Finance
Aggregate | Secure | Analyze
AI technologies have developed automated investment advisors which have caused a massive disruption in the investment banking industry
Powered by ITFormula1
Secure transactions
AIFA uses its 100+ algorithms to detect transactional fraud by analyzing millions of data sets that may be missed by the naked eye. These models are built on the client’s past behaviour and their transaction history.
Proficient predictive abilities
AIFA can learn your customers’ past behavior and provide result-based recommendations.
AIFA combines business expertise with statistical measures to give precise and accurate scoring of your customer’s creditworthiness, propensity to buy and generates conversion by identifying digital marketing strategies that are most likely to convert
Propel visible results
A.I. shows disruptive results in the Banking Industry as banks leverage their Intelligence for credit scoring, early warning, and identifying self-curing customers for over a decade.
A.I. technology’s automated investment advisors have caused a massive disruption in the investment banking industry.
Credit scoring
Credit scoring is an evaluation of how well the bank’s customer can pay and is willing to pay off debt.
AIFA combines business expertise with statistical measures to accurately store your customer’s creditworthiness and propensity to buy.
A.I. credit scoring decisions are based on data, such as total income, credit history, transaction analysis, and work experience.
A.I. can analyze significant volumes of personal information to reduce their risk, unlike the conventional systems, which are usually limited to fundamental data such as credit score marketing strategies that are most likely to convert.
Credit scoring using AI provides better individualized and sensitive assessments.
Make investment forecasts to identify distinct market changes much earlier than traditional investment models.
Meticulous risk management
Integrating A.I. with banks and financial institutions can significantly analyze massive volumes of data sources and lower the risk levels.
A.I. can analyze significant volumes of personal information to reduce their risk, unlike the conventional systems, which are usually limited to fundamental data such as credit score.
Customer data management
Integrating Artificial Intelligence to handle such large volumes of data can bring both process efficiency and the benefit of extracting natural data intelligence.
A.I. tools such as data analytics and data mining help get relevant insights from data for more immense business profitability.
Enhance existing financial solutions
Banks can transform their business using A.I. in several ways, including pushing new products and services, opening new markets, industries, and making innovation.
A.I. technologies achieve significant savings both today and in the future by reducing operational costs and lowering base costs, increased employee capacity to handle workload volume, and enhanced customer service and satisfaction.
Predict and assess loan risk
A.I. is beneficial in corporate finance as it can better predict and assess loan risks. For companies looking to increase their value, A.I. technologies can help enhance loan underwriting and decrease financial risk.
ML algorithms outperform traditional models in predictive power for various applications, such as predicting defaults.
AI can discover patterns based on large volumes of data that can be used to generate credit default signals.
With sufficient computational power, A.I. algorithms are capable of developing early warning signals using indicators from a wide range of sources and increasing the accuracy of said indicators.
Meticulous risk management
Integrating A.I. with banks and financial institutions can significantly analyze massive volumes of data sources and lower the risk levels.
A.I. can analyze significant volumes of personal information to reduce their risk, unlike the conventional systems, which are usually limited to fundamental data such as credit score.
ESG Evaluation
Financial institutions increasingly use A.I. in ESG evaluation. But ESG data often come in large quantities and are unstructured. To achieve consistency and comparability across companies, financial institutes can use A.I. to use proprietary algorithms to quantify ESG risks.
Companies need to embrace tactical and calculated techniques to examine the unstructured data and make crucial decisions. A significant part of artificial intelligence in ESG comes from sentiment analysis algorithms.
Artificial intelligence solution helps investment researchers identify ESG risk by drilling down vast amounts of qualitative, unstructured data through A.I. algorithms that fast identify and measure ESG details.
Cross-sell and Up-sell
Banks can transform their business using A.I. in several ways, including pushing new products and services, opening new markets and industries, and making innovations.
AI generates relevant recommendations for intelligent conversations to guarante an increase in conversions, cross-sell and upsell.
Recommender Systems are used to provide the best recommendation of a product that would interest the client most based on the user data.