Gyaan DeFi Protocol

Decentralized Finance (DeFi) - Gyaan Protocol

Gyaan Protocol is a Smart DeFi protocol powered by AI which will perform all the DeFi functions, analysing the rate and to facilitate smooth transactions between the borrower and the lender. It will connect both the borrower and lender securely using the smart contracts. Securing both the interest of the borrower and lender, by minimizing the risks. Helping both borrower and lender securely execute the transactions without the need of the guarantor and third parties.

Gyaan DeFi Protocol is a DeFi protocol built in the Gyaan Ecosystem.

Gyaan DeFi protocol can accommodated and processes 3 different types of requests under Gyaan Loans

1. P2P - Borrower directly gets loan from one lender.

a. Borrower directly approaches lender.

b. Borrower submits loan requests on the platform which is visible to active lenders.

c. Borrower approaches individual small business lenders.

1. Group - Borrower gets loan from multiple lenders and

2. B2C - Borrower gets loan from small business lenders.

Gyaan Protocol facilitates the following things.

1. Lending and Borrowing for Educational Loans in Gyaan Loans.

2. Low-volatile yields on stablecoins and other reputed cryptocurrencies deposits for Liquidity Providers.

3. Gyaan rate is powered by a Gyaan DAO and other diversified stream of staking rewards from major proof-of-stake blockchains, and therefore expected to be much more stable than money market interest rates.

4. Gyaan DeFi protocol will also use latest Artificial Intelligence tools to minimize the risk encountered by current DeFi protocols.

5. Gyaan DeFi Protocol will also be offering low-volatile yields on stablecoins and other reputed cryptocurrencies deposits for providing liquidity

GYDAO token holders will be responsible for governing the Gyaan DeFi Protocol and implementing any current and future changes includes, yield rate, interest rates, additional or removable of assets as collateral, pools required etc. in shaping the future of DeFi by casting their vote for making any changes to the protocol which includes adding new or removing existing collateral types, changing interest rates improving governance itself.

. The Gyaan rate is powered by a diversified stream of staking rewards from major proof-of-stake blockchains, and therefore can be expected to be much more stable than money market interest rates. Gyaan DeFi protocol will also use latest Artificial Intelligence tools to minimize the risk encountered by other current DeFi protocols.

Different Pools on Gyaan DeFi Protocol

1. Loan Liquidity Pool - Providing Liquidity for Gyaan Loans.

2. Staking Pool - Providing Liquidity for staking rewards.

Importance of Aritificial Intelligence in Gyaan DeFi Protocol

Banks and financial institutions are increasingly utilising artificial intelligence (AI) technology for a plethora of reasons, such as credit scoring to assess a borrower’s risk more accurately or for enhancing customer service with virtual assistants. But one of the most important use cases of AI is in the fight against fraud and money laundering.

Onfido’s 2022 Identify Fraud Report has identified a concerning 47 per cent increase in identity fraud since 2019, with financial services remaining one of the highest targeted sectors.

Further research from McAfee reveals cybercrime costs the global economy $600billion annually, while consulting firm Accenture forecasts cyberattacks could cost companies $5.2trillion worldwide by 2024. Global payment card fraud losses, specifically, amounted to $28.58billion in 2020, says a Nilson report.

Payments card fraud is such a concern that the UK recently implemented tighter anti-fraud checks on card payments with new Strong Customer Authentication (SCA) rules coming into force in March 2022, activated for almost all online purchases above £25 to provide a greater level of security against fraudsters.

With cybercriminals getting ever more inventive with their malicious get-rich-quick schemes, fraud prevention and detection has never been more critical.

Protecting payments and transactions with AI

By utilising sophisticated technologies and large amounts of data via AI, financial industry members can fight against fraud in innovative ways, analysing data and training algorithms to help improve their ability to recognise fraudulent activity and tackle it quickly.

According to a global survey of over 500 financial services professionals by NVIDIA, financial institutions know all too well the key role AI can play in maintaining a competitive advantage. Eighty-three per cent of respondents of its State of AI in Financial Services study indicated AI is important to their company’s future success, while a further 34 per cent of respondents believe that AI can increase their company’s annual revenue by 20 per cent or more. Fraud detection involving payments and transactions was the top AI use case across all respondents at 31 per cent, followed by conversational AI at 28 per cent and algorithmic trading at 27 per cent.

Kevin Levitt, director of industry business development, financial services at NVIDIA

Kevin Levitt, NVIDIA

“Fraud results in significant losses to the bank and ultimately to the consumer, either indirectly through potentially increased prices for products or directly by having funds stolen from their account,” comments Kevin Levitt, director of industry business development, financial services at NVIDIA.

“Banks must stay a step ahead of the bad actors and AI is a critical tool to protect the broader financial ecosystem.”

A constant fight with fraud

Computing giant Dell Technologies agrees and suggests the cost of credit card fraud would be much higher were “payment processors not waging a constant fight against fraud using all the tools at their disposal”.

Dell’s Fighting Fraud The Smart Way – With Data Analytics and Artificial Intelligence report outlines how tools including AI and machine learning can analyse transaction data in milliseconds, weeding out the fraudulent transactions from the genuine ones.

These technologies that go into fraud prevention systems enable financial institutions to instantly analyse data, while continually training algorithms to help them improve their recognition of verified user biometrics and potentially fraudulent activities.

Parallel efforts are helping the industry ward off bad players on the merchant side. This results in a more “trustworthy transaction experience for legitimate cardholders and merchants and more digital barriers to stop the criminals who try to exploit vulnerabilities in the payment systems.”

Saving time and costs

Another key benefit of AI in fraud prevention is the provision of more cost-effective solutions. AI automation can free up manual resources that can be allocated elsewhere, leaving the AI models to do the intensive work of initial detection.

Programmes can also detect fraud and prevent it there and then, rather than weeks later when chargebacks occur. Customer experiences can be enhanced in this way, as well as reducing wait times for analysing fraud and allowing companies to respond to customers in a more timely manner.

AI in action

Michael Ross, chief product officer at Trintech

Michael Ross, chief product officer, Trintech

Trintech, a financial software-as-a-service (SaaS) firm and one of the world’s leading providers of financial close software, has invested heavily in artificial intelligence and automation, integrating these with Microsoft SQL Server running on Dell PowerEdge Servers. The AI-powered solution automates routine functions for its accounting and financial services customers.

“Trintech is in the process of building out machine learning and AI technologies that help improve controls in financial close processes to detect fraud and lower costs without increasing risk profile and still satisfying audits,” says Michael Ross, chief product officer at Trintech. “We believe this will be the next revolution in financial close automation.”

Trintech utilises AI to help its customers:

Evaluate and quantify risk across various financial close processes, entities, and functions

Automate and optimise workflows based on risk

Leverage insights into compliance controls through data analysis

Develop best practices for risk evaluation, controls, automation and optimisation, based on benchmarking data

Evaluate and quantify key market trends to determine impact and drive proactive risk prevention measures

“Over the past year, Trintech has invested heavily in its Financial Controls AI capabilities to help our customers reduce financial risk from multiple angles, save time and resources, and ultimately transform their operations,” says Ross

References

https://thefintechtimes.com/how-artificial-intelligence-is-helping-finance-firms-in-the-fight-against-fraud/

How AI is helping DeFi

https://www.mlq.ai/DeFi-meets-ai-this-week-in-ai/

AI-powered Intelligent Cryptos will Change the DeFi Ecosystem Soon?

https://www.analyticsinsight.net/ai-powered-intelligent-cryptos-will-change-the-DeFi-ecosystem-soon/

Artificial Intelligence in Decentralized Finance

https://www.trendingtopics.eu/artificial-intelligence-in-decentralized-finance/

What Does an AI Chatbot ‘Think’ About DeFi? We Asked ChatGPT

https://www.nasdaq.com/articles/what-does-an-ai-chatbot-think-about-DeFi-we-asked-chatgpt