OpenFinancial.AI is an Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning based customizable solution suite for financial services.
As data breaches become a more common occurrence, fraud has become a major problem for banks and credit card companies. With hundreds of millions of bank accounts and credit cards, trying to find instances of fraud manually is plainly impossible. The only way that financial companies have any hope of fighting fraud is to use machine learning algorithms to detect unusual transactions. By feeding the algorithm millions of data points about real and fraudulent activities, machine learning models can make better guesses about which transactions are most likely to be suspicious.
Digital customer support assistants are invading the world of financial services. Automated phone systems that rely on machine learning can help route callers to the right department within a company, providing good-quality customer service without the need for human employees. AI and machine learning techniques are used for speech recognition and natural language processing in order to understand what customers want and connect them with a human agent if necessary. For example, recurrent neural networks (RNNs) are used to parse the individual phonemes of a voice clip and assemble them into words.
Algorithmic trading involves the use of complex AI systems to make extremely fast trading decisions. Algorithmic systems often making thousands or millions of trades in a day, hence the term “high-frequency trading” (HFT), which is considered to be a subset of algorithmic trading. Machine learning and deep learning are playing an increasingly important role in calibrating trading decisions in real time.
Machine learning algorithms can be trained on millions of examples of consumer data (age, job, marital status) and financial lending or insurance results, such as whether or not a person defaulted or paid back their loans on time. The underlying trends that can be assessed with algorithms, and continuously analyzed to detect trends that might influence lending and ensuring into the future.