuation: The current art trading market is not liquid, and in many cases art is not sold in auction. This makes the price valuation of art highly arbitrary.
  3. Utilization: The current system of ownership is based on one owner utilizing one piece of art, this does not fully make use of the value of the art.
  4. Art investments: Even though current infrastructure exists to enable investment in arts, there is no system to support decision-making and prediction of future returns and risks.

Proposed Solution

Our proposed solution has several integrated components, each of which is comprised of existing technology that is in almost all cases open-source and available for repurposing or is reasonably replicable.

Machine Learning Platform : We see two uses, authentication and predictive analytics. Using tools available via the Google Cloud Platform, our team believes a highly accurate machine-vision model could be trained using image data collected in high definition to create a model that can identify forgeries on a going forward basis for any onboarded Art with near 100% accuracy. A recent paper by Elgammal, Yang, and Den Leew of Rutgers and the Atelier for Restoration & Research of Paintings recently used a RNN in ensemble with a “handcrafted” supervised model to achieve 100% forgery recognition on drawings. Companies Verisart and Dust both currently use machine vision to create a digital token or certificate for such assets now, and could be partnered with if the technology proves too difficult to deploy internally.

With regards to analytics, we see company Artrendex currently uses a dataset of extant artwork to formulate categories for artwork that is useful for trend identification. We believe continuing this form of research into these assets could provide insights into expected returns. Moreover, we feel that the integration of those expectations with Monte Carlo processes and traditional portfolio theory (which are employed in the structured product businesses currently) could liken this asset class to more traditional financial products. In so doing, it could be made more readily understandable and optimizable by Wealth Managers.

Distributed Ledger: Here, the Blockchain element of ArtInvest’s platform plays a role in allowing for liquidity to form for individual art pieces as ownership stakes can easily be traded. This allows for the secondary market to give accurate MTM values for pieces in more frequency than auctions and even allows for derivative products so as to hedge positions. Moreover, the operational benefits of a smartcontract enabled platform would make transaction verification and recording robust. Incorporating the requisite tax and regulatory frameworks becomes easy as it can be embedded directly into the contract logic. Else, Wealth Managers can use their existing infrastructure as an overlay to ensure compliance.

The solution is made scalable by implementing machine learning processes on the cloud and operational processes on a permissioned distributed ledger on which tokenized works can be traded. In each case, this can be done with almost entirely open-source software.  



  • Transparency – Sales recorded and traceable through the blockchain, ensuring information on past ownership and prices
  • Liquidity – Selling shares of art increases transactions, increasing access to art ownership and accuracy of prices
  • Regulation – Creating a standardized process for art sales can allow for greater guidelines and regulation for the industry as a whole
  • Investment Opportunities – Increased transparency and liquidity will allow Wealth Managers to provide art investment as a trusted and beneficial investment tool in their portfolios


  • Selling physical object on a virtual platform – Sellers can authenticate item and then deliver forgery at the time of sale, leading to potential trust issues with our platform
  • Resistance in the current market – Current market is full of high net worth individuals who prefer anonymity and less regulation, which could prevent these individuals from using our product
  • Lack of support from auction houses and appraisers – Auction houses and appraisers might see our process as replacing their line of business, especially if the need for appraisers decreases with our new authentication tool. We will need these market participants support to ensure our product is funded and trusted by the art selling community

Commercial Promise – What Appropriable Value is There?

ArtInvest’s initial target customer segments are dealers and wealth managers. Revenue will be generated through two main streams: rental fees from leasing out the artwork on the platform and fees from providing market intelligence and data analytics services to wealth managers.

Market opportunity for the art leasing market:

In 2017, the fine art market recorded $63.7 billion worth of sales, from 39 million transactions. Each of these art pieces sold required appraisals and validations at least once, requiring extensive time, effort, and money to sell the pieces. With ArtInvest and its machine-vision technology in identifying forgeries, we plan to help dealers and wealth managers reduce acquisition costs by 50%. We conservatively estimate that capturing 0.5% of overall transaction volumes over five years would bring in annual transaction value of $347 million by Year 5, bringing in more than $9 million in rental fees.

Market opportunity for providing art data analytics services:

In 2017, according to a study by Deloitte 88% of wealth managers said that art and collectibles should be included as a part of wealth management offering. In 2016, $1.6 trillion USD of ultra-high net worth wealth was allocated to arts and collectibles, and this is expected to grow to $2.7 trillion in 2026. We estimate that providing investment management tools to these wealth managers, based on a 2% fee would yield more than $6 million annually.

Given the potential size of the industry (1.7 trillion with $>50 billion annual sales), capturing even a small market share would bring significant incremental revenue to the company, and we foresee that with the network effect, the platform will see exponential growth once a critical amount of artworks are registered.


Potential Competition

The competition currently consists of several small ventures, largely based in London, which focus on different elements of the opportunity set. Verisart and Artrendex are startups focusing on art verification and token creation, while Codex and Maecenas are blockchain platforms , as is ArtStaq, our most similar competitor. Currently, each of these companies target the end user or “consumer” but no company is focused on developing a B2B model. ArtInvest would hope to use its technology as a backend service to existing Wealth Management firms to better leverage their expertise and existing client base but providing them a similar service as our competitors do for individuals.

Funding Needs

The estimated cost to build the platform in Year 0 is $1.05 million, the majority of which is the cost of hiring engineers. In the initial 3 months, we aim to build a minimum viable product with the blockchain and machine learning set up and a beta application, which will be tested by selected users in the art industry. The estimated cost to build the MVP is $200,000.


*Revenue includes: Rental fees, fees from investment management, storage fees and insurance fees. Note that both storage fees and insurance fees are expensed and do not contribute to the bottom line.


Market Size:

Cost of Authentication:

Codex Blockchain White Paper:

Artrendex – Computer Vision which Verifies Art by Brush Strokes

Machine Learning Technique:

Provenance Guide:

Current Technology:


Appraisal Info:

Art Investment Funds:

Tax information:

Code from GitHub:


Play Business – connecting investors with early stage startups

Company Background

Founded in 2014, Play Business (PB) is the first and largest equity crowdfunding platform in Mexico. It targets first round investment for non-accredited investors and helps startups get to institutional investment faster while giving investors the opportunity to have equity in the most innovative startups in LatAm.

While other Fintech investment companies or platforms utilize machine learning for credit models to determine their lending rates and amounts, Play Business utilizes machine learning in marketing efforts towards entrepreneurs and investors as well as for investment recommendations based on past investments and profile data.

Participants – Non-Accredited Investors (Buyers) / Entrepreneurs (Sellers)

Value Unit – Investment and ROE

Filter – Risk Tolerance and Investment Thesis Fit

Play Business’ website:

Problems & Opportunities

Investing within the PB ecosystem is easy, investors only select a startup and an amount to invest, and fill the required information.

However, PB and other equity crowdfunding platforms face one main problem: most users are unfamiliar with evaluating the quality and reliability of early stage investments. This can lead to excessive research time and undesirable investment results for the investor, who is in need of further guidance to find a good match. Currently, a list of proposed investments populates based on a “liking” system that bubbles up popular choices and a “favoriting” system to indicate investments of interest. Examining the startup side of the platform, little is available in terms of investor selection. Currently, the matching does not seem capable of high degrees of customization.

Effectiveness and Commercial Promise

Improving the match algorithm for PB adds value as a platform in the following ways:

  • Entrepreneurs benefit from getting faster funding and access to new capital without VCs’ meticulous requirements
  • Investors benefit from having a new avenue to invest their money that was not previously available to them
  • Overall, the public benefits from bringing to life ideas and innovation that address social, business and infrastructure problems
  • Ease-of-use has greater value for the built-in compliance of platform contracts with Mexican Securities Law

PB commercializes this platform by taking a 5% cut if startups achieve their funding target, aligning the incentives of the platform with the success of businesses achieving funding.


PB currently faces two types of competitors:

  1. Equity-based crowdfunding platforms in the domestic market: These include local players such as Propeler, Crowdfunder and However, PB remains the largest equity-based crowdfunding platform in Mexico, with the largest number of startups and businesses listed on its platform, and its small minimum ticket size allowing it to reach the largest number of investors.
  2. Other types of crowdfunding platforms operating in Mexico: These include donation-based crowdfunding platforms, such as Donadora por Fondeadora, reward-based crowdfunding, such as Kickstarter.

Improvements, Suggestions

We propose improving their match algorithm by increasing preference input on both investor and startup side and creating a “star” rating system rather than a binary “like” system. This would require both investors and entrepreneurs to create a detailed profile including variables such as industry, investment thesis, expertise, stage of the business, etc. In addition, we could rate data related to team consistency or investor commitment. Considering all the above, a matching algorithm would deliver a limited list of investors and ventures organized by best matches to the customers. In terms of adding onto the selection for both sides of the platform, we recommend taking advantage of machine learning enhanced marketing tools via Google or Facebook to find new potential investors. For each startup, due to the small investment size, many more investors are needed.


Cyber-security breaches remain one of the biggest risks in the crowdfunding space. Secondly, a notable increase in default rates and business failures due to macroeconomic conditions or turmoil in the financial markets could lower general risk-taking appetite, affecting the usage of the PB platform. Lastly, a potential collapse of a well-known platform due to malpractice would pose reputational risk to the industry as a whole. Further, while PB is well positioned from a regulatory point of view any changes therein could be problematic.


2017 Americas Alternative Finance Industry Report: Hitting Stride

Derivative Risk Control – Augmented Judgment


Financial Broker-Dealers such as Goldman Sachs, JP Morgan, and Morgan Stanley are constantly monitoring their trading activity for errors, particularly in the Over-the-Counter or OTC market, where trades are done off of exchanges and are bilateral agreements between parties with bespoke terms.

Simple, “vanilla” products such as Calls and Puts may have as many as 15 relevant fields, but structured products and exotic derivatives may have upwards of 100. Each field is negotiated and confirmed with counterparties on the “street” in a multi-level proofreading and verification process that can take anywhere from three days to two weeks or more. Errors on the direction of a trade, the underlying asset a derivative references, the size, strike, or expiration date are all relevant economic factors that can significantly damage the profit margin, reputation, and volume capacity of a given securities business.

It is in the interest of Investment Banks to mitigate the risk they incur within this OTC market by intelligently sensing where potential risk may lie and devoting resources accordingly. However, the current model involves visual, manual reconciliations performed by specialists on an ad hoc, continual basis with each trade, and field, reviewed as it comes.

 Process for Confirming and Controlling OTC Trades Currently:

Generic Sample OTC Option Ticket:


Commercial promise and challenges:

According to the Bank of International Settlements (BIS) the outstanding notional amount of OTC derivatives exceeded $416 Trillion globally, with a gross market value of $13 Trillion.

Utilizing these assumptions as well as error rates and sizes provided by conversations with industry experts we create an estimated total addressable market and assign a value to the potential industry savings annually per incremental improvement in error reduction:

Number of Major Broker-Dealers 25
Average Transactions 100
Market Transactions / Day: 2,500.00
Yearly Transactions: 625,000.00
Gross Market Value: $   13,000,000,000,000.00
Error Rate: 3.0%
Average Value of Error: $                              5,000.00
Annual Error Cost: $                   93,750,000.00

Hence, a reduction in the error rate by 1% and error size by $1,000 would yield annual savings of approximately ~$44mm, whereas a 2% reduction and $3,000 reduction saves ~$80mm. Thus, even a small market penetration rate with moderate success could be significant.

The principal challenge would be in getting Investment Banks, which closely guard their internal affairs, to be willing to integrate an outside vendor’s technology in their operations without having direct control over that entity and in addition having that entity service other banks. Furthermore, the usage of this technology to reduce error rates in an opaque and poorly-understood market, which is the reason for the opportunity in the first place, does not lend itself well to clear-cut spending decisions. Therefore getting buy-in for COO’s and CTO’s will be a lengthy process and require imaginative data visualizations and proof of data security.


We face competition on two fronts, internal to Broker-Dealers and externally in the form of “RegTech” companies seeking to provide tailored AI Solutions. Internally, there are significant resources available to banks within their IT departments, however, the deployment of those resources to such targeted AI solutions is limited at current due to the intense product-specific knowledge requirements that are often siloed. Externally, there are numerous RegTech companies which primarily focus on compliance rather than financial risk. AQMetrics and Ancoa, which received the 2016 Operational Risk Award, both are focused on automated auditing of transactional data and real-time trader behavior surveillance. These companies thus pose the greatest direct threat.

Proposed alteration:

Model Design:

  1. Data from email, chat, firm booking systems, issue trackers, integrated in a pipeline to create a dataset for analysis. Historical testing and scenario analysis used to create weightings of risk for fields/attributes.
  2. Using Supervised Learning we will use cross-validation and other techniques to tune hyperparameters on predictive indicators of the risk a particular trade has
  3. We believe this should be constructed as follows:

Risk (1-100) = F(Trader Behavior, Objective Risk)

Trader Behavior = F(Historical Error Rate, Estimated Error Rate)

Estimated Error Rate = F(Trade Complexity, Number of Trades, Time of Day, Experience Level, etc)

  1. Issues will be prioritized based on the risk construct and highlighted for further investigation


The model will create informative risk parameters for each trade that will be incorporated into the prioritization of the item either in terms of rigor of checks (can be automated via email/UI interface) or legacy human-performed checks. Large-significance errors will be disproportionately targeted by the model’s tuning. Long-term, the risk identification process will precede trade booking and help establish a more rigorous booking process at the origin. This will skew control processes to reduce error size and frequency measurably.


Mean Error Size and Error rate estimated based on conversations with Operations Professionals from Goldman Sachs and UBS Securities