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Arboreum Deck
- 1. “The world needsbanking, but it does not need banks” -Bill Gates
- 2. 2 Arboreum is anAI- enabled platform for receivables finance. 2 We harness the power of “small-world networks” within supply-chains to unlock credit. How? 1. We use supply-chain data to underwrite risk 2. We use the supply-chain itself to absorb part of the risk
- 3. 3 Hence networks arefundamental to our approach We can holistically underwrite using the structure of correlated time-series on a network • Decentralizable • Respects privacy • reliable credit risk assessment even if data available for an individual borrower is limited We can distribute some of the risk onto the supply-chain itself • Goods on consignment represent existing credit relationships. • Owners of outstanding receivables can earn yield simply by indicating their acceptable default risk • Securitized, diversified, senior tranche can be sold ad-hoc to a market
- 4. 02 We enable formalcredit to piggyback on informal credit Tranche N Tranche 2 Tranche 1 Arboreum investors take lowest risk, highly diversified across loans Anchor takes higher risk at below-market interest. “Staked” excess liquidity and NFTs create the junior tranche and collateral respectively. Other businesses in the value-chain can participate in different tranches Liquidity pool Investors purchase a Collateralized Loan Obligation (CLO) de-centrally minted and backed by trade receivables. 4 100+ bn $USD in stablecoin currently earning no yield can safely earn 9-20% APR financing real-world business activities We are empowering businesses to finance themselves ad-hoc directly from de-fi liquidity pools Liquidity Pool NFT NFT NFT 1. A credit-worthy “anchor” provides the junior tranche 2. NFTs representing the borrower’s receivables are minted 3. The liquidity pool provides remaining required capital Our AI and underwriting tranches debt as to maximize returns and minimize risk for the liquidity pool
- 5. Step 1: thecredit union (in the market) 5 Arboreum uses the anchor’s data to underwrite the financing provided to union members. Credit union Credit union Individuals and/or institutions purchase senior–tranche, securitized union debt. Members borrow, preferably against business they’re already doing with the anchor (invoices, salary slips, warehouse receipts). A minimum of 20% if provided through Junior-tranche debt which comes from the union. External Lenders Borrowers Arboreum is building credit unions as a service, hinging on a credit-worthy “anchor” The credit union serves as the junior- tranche that co-lends funds, and smooths cash-flows by acting as a ballast or buffer of funds The credit-union is capitalized by member-deposits and Arboreum investors
- 6. Step 2: Tokenizationof receivables (in progress) 6 Through their generous support ($75K grant + $25K credits) we are moving our product onto the Algorand blockchain. ACH 1. Receivables can be paid via multiple rails (bank transfer, credit/debit cards, Automated Clearing House). Circle is merging the world of traditional banking with blockchains. Any customer wishing to use our platform would be required to use Circle for their receivables. Ergo: 2. Funds are collected in a Circle- managed wallet denominated in USDC. 3. Smart-contracts can be written against the wallet, minting various tokens on the Algorand blockchain. NFT Receivables Investment Asset Reputation 4. Funds can be converted to fiat either directly via Circle or using any other exchange 1. Receivables can be tokenized in a digital native manner. 2. We can accommodate far more complex structures than the credit-union. We can organize credit along real-world supply chain networks.
- 7. Step 3: Fullautomation with decentralized AI 7 Just as networked AIs + Battery Storage have created the decentralized utility company; we can create a true decentralized trade bank! Each node-level AI needs just a few simple pieces of information: 1. Hurdle rate & target rate of return 2. Excess liquidity & receivables on hand 3. Max time period liquidity can be encumbered 4. Max acceptable discount rate on receivables Once on a network the AIs in concert can solve a sophisticated risk-management problem all through the process of novation: 1. Safely circuit excess liquidity and provisions collateral to mitigate supply-chain frictions 2. Borrow funds/sell receivables directly from a market to cover shortfalls 3. Enable a self-powered trade-finance system running on blockchain + Arboreum Intelligence We are building a plug-and-play solution to become the go-to credit facilitator for independent networks such as marketplaces We have built a working Proof of Concept of this technology.
- 8. Our Current CoreAssets 8 Receivables-financing front- end and back-end application (in production) Conditional Copula Tail- Expectation portfolio optimization – proprietary algorithm specifically designed for the exigencies of small, short-term loans as the underlying basket of assets. Proprietary underwriting algorithms based on the structure of correlated time-series on a network. “Tetris Algorithm” - - easily tranches payments and mints CLOs with almost any desired number of tranches and payment schedule. Algorithm is designed for decentralization (please see our whitepaper to learn more)
- 9. Gaurav Singhal |CEO Lead Data Scientist, United Nations WFP Machine Learning Engineer, Accenture Technology Labs Core Team Junior Fellow, William Davidson Institute Double MS, University of Michigan (Economics & Information Science). . 9 Dr. Avinash Bhardwaj | Technical Advisor Professor of Engineering, IIT Bombay PhD Operations Research, UC Berkeley BTech, IIT Delhi Dr. Igor Rivin | Technical Advisor Former Regius Professor of Mathematics Creator of Mathematica Kernel https://en.wikipedia.org/wiki/Igor_ Rivin PhD Mathematics, Princeton Vishal Hemrajani | Engineering Lead Engineering Lead, Valinor Earth CTO, Flameback Capital BE Electronics & Instrumentation, BITS Pilani Julius Faber | Founding Engineer Full-Stack Blockchain Dev, Neufund Full-Stack Blockchain Dev, Consensys MS Robotics & ML, TU Berlin BS Computer Science, TU Berlin Marc Held | Strategy Advisor Built & sold multiple companies in the Supply Chain + IOT space, currently CEO & Founder, Fishtail.ai BS Computer Science, Northeastern
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- 11. Phase 1: ReceivablesFinance (Mostly Centralised) The current goal is to build a scalable receivables finance solution for MSMEs connecting to liquidity pools for funds NFT NFT NFT 1. NFTs representing the borrower’s receivables are minted Tranche N Tranche 2 Tranche 1 NFT NFT NFT NFT NFT NFT 2. Those NFTs get tranched, pooled, & divided to create different CLOs (centralized) Liquidity pool investors take lowest risk, highly diversified across loans Anchor takes higher risk at below-market interest. Other businesses in the value-chain can participate in different tranches Liquidity Pool 3. Different CLO tokens are minted with the appropriate repayment terms and distribution of profits controlled by smart-contract.
- 12. Phase 1: Fiat<-> Crypto is as its core End borrowers get fiat and repay in fiat – goal is for them to operate their business insofar as possible with minimal disruption. They issue invoices and expect to be paid in fiat. Tranche N Tranche 2 Tranche 1 Liquidity Pool 1. When the loan is approved stablecoin is automatically taken from the liquidity pool distributed in a manner as specified by the original smart- contract 2. Stablecoin is converted to fiat and put into an escrow account with a traditional bank. 3. Borrower receives and repays in fiat. 4. Repaid funds is converted back to stablecoin. 5. Stablecoin + profits distributed back to tokenholders as originally specified..
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- 14. Arboreum holistically underwritesthe borrower Borrower Supporter (degree 1 trustors) We let the network reach a “consensus” (not every node must exactly agree) about the risk a borrower poses over the tenor primarily by assessing cashflows and correlation between cashflows.
- 15. Principles: Generalized MethodsAlways Win The bitter lesson of 70 years of AI research: general methods leveraging computation are the most effective, and by a large margin – incorporating human, domain-specific, knowledge tends to over-complicate and under-perform human underwriting is obsolete Principle 1: The best predictor(s) (especially short- term) of a given time-series are other time-series’ Principle 2: In an ad-hoc credit-network there exist actually two kinds of risk we must measure 1. Creditworthiness – what is the probability that the node returns the money 2. Credibility – how reliable is the information a node is providing on another node Signal A Signal B Signal C Beyond correlation, wavelets allow us to study how one time- series modulates another over multiple different time scales P(Credit- worthine ss) P(Credi- bility P(Creditworthiness | Credibility) Principle 3: All knowledge is Bayesian; everything is a stochastic random variable (i.e. its probability distributions all the way down) Specifically we use Beta distributions to describe these random-variables we are building a Bayesian Reputation System on an ad-hoc network
- 16. (1) Trusted Seednodes incorporate off-chain data and broadcast an initial estimate Trusted nodes rate the borrower and produce a rough estimate using a reinforcement learning model A 3 B 2 C 2 Borrower Seed Node We use a hazard-model in a reinforcement- learning framework. We take a vector of characteristics X, previous cash-flows Yt-1 and produce a best guess of loan default over time for a given amount. X varies from situation to situation. For example with our logistics-partner pilot we have access to all their historical consignment data.
- 17. (2) Each nodemakes its own prediction & computes neighbour’s credibility Each node computes the creditworthiness of the borrower using Bayesian wavelet regression and forecasting Each node computes its neighbour’s credibility using a simple Bayesian model relying on two inputs: 1. How close is my neighbour to the borrower? This is determined from: • Network distance between node and borrower (the closer distance, the more related they should be in real-life) • Correlation of cash-flows with the borrower. 2. What is the historical performance of their predictions? Wavelets are a powerful, generalizable time-series modeling technique that work well with minimal assumptions across different temporal scales. Wavelet regression methods often out-perform LSTMs while matching their black-box convenience.
- 18. (3) The nodeupdates the “weight” of its own forecast (sensor fusion by Bayesian Kalman Filtering) Each node is trying to minimize its own potential inaccuracy compares the errors of their neighbours’ models against its own Forecast Weight
- 19. (3) As theassessment propagates, each node updates with its own assessment and broadcasts Each node combines the creditworthiness message of their neighbours with their own internal credibility measure of their neighbours
- 20. Better risk assessment Whatis unique about Arboreum’s risk assessment Leverages the power of the network (the signals provided by the credit-worthiness of friends, the amount of trust that they have placed, and the capacity of credit flows on the network) to assess risk Uses peer-enforcement mechanisms to lower default risk by having friends (trustors) participate in the loan by design Continuously updates risk through Machine Learning with Bayesian modelling + Neural Networks; multiple external and internal sources of information used for this Understands correlations between loans/risk/borrowers e.g. what is the probability you default if your friend's friend defaults ~ 10% reduced risk of default ~ 80% increase in your default probability 0.5 to 1.35% reduction in interest rate for borrowers 5.5% relative increase in risk adjusted returns for lenders If a friend contributes to your loan… It has been mathematically proven1 that large networks with high connectivity result in lower interest for borrowers and increased high-quality opportunities for lenders If a friend defaults... Why bring in the societal trust and network concept 1 literature can be shared upon request ~ 0.9 correlation in likelihood of default if default risk was already low
- 21. 21 Risk Management & PortfolioOptimization (Phase 1 & 2)
- 22. Phase 1 &2: Optimization-based Protocol We currently implement a proprietary anti-fragile portfolio optimization approach https://innolution.com/blog/agile-risk-management-antifragile Goal: we want to create portfolios such that volatility in the portfolio creates minimal losses https://goodup.com/who-will-turn-out-to-be-antifragile/ Antifragility is a characteristic of things that gain from disorder
- 23. Antifragile ConditionalTail Dependence One way to measure anti-fragility of a portfolio is the dispersion of the assets in the tails https://analystprep.com/study-notes/frm/part-1/quantitative-analysis/correlations-and-copulas/ The portfolio with weak tail dependence is more robust than one with strong tail dependence This portfolio can be considered anti-fragile as dispersion increases in the tails We use a soft clustering technique to determine weights such that tail-dependence is minimized
- 24. Phase 3: NeuralNetwork-based Protocol This essentially gives us the core functionality required to create not only truly decentralized credit- systems but also general-purpose self-adaptive, intelligent networks that can solve distribution and coordination problems. In the future we will transition to a Reinforcement learning approach where portfolio weights are dynamically learned from the joint-moments and spectral properties of the risk distributions of assets
- 25. 25 Phase 3: DecentralizedAI - Fundamental Principles
- 26. Risk distribution logic Fundsfrom node-to-node, entities that know each other off-chain and opt-in to trust This trust is the fundamentally used as an indicator of your creditworthiness Ergo: Default also affects the credit worthiness of those who opted to trust you - a proven powerful enforcement mechanism Funds flow on the basis of indicated trust 05 Lenders closest to the borrower: • Have indicated trust in the borrower • Consequently, they bear the highest risk and enjoy the best interest rate from the loan Lenders farther away from borrower: • Don’t directly know the borrower • So, their loan is more collateralized so it can be safer • Receive lower interest rates than those close to borrower Bob i=8% c=20% Eli i=7% c=30% Finn i=4% c=60% Numbers shown are interest rate i and collateralization c as output of a simulation
- 27. What happens underthe hood At the heart of Arboreum is the ability for any blockchain node to (1) send messages, (2) coalesce messages; (3) compute new messages. What makes the network intelligent? These messages can themselves be mathematical functions! Machine-learning algorithms compute these messages from coalescing input messages and computing their desired optimal state, before passing the new message along. AI Message Function Description 1 Assess Risk 2 Collate & Compute Demand Function 3 Novation Alice Bob Charlie Eric Donna The respective borrowing/lending histories of nodes and the trust amounts are used Wave 3 to construct a probability distribution representing their best guess of the risk of default. compute a function equating the amount of the loan they will buy given rate (R), tenor (T) and collateralization level (C). Given a view of the loan risk and their neighbors' demand functions as well, the AI can then i.e. as the loan is handed off in smaller and smaller amounts from neighbor to neighbor the rate, tenor, and collateral are altered to decrease risk in exchange for less interest. R1 C1 T1 R2 C2 T2 The bond is propagated through the process of novation; Wants A $1000 Loan Commits $200 @ r=8%,c=20% Commits $50 @ r=5%,c=40% Receives Loan @ r=8%,c=20% Eric decides to not participate due to risky terms Commits $25 @ r=4%,c=60% These messages propagate across the network in 3 consecutive waves, allowing the network to intelligently distribute the loan Wave 1 Risk Assessment Messages Bob assesses Alice's risk profile and passes that as a message to Charlie, who repeats this whilst adding in the information he knows about Bob. This continues up the chain until the message reaches Eric. Wave 2 Demand Function Messages Given his view of Alice's risk, Eric computes his demand function and passes it along as a message to Donna who does the same, knowing there is a possibility to novate some of Alice's loan to Eric. This continues back up the chain until it reaches Alice. Wave 3 Novation Messages Alice chooses from the options presented to her by combining the demand functions of her neighbors, then the loan is novated back up the chain and accounts are adjusted. Wave 1 Wave 2
- 28. Under the Hood– An Example Loan 06 M A6 A5 A4 A3 A2 A1 B6 B1 B2 B3 B4 B5 C1 C2 C3 C4 C5 C6 $200, r=8%,c=20% r represents the interest rate, and c represents the collateralization ratio $100, r=8%,c=20% $50, r=4%,c=60% As seen in this example, when the loan propagates it is novated as follows: A chain of accountability from creditor to borrower is created, resulting in a proven, highly-effective, peer-enforcement mechanism that incentivizes loan repayment. Interest Rate Collateralization Rate Risk-Adjusted Return Steps Away From Loan Originator Hence when default occurs, losses to lenders accrue as follows: Collateral Amount % Of Loan Held By Lender (Principal Owed) Losses Steps Away From Loan Originator
- 29. Less risk andlower spreads Our simulated network consists of 125 people, each with different risk tolerances and amount in wallet In all scenarios, we assume that the borrower is Ram, he wants to borrow INR 25K, for a duration of 1 year, is willing to pay an interest rate of at max 20%, and he has 5 immediate trustors on the network We make the assumption that Ram is comfortable committing 25% as guarantee in case he defaults1 1 Social lending scenarios varied by degrees of separation from borrower 4 5 2 3 Number of loan contributors Interest rate, % Average individual risk of immediate trustors, INR For Ram For network Simulation outputs for a single loan 1 Details of how to manage guarantee commitment while respecting RBI regulation to discussed 40% 22% 14% 12% 5 20 27 42 110 4000 3300 2250 2000 31 17 13 11 Risk-adjusted return, % -9 -5 -1 -1 AI solves for optimal risk-adjusted distribution of any loan over the network, while respecting the individual risk tolerances of all in the network. Benefits increase as the loan spreads farther and farther. Risk premium as adjustment to interest rate, % n/a as only INR 10k are available from Ram’s immediate trustors n/a AI will propose 12% interest rate as it meets Ram’s requirement while giving all lenders significantly safer loans This is the power of social lending. Smart contracts will spread the loan over people till 5 deg of separation away.