Intelligent Mortgage Loan Approvals Imagine technology that pulls third-party data to verify applicant's identity, determines whether the bank can offer pre-approval on the basis of a partial application, estimates property value, creates document files for title validation and flood certificate searches, determines loan terms on the basis on risk scoring, develops a strategy to improve conversation, provides real-time text and voice support via chatbot. Imagine software that calculates mortgage risk based on wide range of loan-level characteristics at origination (credit score, loan-to-value ratio, product type and features), as well as a number of variables describing loan performance (e.g., number of times delinquent in past year), several time-varying factors that describe the economic conditions a borrower faces, including both local variables such as housing prices, average incomes, and foreclosure rates at the zip code level, as well as national-level variables such as mortgage rates. When uncharacteristic transactions occur, an alert is generated indicating the possibility of fraud. Credit Risk Management Imagine software that allows for more accurate, instant credit decisions by analyzing news and business networks. This system can also be used to improve Early Warning Systems (EWS) and to provide mitigation recommendations.
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.
As digital lending continues to grow in size, companies are looking for ways to make their services more efficient and profitable to both lenders and borrowers. And they believe artificial intelligence and big data hold the key to the future of loans. 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. They're used to determine how likely applicants are to repay their debts and to calculate the interest rate of loans.
No longer the "stuff of science fiction," artificial intelligence (AI) is at the heart of a new wave of technology – transforming mortgage lending. 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. It is helping lenders predict consumer behavior and evaluate risk more accurately. Machine learning is also making online financial advice from lenders become more intuitive, individualized, accurate, and available 24/7. Financial technology (fintech) is all the rage among mortgage marketers today, but some lenders are far ahead of others in creating and applying AI to their businesses.
Artificial intelligence is gaining traction in enterprises, with many large organizations exploring algorithms to automate business processes or building bots to field customer inquiries. But while some CIOs see self-learning software as a boon for achieving greater efficiencies, others are leery about entrusting too much of their operations to AI because it remains difficult to ascertain how the algorithms arrive at their conclusions. CIOs in regulated industries in particular, such as financial services and any sector exploring autonomous vehicles, are grappling with this so-called "black box problem." If a self-driving rig suddenly swerves off of the road during testing, the engineers had darn well better figure out how and why. Similarly, Finservs looking to use it to vet clients for credit risks need to proceed with caution to avoid introducing biases into their qualification scoring.
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.