Embedding financial intelligence in machines
With the rising popularity of electric vehicles and the efficiency of blockchain technology, this AI system combines the power of sophisticated technologies and delivers fully autonomous machines embedded with financial intelligence. The AI system self-negotiates a smart contract using IOTA with multiple charging stations at the nearby locations on behalf of the vehicle owner. The vehicle holds the financial wallet and intelligence to lock in the best price and grid balancing criteria. The machine learning model constantly learns the commute routes on an hourly basis and sets up an optimal time for the vehicle owner to charge the vehicle at the right charging station. The system also factors traffic and other real time factors to guide the vehicle to arrive at the charging station on time.
The financial transactions are exchanged directly between the vehicle and charging station without the use of a charge card or subscription. The AI system allows the charging station to operate autonomously and takes care of communication and payment with the vehicle without human intervention. For accuracy and trust purposes, the charging meter values are stored in the IOTA Tangle, which ensures reliable and well-kept administration. It limits errors and makes transactions transparent, and a receipt will be delivered and posted through the IOTA wallet real time.
Artificial intelligence powered intermarket analytics system empowers traders to make more effective trading decisions based on the linkages between related financial markets such as stocks, bonds, commodities, and currencies. By incorporating intermarket analytics into the trading strategies, rather than limiting the scope to individual markets, these relationships and interconnections between markets can be leveraged to benefit in this complex environment. Global market fluctuations, obscure currency devaluations, fluctuating oil prices and many other diverse and seemingly distant factors impact everyday trading decisions. Real time learning of the patterns and trends in today’s interconnected markets is vital for today’s successful trader and investor.
Reinforcement machine learning system discovers hidden market relationships and constantly learns how to provide early cues for price direction. The neural network has been initially trained on intermarket correlation data from stocks, bonds, commodities, and currencies, as well as how independent global events impact everyday trading decisions. For each and every trading instrument, the reinforcement learning network identifies and analyzes several hundred of its correlated data in real time. The performance of this model is further enhanced using feature engineering. Feature engineering is the process of using domain-specific knowledge to create additional input data that improves the machine learning model. By adding insightful technical indicators to the data set, as well as the output from the StatsModels SARIMAX prediction model should provide a nice balance of useful observations for the machine learning model to learn and act in this complex environment.
Artificial intelligence (AI) technology stack in the intersection of finance and machine is introducing fully autonomous machines capable of executing financial transactions without human intervention. Each machine holds its own financial wallet for machine to machine payments using blockchain technology based smart contracts and cryptocurrencies.
We unveiled an AI system architecture by embedding an intelligent financial wallet in both electric vehicles and electric charging stations. It enables an electric vehicle to directly negotiate and lock in the best price before the vehicle arrives at charging station.