Rahul Reddy BDSc (Hons) BSc FRACDS (GP)
Zauraiz Alamgeer MPhil (Hons) Eng B.Eng (Hons)

Executive Summary

Loan default is a multi-factorial issue for lending institutions which has been traditionally managed in a binary manner i.e., either the loan is paid in time, or it is not. Loan defaults result in either the cancellation of a loan or a penalty. The subsequent renegotiation of loan contracts and/or the refinancing of loans after default occurs, can be a complex and lengthy process.

This whitepaper presents a graded method of addressing loan defaults to significantly mitigate risk for lenders and protects borrowers against excessive leverage.

In addition, we will discuss:

a) Advanced KYC/AML (Know Your Customer / Anti-Money Laundering) verification also referred to as AML/CTF (Anti-Money Laundering/Counter-Terrorism Financing) [1]
b) Network collateralisation with Real World Assets (RWAs)
c) Asset tracking via the blockchain

We have determined that dentists and doctors are less likely to default on loans and have developed novel IP that will likely further reduce the incidence of “bad loans”. With the release of this whitepaper, we hope to reduce the impact of high-risk borrowing behaviour and corresponding default risk in both off-chain and on-chain environments.

The over-arching aim of our project has been to reduce the friction associated with obtaining finance within the Healthcare sector, for both health professionals and patients. To accomplish this, artificial intelligence, and smart contracts [2], [3], [4] are to be used concurrently to match the risk profile of lenders and borrowers whilst significantly increasing the processing speed of loan applications, assessments5 and disbursement of funds. The eventual goal is to facilitate rapid investment into infrastructure of the Healthcare industry to accelerate the adoption curve of a non-digital i.e., paper records to a full digital workflow and record-keeping. The value of data in digital format, which can then contribute to better diagnosis and treatment planning for patients with the assistance of AI algorithms cannot be understated. The development of our unique IP around risk management was a by-product of the efforts and the collective work of the Healthera.AI team.

1. Healthcare Finance

Many Healthcare practitioners have relied on traditional banking to service their needs. However, in more recent times, the development of online banking, digitisation of existing banking products and services, and the proliferation of neobanks has provided an opportunity in the market for alternative financing including the integration of new forms of digital currencies such as bitcoin [6].

As is commonly known, debt is initiated when funds are transferred from an entity with available capital to an entity with insufficient capital or if the business entity is seeking growth. Traditionally banks were the most trusted as intermediaries for financial debt management but all over the world banks operate according to their unpredictable risk tolerances. As a result, the cost of debt can be highly variable from jurisdiction to jurisdiction, and sometimes the loan requests are rejected altogether without adequate justification from a financial perspective. This is due to a myriad of factors however the result remains the same, a greater rate of loan rejections.

Although traditional banks perform various duties such as debt risk evaluation, the confirmation of debt amount, time allowed to return the capital along with other terms and then handling the responsibility of funds transfer from one party to another. Despite their benefits, even if there are lower transaction costs, the banks are still not impervious to other limitations such as geographical limitations. Also, consider that many developing countries don’t even have most of their population on a digital platform either through smartphone or a desktop/laptop computer. This is a large market of potential clients for debt financing that are currently being largely ignored by the global banking sector.

Decentralised Finance, or “DeFi”, refers to the emerging blockchain-based ecosystem of permissionless and transparent financial services. It is an ideal use-case of smart-contract technology for the Healthcare sector [7]. Currently, many intermediaries impede from the efficient and effective functionality of the Healthcare system notwithstanding financial institutions. Borrowing by and lending to healthcare practitioners is not frictionless. We propose a DeFi token with expanded utility for the Healthcare sector.

If staked, the token returns a fixed yield of a pre-agreed time-period to the holder of the token. This yield is to be determined by factors such as:

1) Demand for loans
2) Availability of free-floating capital (liquidity)
3) Size (quantum) of loan submitted
4) Type of underlying asset associated with the smart contract
5) Baseline (minimum) collateral provided in the form of;
i. Fiat Currencies
ii. Digital Assets i.e., cryptocurrencies
iii. Real-world Assets

These above factors are not meant to be exhaustive but will be used to develop a dynamic DeFi engine that determines the borrowing power (LTV) of a network participant, and thus disburses loans in a peer-to-peer (P2P) frictionless fashion. We have based our lending protocols on the AAVE protocol, which is highly developed and current, regarding these aspects [8].

An answer to the global financial system’s limitations is the emergence of Web 3.09 and subsequently, blockchain technology. Amongst many of its benefits, a particular acceleration towards the decentralisation of financial systems was observed. Cutting edge and state-of –the-art social networks now allow one to create financial loans outside of traditional banking infrastructures. One of those applications is P2P loans.

Some of the advantages of P2P loans is that they widen the demographic for loan applicants and overcomes the limitations related to localisation, especially with the utilisation of blockchain. Furthermore, leveraging on the nature of decentralised networks, P2P loans can maintain lower brokerage fees and ultimately lead to lower interest rates in comparison to most of the international bank rates.

The risk of default is inevitable within the field of finance, however internal data within Healthera.AI indicates that default risk is very low for doctors and dentists. Our whitepaper addresses this with a combination of a deep understanding of the underlying principles of lending, borrowing and mathematical algorithms, with the intent to over-engineer our application to both reduce thisrisk and eventually prevent defaults before they occur. We are confident that our novel methodology will assist other DeFi projects and/or traditional lending institutions.

2. Social Capital

Dr. Muhammad Yunus of the Grameen Bank described the concept of microfinance in 1974, almost 5 decades ago.10 There are numerous credit lending models, with the primary model of “Cooperatives” being employed for our platform. A co-operative, as defined on the Grameen Bank website “is an autonomous association of persons united voluntarily to meet their common economic, social, and cultural needs and aspirations through a jointly owned and democratically-controlled enterprise. Some cooperatives include member-financing and savings activities in their mandate.”11

In creating our platform, the principles of microcredit have been employed within an algorithmic/mathematical framework, with the utilisation of blockchain and AI, combined with network effects to “supercharge” his finance model.

It was determined by Dr. Yunus that P2P lending in a “one-to-many” relationship in a communitybased lending approach did not yield in significant default of loan repayments:

“Against the advice of banks and government, Yunus carried on giving out ‘micro-loans’, and in 1983 formed the Grameen Bank, meaning ‘village bank’ founded on principles of trust and solidarity. In Bangladesh by 2015, Grameen had 2,568 branches…and…collects an average of $1.5 million in weekly installments. Of the borrowers, 97% are women and over 97% of the loans are paid back, a recovery rate higher than any other banking system."

This core principle has been employed in addition to the fact that dentists, doctors, and health professionals tend to have a community-like relationship bound by collegial relationships. As a result, the combination of these factors suggests that a platform engineered with the principle of social capital built within the ecosystem would be the “first-line of-defense” to prevent the default of loans.

The advent of blockchain technology, artificial intelligence, and the convergence of both technologies has enabled the utilisation of a P2P platform to connect lenders and borrowers in a one-to-one, manyto-one and many-to-many basis, being able to execute borrow and lend requests including the corresponding disbursement of funds, within seconds.

To interact with the platform, users need to register through advanced KYC/AML mechanisms. They also need to contribute some knowledge or form of value to the platform. Knowledge can be in the form of informative, relevant, and factual healthcare related information. Educating or passing on valuable knowledge to peers on the platform would count as a contribution. Another example would be secondhand equipment to less fortunate colleagues in developing countries for a discounted price.

Over time, the contribution of social capital to the network by participants, i.e., specifically, health professionals will ensure that the risk of default remains insignificant. This presents an entirely new model of trust-based financing through leveraging large-scale P2P networks within niche industries. Without the groundbreaking work of Dr. Yunus, Satoshi Nakamoto and other cryptographers such as Wei Dai12, we could not have previously imagined the concept of “trustless” finance i.e., trust is not needed between the network participants if an immutable ledger is created to track the transactions between these network participants (users) on a moment-to-moment basis.

Borrowing to Lending Ratios in a P2P Environment

Dr Yunus’ work determined that there were ideal ratios between a borrower and lenders.

In our platform, we suggest there should be one Health Professional who is accountable (HPA) and engages with the other 5 practitioners in a lending pool. The HPA builds direct relationships with the other 5 Health Professionals Responsible (HPRs) to ensure there is cohesiveness between lending pools. Where there are 10 or more Health Professionals in a pool, cross-accountability should be spread between 2 senior/accountable HPAs. This concept can be expanded depending on the needs of the platform as it scales in size, and the ideal P2P ratio to prevent and/or reduce the risk of default will be developed with assistance from AI algorithms, over time.

The 3 Cs of Credit [13]

To reinforce the importance of this concept, Dr. Yunus also determined the “3 Cs of Credit. As below:

a) Character: means how a person has handled past debt obligations: From credit history and personal background, honesty and reliability of the borrower to pay credit debts is determined.
b) Capacity: means how much debt a borrower can comfortably handle. Income streams are analysed, and any legal obligations looked into, which could interfere with repayment.
c) Capital: means current available assets of the borrower, such as real estate, savings or investment that could be used to repay debt if income should be unavailable.

Surely, the vast majority of health professionals would satisfy the 3Cs of credit more than adequately.

3. Security Mechanisms Built-In to Ensure Safety of Funds

3.1 Customised E-wallet and Access for Health Professionals

Since Healthera.AI facilitates loans to borrowers it is only fair to ensure that there is fair usage of the platform resources and that the borrowers are legitimate. This calls for sufficient KYC and AML requirements. On a typical blockchain platform, usually all data is kept in a decentralised environment and available publicly to anyone interested. However, since we are dealing with sensitive data relating to the private and confidential information of professionals, our decentralised application (DApp) ensures that all data collected in the KYC/AML process is kept off-chain and in a private decentralised data storage on an encrypted database. This means that even the database developers cannot access the contents of the database unless otherwise authorised parties are involved.

Since regulation is an essential component of fiduciary responsibility, it is mandatory for us to collect certain levels of information from the loan applicants that give better insight to the platform for offering better options to the users and adhere to compliance. However, in a P2P environment, we will ensure that the lenders are only able to view the borrow requests anonymously and the only information available to the lender is the amount to be loaned, duration of loan and the purpose of loan. Other details relating to KYC/AML are kept private and secure unless or otherwise requested by relevant authorities or banks. This can also be referred to as “permissioned” access. However, lending and borrowing behaviour (and the corresponding data) via our DApp will not be provided to 3rd party credentialling and/or credit scoring agencies, except with the implicit and full consent of the user. To say this in another manner, the financial data will not be sold or realised for commercial benefit of any one entity or person through the Healthera.AI DApp and platform.

3.2 Advanced Smart Credentialing

We intentionally chose a low-risk population i.e., doctors and dentists within an already low-risk base of healthcare professionals. We further chose the trustable sub-population of dentists to operate on the platform, due to the Healthera.AI team’s domain expertise. Dentists, as with other health professionals, are unlikely to be intentionally dishonest with their loan repayments and behaviour. With that said, Healthera.AI will implement a method of smart credentialing to maintain and ensure that the platform excludes non-Healthcare providers from accessing the DApp to specifically submit loan applications.

One way this is achieved by allowing only those users who have a valid and active regulatory body registration number. This regulatory body in Australia is known as the Australian Health Practitioner Regulation Agency. This would not be dissimilar to other countries and for other professions.

3.3 Verification Mechanisms

Furthermore, apart from traditional One Time Password (OTP) via email address access, Healthera.AI will utilise some of the more advanced biometric based verification algorithms such as TypingDNA that makes the user type a specific sentence into a text box before initiating any transaction despite being logged on to the platform. This biometric verification analyses the specific user’s typing pattern and then utilises Artificial Intelligence (AI) to verify their identity. This includes microseconds delay in typing each letter of sentence, typing speed, error rates and forms a psychometric profile of the user based on several other useful metrics derived just from the typing behavior alone.

3.4 Real-World Asset (RWA) Collateralisation and NFTs

We are in the process of the design & development of a unique real world asset collateralisation mechanism. This utilises the technology underlying Non-Fungible Tokens (NFTs) and invoice financing functionalities along with several other risk mitigation protocols to ensure a highly collateralised network. Collateralisation of physical assets utilising NFTs is a potential way for creating digital assets that can be tracked via geolocation and/or other tracking mechanisms.

Since the equipment financed via the platform is to be used at a static address that is in a specific practice, the asset can be tracked and recovered in the worst-case scenario of default. The physically larger the asset and the less mobile i.e., commercial property, the easier it is to track. This will allow the opportunity to use RWAs as collateral in the finance process and mitigate the risk of loan default and/or theft of assets.

In addition, the HLTA decentralised autonomous organisation (which is planned) may eventually be able to better negotiate for finance rates and a myriad of other goods and services, based on the amount of collective collateral and capital it has. This markedly shifts the balance of power to digital co-operative organisations. Healthera.AI aims to build a first-of-its-kind DAO which brings together dentists, doctors, and health professionals in a highly systematic and integrated manner. By enabling decentralised finance functionality backed by real world assets, this would ensure financial stability of a token economy contributed to primarily by healthcare practitioners, for healthcare practitioners

3.5 Predicting Default Risk with Artificial Intelligence

An advantage of big data is its capability to provide trends and depth of information that humans cannot process naturally. In the future, we will be able to use advanced machine learning and AI algorithms to constantly learn from previous and existing data to draw risk profiles from the user’s information and usage trends. The platform can utilise AI to recognise high-risk behaviour and predict those at high risk of defaulting on their loans. This will trigger mechanisms to limit the amount that high-risk users can request and, in some cases, completely reject certain requests if the risk is too high.

The predictive power of A.I. can be applied to the area of decentralised finance by recognising patterns in large datasets and utilising the outcomes of base-level analysis to prevent loan default before it occurs. This would reduce the overall risk to all the network peers, but it could also enable debt to be recycled within the platform and disbursed to lenders with the capacity to hold debt for longer periods than just a single non-institutional peer.

4. Risk Diversification & Management

Individuals and/or even companies can default, even with the best intentions and for macroeconomic reasons out of their control. Therefore, if the DApp notices a user is falling behind on their repayments and once a certain amount of overdue amount has been reached, the platform protocol allows the borrower to re-finance their overdue amount with another peer lender on the platform and subsequently avoids the impact of defaults on the DeFi ecosystem. This utilises the concept of microcredit and enables a robust, dynamic environment for finance.

A risk diversification model is suggested below, however there is no reason why an individual peer cannot willingly agree to hold the greatest “weight” of debt for a requested loan (they just need to have the capital available).

5. Protocol Architechure

Healthera.AI allows its users to perform borrowing and lending activities. In traditional P2P platforms, there are three entities involved such as borrower, lender, and the platform itself. In those cases, the lender bears the maximum risk when a default is incurred.

However, we propose to use a four-entity model where the fourth entity is the loan originator. The role of loan the originator is of an intermediary entity that acts as a guarantee or a safeguard for the lenders’ funds to always be returned even in the worst-case scenario such as a default condition. This is enforced in full transparency and open-source code (for protection against malicious code being integrated into the DApp) with the help of smart contracts.

Along with that, there have been additional steps taken to further the development in terms of several layers to increment the platform’s risk mitigation strategies and enhance collateral values. The protocol architecture diagram explains that before a borrower is issued loans, their profiles are vetted through AI filters since they are required to submit their financial information and owned assets for the system to decide if they are eligible for certain loans. This is part of the platform’s proprietary KYC/AML system and all the borrowers on the platform are carefully vetted before being allowed to carry out financial transactions. This ensures additional peace of mind for lenders since the platform’s KYC/AML layer works as additional security on-top of the loan originator pool.

The architecture explains that the borrowers initially would be paying 10% interest on their loaned amounts while the lenders receive 8% APY on their funds staked in the pool. As the platform grows older and more loans are paid back, the loan originator pool will get stronger with a healthier tolerance against defaults in case any occur.

As more loans are paid back over time, the platform becomes “hyper-collateralised” at a ratio of 125% of staked funds. At any point if the funds in the pool fall below 110% of the staked value (due to bad actors), according to the concept of social capital, the entire borrower community bears the consequences, and the platform then commands higher interest rates for future loans to make up for the available collateral shortfall until the funds in the pool are again over 125% in value comparison to staked funds. On the other hand, the community also gets to benefit if the borrowers keep the loan originator pool healthy. The benefits result in lower average interest rate for all the borrowers on the platform.

We will also implement a second layer of personalised evaluation for additional security with the help of personality and behavioral analysis. This will improve over time because AI picks up patterns as time passes and is better able to predict possible future bad actors and micro-adjust interest rates on a personal level with reference to the global interest rate as baseline. AI algorithms assign a potential risk score to the borrower and their interest rates change over time for each loan. For example, when a borrower takes a new loan for the second or third time and given that their last loan was paid back on-time then that would mean that this borrower has a good potential risk score and henceforth translated into lower interest rates for their subsequent loan.

5.1 Risk over time compared to collaterisation and/or liquidity of loan originator pool (LOP)

Building on top of the concept of the LOP, the risk for lenders decreases over time as more and more dentists and doctors use the platform to take loans and then repay them back. Since each successful loan adds 2% of the loan value back to the loan originator pool, this means that the liquidity in the pool would only increase with time. For example, on average each dentist and doctors take $1M AUD loan for a 1 year tenure. They pay 10% interest on $1M and 8% of which is earned by the lenders. 2% of $1M AUD i.e., $20,000 is injected directly into the LOP and that value in turn adds more liquidity that can later be used to repay a lender even if a certain borrower has taken longer than expected to return the loan.

For example, let’s discuss the example if a certain borrower does not repay their loan. The liquidity accrued by the LOP pool provides good health to the pool and that in-turn allows the pool to be able to still guarantee repayment and rewards to the lender for their loan to the platform. The defaulted borrower however is then not allowed to use the platform then onwards so that no further harm from a particular bad actor could occur.

5.2 Network transaction fees breakdown

The network charges 0.5% of the fee each time a borrower takes out a loan as a settlement charge. For example, if a USD$1M loan is approved for a dentist with Healthera.AI, then the network would charge USD$5000 as settlement fee. Then upon each installment repayment, the network would charge 0.5% of that transaction value to the borrower. This is to ensure that the project continues to grow and develop further. The breakdown of the 0.5% charge is mentioned below in the table.

5.3 Treasure Management [14]


This covers activities devoted to HLTA market share growth, market analysis, advertisement, conferences, etc.

Technology Adoption

This includes costs needed for wider adoption of HLTA; integration with various existing blockchain platforms and projects, websites, DApps and existing software applications, deployment of Automated Teller Machines (for easy withdrawal of crypto and/or fiat currencies).

Development and Network Security

This includes costs allocated for funding core and non-core development, network security, patch management, running and maintaining the testnet, as well as developing a mainnet in the future.

Client (user) Support

This category includes client support, FAQ documentation, maintaining of web-infrastructure needed for the community and other aspects of the platform.

Organisation, Management and Legal Support

This category includes costs on team coordination, administration, management, and legal costs associated with compliance with regulatory bodies and the laws of various jurisdictions that the platform is expanded to.

General Costs

This includes costs not covered by the earlier categories, e.g., research on newer, more robust algorithms for HLTA security, external security audits, collaboration with other projects (associated cross-promotional costs) and any altruistic pursuits of the eventual DAO.

Loan Originator Pool

A certain percentage of fees should be circulated / cycled back into the Loan Originator Pool to ensure the impact of any defaults remain minimal to the corresponding liquidity and/or collateralisation of the network.

6. Liquidity and Network Collateralisation

Network participation will be incentivised for:

1) Liquidity Providers (LPs) – staked HLTA (sHLTA) i.e., 80% HLTA: 20% ADA liquidity pool. The staking rate will be determined by market conditions and will be automatically adjusted by AI algorithms in the future.
2) Provision of collateral i.e., fiat currencies, stablecoins (USDT, USDC, DAI) and real-world assets (RWAs). In principle, the more collateral that is provided to the network, the more a user will be able to correspondingly borrow.

7. Tokenomics

7.1 Microtokenomics (A new method applied to an old system)

Another feature for borrowers and lenders on the platform is a modified credit score and/or rating system. We will employ a function where each time a user transacts on the platform, it adds up a certain value to the user’s leverage score. The leverage score will increase the more a user borrows and repays those loans in a timely manner. With a higher leverage score, the borrower's maximum financing limit increases commensurately. The leverage score will be visible to lenders without initially identifying the user in detail but would still them more confidence to lend to that user.

7.2 Macrotokenomics

For a token economy to work, there must be an exchange of value in the form of capital, tokens, or physical assets. However, within our platform there are also other ways to add value to the platform:

1) As discussed above, engagement via community contribution i.e., posting relevant/educational material relevant to the digital community of peers i.e., in this case, health professionals.
2) Contribution through staking of the HLTA token i.e., providing liquidity within the platform, for competitive yields, and the deleveraged risk mechanisms previously described. We know that with a mixture of debt at varying interest rates from different types of lenders (institutional and peers), the staking rate will still be commercially viable for both borrowers and lenders, based on current client engagement.
3) Utilisation of the DApp and associated token to obtain equipment, materials, and other relevant goods and/or services that can be acquired in a co-operative manner. This has been also established another form of utility for the token in addition to the following.

8. Utility (Uses) of Token

Aside from the base-level DeFi functionality, the token can be thought of as a “community token for the Healthcare industry”.

1) Like a token used for “acknowledging the contribution of another” i.e., tipping, the HLTA token will have a use-case for user engagement on the platform i.e., discussing patient care, giving advice to colleagues and/or even contributing new ideas or thoughts to the body of clinical knowledge.
2) We can also consider the new idea that the tokens are facilitating a method to digitise Healthcare-industry assets. Whether it be the base-level HLTA token or the development of NFTs linked to RWAs, one of the core concepts of the utility of the token, is the acceleration of technological adoption by a predominantly, long-standing cottage industry.
3) In time, non-health professionals and patients interested to visit practices and health practitioners on the Healthera.AI network, may be able to use the token if accepted by the service provider, but will not have voting rights on the platform from a governance perspective. However, they will be able to provide feedback to the Healthcare Council Members (a concept to be discussed later).
4) Later, with the continuation of key collaborators within the HLTA network, token issuance would also be a joint function of the Healthcare council to acknowledge the contributions of active, helpful community members. Another way this could be interpreted is that the token would be utilised in the form of an incentive to encourage, sustain and improve the behaviour of network participants. i.e., a good borrower and lender, who also contributes to the body of clinical knowledge should be able to be remunerated directly for their work.

9. AI and Blockchain – How Do they Relate in Our Platform?

Blockchain automates and simplifies finance and AI will help to analyse the big picture financial trends of the industry, to reduce the risk of individual default of repayments in a timely and “ahead-of-time” manner (as discussed previously).

10. Allocation of Tokens

The total investment quantum required is USD$6.93M with an initial USD$3.01M required for further software development, including smart contract deployment, legal and compliance matters, audits and TestNet/MainNet launch on the Cardano blockchain this year.

11. Governance

The governance of the platform will initially be based on the contribution of value by network participants. With the passage of time, the contributors to the platform who increase the network value the most i.e., either contribute knowledge, time (which can be tracked through timestamped interaction with the platform) and energy (which can even be tracked down to the number of bytes of information or data) that are contributed to the platform, will eventually result in key stakeholders which will stand out as notable “Healthcare Council Members”.

We suggest that the top 10 contributors will be voted by governance token holders. The governance token is to be designed and launched later this year. It is suggested that the platform have, 5 individuals or token holders nominated to make pivotal decisions affecting the good governance of the platform. The first vote should be taken after the first successful IEO for HLTA.

The future DAO will decide if the Council will be expanded to include a number which will be commensurate to the total users (participants) on the network. We envision that the HLTA DAO will be the largest of its kind for the Healthcare sector, facilitating care globally whilst ensuring the costs of healthcare remain affordable and easily accessible, without a corresponding decrease in the quality of care provided.

12. The Responsible Use of Private Financial Data: Protecting the Fundamental Rights of Data Privacy

We believe in the right to privacy for all participants on the network. As a result, the lending institution or individual will have access to the most relevant, pertinent, and temporal financial information to make an unbiased decision regarding a finance application without considering factors beyond the pure financial statistics i.e., another advantage of the use-case of blockchain allowing “permissioned” access.

13. Plans for the Future

Patient Contribution Pool

From time-to-time, a situation arises where a patient is not able to afford medical treatment for several reasons i.e., cost / access issues / other unexpected factors.

As a result, a percentage of transaction fees could also be directed towards a patient financing pool for patients requiring urgent and/or critical treatment that would otherwise result in their mortality or significant morbidity.

The utility of the token could also facilitate pools of token-holders that could provide the financial means for these patients to obtain treatment. Tokens could be donated from other pools to assist a peer-to-peer network in another country.

By leveraging the community concept, peers may be able to contribute to the betterment of society, rather than in a fragmented manner as the situation currently is, with geographical boundaries defining jurisdictions rather than industry-level demarcation.

In addition to the monetary flow of funds for treatment, the voluntary contribution of time and discounted services / products to the patient should have a marked impact on patients with a lower socio-economic status. This could also be tracked and incentivised for via the platform, and the utilisation of blockchain technology again in an open, transparent, and time-stamped manner.

Combined with this and good governance, the impact of this would result in a more direct and effective use of resources, rather than some of the methods and infrastructure that currently exists i.e., ineffective government departments and/or private purely-profit driven entities.

Glossary of Terms

Smart contract - A smart contract is a set of promises, specified in digital form, including protocols within which the parties perform on these promises. It is a core component of decentralised finance and well-understood since the mid-1990s (see previous references).
LTV – Loan-to-value ratio. A financial term used traditionally by lending institutions to express the ratio of a loan to the value of the asset purchased.
DApp – Decentralised Software Application. The future direction of software on Web 3.0.

Appendix 1: Benefits of HLTA for Healthcare Professionals

Tokenisation enables the provision of capital/funds to dentists/doctors/health care professionals currently unable to:

a) Dedicate the time or capabilities to engage with a lender to provide detailed documentation of complicated tax and company structures.
b) Or obtain finance due to errors, mistakes, or long-standing unresolved legacy issues on their existing credit file.
c) Obtain finance utilising a conventional finance application pathway in a timely manner.

The Healthera.AI Education Alliance (THEA)

Upon a clinician;

a) Successfully registering on the HLTA DApp as a qualified and/or registered healthcare Professional,
b) Purchasing and/or acquiring HLTA tokens,
c) Staking the tokens for a minimum period of 3 months,

A clinician will be granted access to “THEA”. THEA will be composed of like-minded healthcare professionals who are on the quest for self-education, acquiring further learning and knowledge, who are also keen to contribute to the healthcare community.

The interest of the Alliance is to ensure truthful, timely and appropriate advice is given regarding healthcare issues affecting the global population. This organisation could have been somewhat helpful during and/or after the onset of the Covid-19 pandemic.

Taking this concept even further, THEA could establish and use an independent pool of funding within the HLTA DAO, towards students seeking education in the healthcare sector unable to afford high quality education of their own accord and/or the sponsoring of continual education and professional events, in line with Healthera.AI’s over-arching vision.

Appendix 2: Default Management (Old and New) – A Suggested Method

NB. If an asset is purchased with debt, and an intentional, malicious act is initiated by the user, then the client’s name and details are voluntarily forfeited to the DAO. The DAO can then initiate a report to the relevant authorities and reporting agencies.

In this unlikely scenario, the best and only form of recourse is “social recourse” i.e., if a financial penalty does not disincentivise the user from committing a fraudulent act, the only remaining action is to initiate legal and/or criminal proceedings against the user of the platform.

This would be implicit in the terms and conditions, before accessing the Healthera.AI DApp to obtain a cryptocurrency-only loan. We hypothesise this is less likely to occur if the client has placed some form of fiat currency and/or offered RWAs as collateral.

Appendix 3: Why the numbers of dentists globally, are an excellent set of proxy-data

Dentists are the only health professional one must see when they’re healthy as well as when they have a medical complaint. Thus, we can approximate a ratio between dentists:total number of potential patients (i.e., total global population) as an approximate proxy reference (with the understanding that health professionals would be a negligent sum of this total population). As a result, the statistics can be used two-fold for the purposes of this project:

a) As an approximate estimate of the token economy size, related to the hypothetical number of practitioners who could theoretically hold a token (or a fraction of a token).
b) An approximate proxy-measure to determine if the HLTA token is making an impact on healthcare access globally i.e., this could be measured on a yearly basis. Current data from the WHO suggests a range of 0 – 17.75 dentists per 100,000 population.

Here are some statistics that can help us extrapolate the tokenomics model:

1) Current world population in millions: 7,954 million (Approximately 8 billion)[15]
2) Mean dentists per 100,000: 8.875 (Approximately 9 per 100,000)[16]
3) Austria currently has the highest rate of doctors per 1,000 people at 5.32. [17] Extrapolated, this would equate to 532 doctors per 100,000.
4) The approximate current healthcare expenditure per capita globally is USD$1,121.80 [18].


1) Based on the current realistic achievable doctor:population ratio, Austria has a doctor:population ratio: 523 doctors per 100,000 of population. This represents a commensurate increase of 5,800% from existing reported ratios. Therefore, population of 8 billion people should have approximately 42.56M doctors. Currently, this estimate is 720,000 (based on the proxy data on dentists as stated above).
2) Based on current known data, this suggests that 43 million would see 186 patients per year, to service the entire population adequately however this is an extreme figure.
3) Anecdotal estimates by the Healthera.AI with their known domain expertise, suggests a dentist/doctor could see anywhere between 1000 – 5000 patients per year. In a 48-week year, with 5 days of work with 8 patients per day suggests 1,920 as a total number i.e., approximately 2000.
4) Thus, we can adjust for the above estimates by a factor of approximately 10 i.e., ~2000(suggested)/186 (over-exaggerated). Thus, we would approximately suggest 4.26M doctors would be a reasonable expectation of doctors globally who could hypothetically use the Healthera.AI platform.
5) If we were able to have 4.26M doctors able to see 2000 patients per year on average, who spend USD$1,213, we can estimate a total economy size at approximately USD$10T annually. The global health industry was estimated to be worth $8.45T in 2018, which falls generally in line with expectations, based on the above data. 6) Expectance of Uptake of Token vs Size of Industry

14. Credits and Acknowledgement

The authors would sincerely like to thank the following individuals for their sage guidance, wisdom and assistance during the last year, and their valuable feedback and comments during the development, finalisation and editing of this whitepaper:

1. Mr. Grant Lenaarts and the entire team at Multilateral Group
2. A senior ex-strategy consultant from Accenture who requested to remain unnamed
3. Mr. Jamie Horsburgh from Decision Intelligence Global
4. Mr. Albert Gigl from MW Partners
5. Mr. Dallas Sather LLB from Sather Legal
6. Dr. John Hagiliassis BDSc (Melb) Grad Dip (PGSD)
7. Mr Martin. Reukers from Red Connect
8. Mr. Jonathan Logan Walker
9. Mr. Carlito Luaton from Healthera.AI

Please also see Healthera.AI | Incorruptible Governance for the intended framework and principles for the Governance of the HLTA DAO.

Rahul Reddy (CEO) and Zauraiz Alamgeer (CTO)

Any feedback is welcomed and encouraged. Please send all communication to hello@healthera.ai with the subject title: “Whitepaper Feedback”. We’ll get back to you within 24 hours, otherwise call internationally on +61 2 7229 0246 and within Australia, (02) 7229 0246 if there isn’t a timely response.


1 https://parlinfo.aph.gov.au/parlInfo/download/committees/reportsen/024747/toc_pdf/Finalreport.pdf;fileTy pe=application%2Fpdf
2 Nick Szabo -- The Idea of Smart Contracts (uva.nl)
3 Nick Szabo -- Smart Contracts: Building Blocks for Digital Markets (uva.nl)
4 https://ethereum.org/669c9e2e2027310b6b3cdce6e1c52962/Ethereum_White_Paper_-_Buterin_2014.pdf
5 We do not intend to provide proof-of-consensus methodology for Credit Risk and Reporting in this whitepaper. This will be the subject of a future whitepaper.
6 bitcoin.pdf
7 What Is DeFi? A Two-Minute Explainer - YouTube
8 aave-v3-core/Aave_V3_Technical_Paper.pdf at master · aave/aave-v3-core · GitHub
9 https://coinmarketcap.com/alexandria/article/what-is-web-3-0
10 Founder – Grameen Bank
11 Credit Lending Models – Grameen Bank
12 https://b-money.cc/whitepaper.pdf
13 https://grameenbank.org/three-cs-of-credit/
14 https://iohk.io/en/research/library/papers/a-treasury-system-for-cryptocurrenciesenabling-bettercollaborative-intelligence/
15 https://www.unfpa.org/data/world-population-dashboard
16 Dentists (per 10 000 population) (who.int)
17 https://www.theglobaleconomy.com/rankings/doctors_per_1000_people/
18 https://data.worldbank.org/indicator/SH.XPD.CHEX.PC.CD?end=2019&start=2016