In has long lived in labs as a tantalizing possibility, but the growth in computing power, the increasing sophistication of algorithms and AI models and the billions of gigabytes of data spewing daily from connected devices has unleashed a Cambrian explosion in self-directing technologies. "Could you start offering people with Nest better mortgage rates before you start getting into fair lending issues about how you're biasing the sample set?" However, MIT Sloan professor Erik Brynjolfsson, McAfee's co-author on the new book Machine, Platform, Crowd: Harnessing Our Digital Future, acknowledged that it makes it harder for humans and machines to work together if the machine can't explain how it arrived at its reasoning. Scott Blandford, chief digital officer of TIAA, said companies have to worry about AI's black box problem because if "you're making decisions that impact peoples' lives you'd better make sure that everything is 100 percent."
All of this information would be impossible to manage or process without machines capable of learning and making decisions about data on a large scale. So far, humans have only been able to create machines that can grasp information, make decisions and act as the machines are told. Consider the fact that the IRS has been letting consumers file taxes electronically for nearly 30 years. Because tax filing takes much less time and effort than buying a house – for most of us, anyway – I find it unlikely that most borrowers will trust their home purchase to a website any time in the near future.
Like all financial services, they are being rapidly changed by waves of technological innovation sweeping through industry – none more so than artificial intelligence and machine learning. Machine learning is essentially teaching computers to teach themselves – much the same way as humans can - by giving them access to huge amounts of data, rather than having to teach them to do everything ourselves. "There's a movement towards open source technology which is less costly to operate and scales very effectively, so essentially you have a lot more horsepower at your disposal and can operate on much larger datasets. Larger datasets obviously give a more accurate picture of whatever they represent, leaving less margin for error.
And eventually it is theoretically possible to feed the borrower directly from the digital adviser into the lender's application system and then receive an approval in principle without the need to deal with a human at all. Hegarty adds: 'We haven't yet got to a stage where the Habito system can directly interact with lenders' application technology but it is something we have had conversations with lenders about and they're keen to develop their digital banking offerings, so we will see how that progresses. Malhi: The whole mortgage advice industry hadn't changed in 20 to 30 years and this was just widely accepted as a problem One borrower who used Trussle earlier this year had this to say: 'I had the option of either waiting three weeks for an appointment with a high street bank mortgage adviser at an inconvenient time of day, or giving Trussle a shot. He hasn't gone down the route of online advice, preferring to interact with customers and process the mortgage application himself.
Well, we've built the first artificially intelligent digital mortgage adviser to provide free, unbiased advice so our experts can spend more of their time helping customers. The good news is that no matter where you purchase a home, house prices rise or fall together. Two thirds of first time buyers living in London are terrified of getting a mortgage and there's a widespread lack of understanding of the mortgage process. Using insight from hundreds of advice interviews, we built the world's first artificially intelligent digital mortgage adviser (DMA).
UK tech startup, habito, has launched the world's first artificially intelligent Digital Mortgage Adviser (DMA) allowing millions of consumers to discuss their mortgage needs from any connected device, 24/7, without requiring a human broker. "Finding the right mortgage product in the UK is like finding a needle in a haystack. "Our digital mortgage adviser is a huge step forward in making mortgage advice accessible for consumers in the way they need it most: unbiased, always available and most importantly free." Habito's digital mortgage adviser is a direct response to the FCA's Financial Advice Market Review Report calling for greater, more accessible financial services advice for British consumers.
The question for insurers is whether to use AI to automate processes or to augment the workforce and make it more creative and effective. Insurers are exploring the use of AI to augment the expert workforce in areas including risk management, client and/or prospect discovery, coverage recommendations and fraud detection. Data complexity and work complexity intersect in some areas such as product development and innovation, which place a heavy emphasis upon human judgment and experience. Even here, however, AI is being used to support innovation in areas such as home health analysis, customer personality profiling and "visual telematics", such as combining the analysis of body movements of a driver with telematics data to establish levels of risk and to use such data for pricing and underwriting decisions.
For instance, this dataset, which contains data on the admission criteria, graduation rates, and graduate future earnings for US colleges would be a great dataset to use to tell a story. Fannie Mae releases two types of data – data on loans it acquires, and data on how those loans perform over time. Performance data, which is published every quarter after the loan is acquired, contains information on the payments being made by the borrower, and the foreclosure status, if any. A good way to think of this is that the acquisition data tells you that Fannie Mae now controls the loan, and the performance data contains a series of status updates on the loan.
For instance, this dataset, which contains data on the admission criteria, graduation rates, and graduate future earnings for US colleges would be a great dataset to use to tell a story. Fannie Mae releases two types of data – data on loans it acquires, and data on how those loans perform over time. Acquisition data, which is published when the loan is acquired by Fannie Mae, contains information on the borrower, including credit score, and information on their loan and home. A good way to think of this is that the acquisition data tells you that Fannie Mae now controls the loan, and the performance data contains a series of status updates on the loan.