We took our inspiration from the decentralized nature of Web 3.0. Neo-Zapit will allow individuals from all walks of life to lend and help individuals in financial difficulty. Similar services available charge individuals unsustainable rates of interest which does not address the problem of financial difficulty for the borrower. Our credit score card system allows for identification of credit worthiness to ensure investor funds are not at risk through making use of the service. Our UI takes inspiration from modern dating applications with investors and borrowers given the option to select a potential match depending on the interest rate, term and amount of the loan.
What it does
Neo-Zapit takes a number of variables input by potential borrowers and investors and aligns their needs based on matching inputs. Neo-Zapit creates a peer to peer network of individuals will to invest in potential borrowers and enables lending across boarders. Our machine learning function addresses the risk associated with lending and determines an individuals credit worthiness based on a range of factors taken into consideration for our score card model. Our model is based on lending and issuing of fiat for crpyto currency, this process is in its infancy currently. We aim to provide individuals the opportunity to mitigate price risk through lending fiat + interest for crypto. An example can be used with Neo, an individual holds 100 Neo however requires money to pay his upcoming rent. The value of his 100 Neo currently stands at $10,000. Our model will allow an investor to lend this person $1000 + interest of say 10% using 11 Neo as collateral for the loan. The borrower agrees to repay his debt of $1100 to the lender and upon payment receives his 11 Neo in return for full payment. This is managed through a smart contract agreement upon issuance of the Debt and 10 Neo collateral.
How we built it
We built Neo-Zapit using React.js or wallet connection will be managed through WalletConnect web frame work, Microsoft Azure web application and Postgres database on Azure. Our machine learning model has been created using Jupyter notebook. Our market research conducted via an anonymous survey provided results from 80 individuals and provided feedback that individuals are willing to lend to others via platform. Predominant feedback was risk aversion and methods of capital being collected. Neo-Zapit services this market through our gig economy model.
Challenges we ran into
Development of the smart contracts to connect to our platform. We have a front end built which allows users to visit the site. We require further development to achieve our desired functionality. Further discussion is required with individuals who have previous experience in decentralized finance, our model requires further refinement should the value of the 100 Neo in the example above drop below the price of the loan and in turn the borrowers collateral is below the value of the loan and removes the incentive to repay the debt. This can be mitigated through a more centralized approach with connection to credit scoring mechanisms and transparency within the platform.
Accomplishments that we're proud of
We are proud to have achieved our goal of making use of platform the leverage our product. We have a deeper understanding of technologies required to progress our platform and our Machine learning model will aid in risk mitigation.
What we learned
We learnt much about credit scoring and Machine Learning and methods by which to rate credit worthiness. Our ambition is to enable lending to multiple regions and allow capital access to areas not serviced previously. Leveraging the peer to peer aspect of Web 3.0 we are confident in our approach to Neo-Zapit's use case and potential.
What's next for Neo-Zapit
Next for Neo-Zapit is to integrate payment processing and further develop our Machine Learning model to mitigate unnecessary risk associated with the platform and ensure lenders and borrowers feel comfortable using the service. Develop out smart contract functionality with the aid of the Neo development team and review our lending model to ensure minimum risk exposure for lenders should collateral reduce in value during life time of loan. Should borrower default without restructure collateral is defaulted to lender + any payments made.