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. Developed by Fannie Mae, Custom DU has been used since 2004 by several lenders to automate the underwriting of numerous mortgage products. Custom DU uses rule specification language techniques and a web-based, user-friendly interface for implementing business rules that represent business policy. 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.
Loan underwriting is the process of evaluating a loan application to determine whether the loan should be funded. The process often starts with a potential borrower walking into a branch office and requesting a loan to purchase or refinance a home. A processor asks the borrower to fill out an application, setting in motion a lengthy information-gathering process in which as many as 1500 data-element pieces will eventually be collected. This loan information includes items about the borrower's employment, income, assets, liabilities, and monthly expenses. During the process, a credit report and appraisal will be ordered from a third-party vendor.
The Lockheed Corp. (Calabasas, CA) and AT&T (New York, NY) have signed an agreement to jointly develop and market intelligent transportation systems. The two companies are responding to the Intermodal Surface Transportation Act of 1991, which calls for enhancing roadway capacity, safety, efficiency, and air quality through the development of intelligent vehicle highway systems. Electronic toll collection systems, traffic management systems, in-car navigational and route planning systems are among the systems being developed. UKbased Empires Stores, a mail order company, has reduced the clerical work in its credit department by about 30%, thanks to the implementation of an intelligent system. The company has successfully automated the decision-making process for passing or rejecting orders referred by its performance scoring system.
Lenders traditionally make decisions based on a loan applicant's credit score, a three-digit number obtained from credit bureaus such as Experian and Equifax. Credit scores are calculated from data such as payment history, credit history length and credit line amounts. Upstart uses machine learning algorithms, a subset of AI, to make underwriting decisions. The platform's algorithms analyze 10,000 data points to evaluate the financial situation of consumers.
Today, lenders are applying powerful new tools based on artificial intelligence and machine learning to make the process of underwriting and approving mortgages faster and more accurate. As the process becomes increasingly digitized, (bank account activity, credit information, tax forms, pay stubs, and other required information) more and more lenders arrange for borrowers to pre-fill online forms with data directly from each source. Borrowers do not have access to information about their application and the status of their loan, so they must rely on loan officers to communicate documentation requirements and status updates. With modern technology automating manual tasks, these costs fall significantly, potentially saving money for both the borrower and the lender.
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.
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.