AI Solutions for Banking, Finance & Insurance
Portfolio and Asset Management
“Robo-advisors” offer the chance to save money by getting returns that essentially match the market while fine-tuning your assets in a way that satisfies the level of risk that you’re comfortable with. Through artificial intelligence and machine learning, automated software agents can use historical results to estimate the best way to allocate your investments.
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.
Loans and Insurance Underwriting
Banks and credit card companies have traditionally used only basic information about their customers when making financial decisions. First, a human analyst might look at half a dozen pieces of information such as the customer’s age, credit score, location, and occupation. The company then decides whether or not to provide a loan or open a new credit card account, and if so with what rates and terms. In today’s world, however, financial companies have access to more information about their customers than any human being could possibly retain—perhaps hundreds of thousands of data points for each person. Only machine learning algorithms are able to process all of this information and use it to help companies make decisions that maximize their profitability. Similarly, insurance underwriters want to limit the amount of money that an insurance company will have to pay out to its policyholders.
One particular application of robo-advisors is to perform algorithmic trading: the use of high-powered hardware and software to rapidly buy and sell assets. Algorithmic trading is almost always impossible for human traders to perform—they simply don’t have the brainpower to analyze all the massive quantities of data they need to read every second in order to turn a profit.
You’ve heard of Apple’s Siri and Amazon’s Alexa, but digital assistants are invading the world of financial services as well. 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.
Xen.AI can help the Banks, Finance and Insurance companies to apply artificial intelligence, machine learning, deep learning and data science technologies to improve the efficiency and reduce the operating cost.