Online lenders have leveraged this data to make underwriting decisions, creating computer programs that can automate loan originations without the need for a customer to ever set foot in branch. While the mortgage process still has a long way to go before digital end-to-end origination is possible, technology can significantly simplify the process by digitizing forms, prepopulating known information and ensuring that all of the documents are in order before a customer sits down with a mortgage officer. For example, personal financial management tools--such as those offered by Geezeo and endorsed by ABA--allow a bank to help users manage their money by categorizing their transactions and visualizing spending trends. Just as fintech is being used to digitize customer-facing financial services, regtech promises to digitize back-office regulatory compliance, simplify regulatory reporting and empower staff to better assess risk and monitor regulatory compliance.
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.
The pressure is particularly strong with FinTechs like US online lender Rocket Mortgage and UK digital mortgage broker Trussle creating a completely digital experience for prospective home buyers. This was the exact focus of Synechron's blockchain accelerator for mortgage financing and processing where we re-architected business processes and developed an accelerator application to help banks leap-frog the innovation stages needed to embrace this type of technology. Additionally, a public blockchain for real estate title, deeds, planning permissions, mortgage registry and other public records associated with the real estate assets could provide a second powerful application to further enhance these processes. In fact, in Dubai, Synechron recently won a leading position in a hackathon for its Land Registry team's blockchain submission which built an application to automatically generate title deeds on the blockchain.
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.
Customers interested in a loan complete a simple application at LendingClub.com LC leverage online data and technology to quickly assess risk, determine a credit rating and assign appropriate interest rates. Customers interested in a loan complete a simple application at LendingClub.com LC leverage online data and technology to quickly assess risk, determine a credit rating and assign appropriate interest rates. Home ownership: Home ownership: Does home ownership have any relationship with LC grades, FICO scores and Charge Off rates? The fico score does not reflect this while the LC score seems to partially capture that risk: Revolvoving balance and employement length: Revolving Balance, along with Employment length are actually the features with the least obvious link to default rates: ## revol_bal_bucket charged net_EL avg_fico avg_grade## (fctr) (dbl) (dbl) (dbl) (chr)## 1 0-1,333 12.93 8.19 734 B4 ## 2 1,333-3,170.2 Fully paying borrower tend to have slightly more accounts but too many accounts may be bad too.
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.