Applications in Finance
The use of AI to the financial sector is altering our relationship with money. All the way from making credit judgements to quantitative trading and financial risk management, AI is helping the financial sector become more efficient and effective.
Create supervised algorithms that can extract characteristics from data to foresee changes in market prices and liquidity. Locate undervalued assets. Interest rate returns and spreads are determined using predetermined time series of both endogenous (stock price data) and exogenous (interest rate data). This information is sent into the Elastic Net regression method so that predictions may be made. If no regularities can be seen, simpler methods are used. Point forecasts are obtained by recalculating the returns as absolute values.
Identifying essential data elements and nonlinear patterns in huge datasets may improve the accuracy of risk modelling. The model residuals are analysed for their distribution in relation to the truth. The residuals are used in conjunction with market data to derive potential value outcomes. The Value at Risk (VaR) for each portfolio is then determined at a specified degree of confidence based on the distribution of the value possibilities. Supporting the development of automated systems for reporting, portfolio monitoring, and contingency planning is one of our specialties.
For a particular time horizon, the optimal asset allocation will provide the maximum potential return without exceeding a targeted level of risk. Only if reliable forecasts of the necessary market data and risk estimates are available will this be possible. Combining market data time series with reinforcement learning, the stochastic gradient descent technique tends to converge to optimal portfolios.
Natural Language Processing
The term "sentiment analysis" refers to the method of deducing an individual's point of view on a topic from their written or spoken expressions. Sentiment analysis has emerged as a vital method for making sense of the billions of bytes of data that are created every day. Our stock market forecasts are based on news and expert commentary. Since computers can quickly and easily comb through vast amounts of text from a variety of news sources, Sentiment Analysis is allowing them to accomplish the same work.
Fraud Detection & Identity Management
In order to determine whether or not a customer's financial behaviours are consistent with those of the cluster they belong to, fraud detection software frequently analyse the customer's social media, employment history, education, and more. These complex models may be regularly updated to include new types of client information and so automatically adapt to new levels of financial fraud. Such technologies are able to successfully detect fraudulent transactions because of the real-time, dynamic analysis of money flows they do. Additionally, they may lessen the likelihood of both false positives (instances when legitimate transactions are incorrectly identified as fraudulent, leading to refused transactions or even account suspension) and false negatives (situations where real threats are missed).
Creditworthiness may be determined with the use of AI, which will speed the loan procedure and improve the borrower's customer experience, especially for individuals with no credit history. Internal procedures may be simplified if they are not wasted on people who are not creditworthy. In a similar vein, clients are more likely to be interested in a procedure if it can be completed quickly and with less effort on their part.