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.
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. 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. He has spent the last 10 years in the financial services industry, holding various positions at industry-leading technology companies, including Ellie Mae and Salesforce.
It's a natural fit for the capabilities of AI because of the large, complex data sets with nuanced relationships, years of historical data, and a unique sales process in need of a facelift. In the pre-purchase education phase, AI bots could be used to help people understand their insurance needs, answer questions about their financial situation, and help customers continue with confidence down the path to purchase. The machine learning is used where it can come to a confident decision based on the data inputs and underwriting rules it receives. There are two main categories of data used for machine learning in life insurance: applicant information and external data sources.
For its Credit Optics Full Spectrum credit score, AI engines look at consumer payment data from wireless, utility and marketplace loan providers, to score consumers who have "thin" or no credit bureau files, including young people and new credit seekers. When the technology seems to make a significant difference in performance, the company will provide credit scores based on machine learning, said Eric Haller, executive vice president of Experian's Global DataLabs. To let clients experiment with machine learning, Experian offers an analytical sandbox with its credit data loaded into it. "In their advertising, TransUnion and Equifax falsely represented that the credit scores they marketed and provided to consumers were the same scores lenders typically use to make credit decisions," the CFPB said in a press release announcing the fines.
"In 2017, machine learning means more efficiency in the mortgage loan cycle," said Ken Bartz, co-founder of Sales Boomerang and Lead Squeeze and CEO of Monster Lead Group. "Machines could now start to effectively predict retention patterns and alert sales or retention departments to those most likely to be at risk. It's the simplicity and efficiency AI can bring that makes it such a powerful and important tool, said Alex Kutsishin, chief marketing officer for Sales Boomerang. "Lead Squeeze uses some machine learning and speech recognition to, almost in real time, analyze a loan officer's phone conversation.
We were inspired this week by author Tom Chatfield's article in the Guardian on how Technology is killing the myth of human centrality, where he described robots as: "Tireless, infinitely patient, endlessly consistent -- our creations measure up in ways we can only dream of." That accurately defines how we believe the role technology -- and more specifically Artificial Intelligence -- plays in our lives. If we can embrace our own shortcomings as humans and build the tools that fill that gap, we will only become stronger and more efficient.
Custom DU is an automated underwriting system that enables mortgage lenders to build their own business rules that facilitate assessing borrower eligibility for different mortgage products. By means of the user interface, lenders can also customize their underwriting findings reports, test the rules that they have defined, and publish changes to business rules on a real-time basis, all without any software modifications. The user interface enforces structure and consistency, enabling business users to focus on their underwriting guidelines when converting their business policy to rules. Using Custom DU, lenders can create different rule sets for their products and assign them to different channels of the business, allowing for centralized control of underwriting policies and procedures--even if lenders have decentralized operations.
An end-to-end system was created at Genworth Financial to automate the underwriting of long-term care (LTC) and life insurance applications. Relying heavily on artificial intelligence techniques, the system has been in production since December 2002 and in 2004 completely automates the underwriting of 19 percent of the LTC applications. Finally, a natural language parser is used to improve the coverage of the underwriting system.
Countrywide loan-underwriting expert system (clues) is an advanced, automated mortgage-underwriting rule-based expert system. The system receives selected information from the loan application, credit report, and appraisal. It then decides whether the loan should be approved or whether it requires further review by a human underwriter. If the system approves the loan, no further review is required, and the application is funded.