During the Mortgage Bankers Association's recent Tech Solutions conference, HousingWire sat down with Tavant Technologies' Atul Varshneya to discuss the disruption that artificial intelligence and machine learning can cause in the lending and mortgage spaces. Varshneya works at Tavant as a vice president and leads the company's artificial intelligence / machine learning (AIML) practice, which has a strong focus on its application in the areas of mortgage and lending. Q: Tavant held the industry's first Artificial Intelligence and Machine Learning summit in 2017. From that event and your experience since, where is the industry in adopting this kind of technology? A: While we see the interest in applying ML to business problems is rising steadily, we believe that most companies in the mortgage ecosystem are still experimenting with ML and have not rolled it out widely.
How are advances in artificial intelligence and machine learning changing credit risk assessment? That's a topic I'll be exploring in three presentations at FICO World 2018, April 16-19 in Miami Beach. These three presentations have a common thread: How FICO applies artificial intelligence and machine learning not as a "silver bullet" but carefully balanced with human domain expertise in the formulation of problems and models. FICO Scores are among the most scrutinized models in the world, so would you expect modern machine learning -- with its challenges around explainability -- to bring great benefits to FICO Score R&D and production? On Tuesday, April 17, 1:30-2:30, my colleague Ethan Dornhelm and I will show that machine learning offers tremendous efficiencies for research "in the lab".
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.
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.
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.
Global banks that have a large mortgage business are facing pressure internally and externally to upgrade their operating model to save money, decrease processing times and enhance the customer experience – today it can take more than 60 days to complete a mortgage transaction. 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. Banks, therefore, are exploring everything from mature technologies like Optical Character Recognition (OCR) to more leading edge and high-tech solutions based on blockchain and artificial intelligence. While some of these solutions could dramatically impact day-to-day business for lenders and their brokers and customers, blockchain has the potential to completely transform the entire mortgage financing industry. The financial services industry is all about trust – whether relationship based, reputational, authoritative (legal) or transactional – banking today is built on trust.
Credit reference agency Experian hold around 3.6 petabytes of data from people all over the world. This makes them an authority for banks and other financial institutions who want to know whether we represent a good investment, when we come to them asking for money. 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. I spoke to Experian CIO Barry Libenson about how the business – a pioneer in Big Data-driven analytics – is adapting to meet the challenges and reap the rewards offered by the new generation of cognitive, self-teaching technology, and the ever-growing data streams which power them.