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Black Knight: Active Foreclosure Rate And Inventory End 2018 Below Pre-Recession Averages

Today, the Data & Analytics division of Black Knight, Inc. released its latest Mortgage Monitor Report, based upon its industry-leading loan-level mortgage performance database. With full-year mortgage performance data in, this month’s report looked at 2018 in review. As explained by Ben Graboske, president of Black Knight’s Data & Analytics division, more than a decade past the start of the financial crisis, most metrics reflect a recovery to their long-term, 2000-2005 pre-recession averages.


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“Across the board, 2018 year-end numbers are good news from a mortgage performance perspective,” said Graboske. “All four major performance metrics – delinquencies, serious delinquencies, active foreclosures and total non-current inventory – ended the year below pre-recession averages for the first time since the financial crisis. Just 576,000 foreclosures were initiated throughout the entirety of 2018 – an 18-year low – and the vast majority of these were repeat actions. In fact, first-time foreclosures were down 18 percent from the year before, hitting the lowest point we’ve seen since Black Knight started reporting the metric in 2000. Even repeats – though making up more than 60 percent of all foreclosures – were down 6 percent from 2017.


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“These year-end numbers are further proof of what we’ve been observing for some time now. The high credit quality and corresponding lower risk we’ve seen in the post-crisis origination market for the better part of a decade continues to pay dividends in terms of mortgage performance. In addition, the low interest rate environment we’ve enjoyed for so long had – until very recently – resulted in a refinance-heavy blend of originations for years. Refis, as a whole, tend to outperform their purchase mortgage counterparts, which has boosted mortgage performance as well. On top of that, we’ve had the benefit of strong employment and housing markets, which have helped the vast majority of homeowners meet their debt obligations, while those few who may have faced a possible default have gained enough equity to be able to sell rather than face foreclosure.”


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As the average interest rate on a 30-year mortgage ticked down again in January, falling below 4.5 percent for the first time since April 2018, Black Knight revisited the impact this change has had on the refinanceable population. The decline in rates has returned the interest rate incentive to refinance to 1 million homeowners, a 50 percent increase in rate/term refinance incentive over just the last two months.


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There are now 2.9 million homeowners with mortgages who could likely qualify for a refinance under broad-based criteria and also reduce the interest rate on their first mortgage by at least 0.75 percent by doing so, the largest this population has been since January 2018. Even if rates should hold steady – and certainly if they fall further – this could lead to an unexpected bump in refinance volumes in early 2019.

Black Knight Makes Digital Closing Advances

Black Knight has enhanced the company’s Expedite Close digital closing solution has been enhanced with advanced intelligence and data recognition capabilities. Already able to support traditional wet-ink, digital or hybrid mortgage closings, the newly enhanced Expedite Close adds the ability to automatically determine the best way to close any given loan based upon a lender’s preferences and business rules, as well as jurisdiction-specific requirements.


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“Expedite Close was already a game-changer for eClosings by seamlessly supporting hybrid or fully digital processes while also making sure settlement agents, lenders, real estate agents, consumers and investors had what they needed without requiring changes to current practices or systems,” said Mike Brown, general manager of Black Knight’s Lending Solutions division. “But now it takes eClosing to a whole new level with advanced intelligence capabilities that automatically determine and execute on the best way to close a loan, based upon lender preferences and what a given jurisdiction allows. This newest iteration of Expedite Close is yet another innovative solution Black Knight is bringing to market as we continue to help transform the industry.”


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Whenever possible, Expedite Close supports fully digital closings – including eSign, eNotary and eClose components. For closings that are not fully digital, Expedite Close automatically identifies and executes whatever combination of wet-ink and digital closing works best for the lender and/or the property jurisdiction, saving significant cost and time. Expedite Close also enables lenders to adopt digital elements at their own pace, without requiring the purchase of additional technology when the lender – or the jurisdiction – is ready to embrace completely digital closings.


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This innovative closing solution also digitally audits the entire closing package at completion. Additionally, advanced document-recognition capabilities enable static PDFs of closing documents to become searchable, eSignable and data-centric, allowing Expedite Close to streamline the post-closing process. These innovative capabilities not only introduce greater speed and efficiency into the closing process, but also support consistency throughout the entire process. 


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 Expedite Close significantly enhances the consumer experience by providing a more streamlined closing with the ability to review all necessary documents before the actual closing and eSign-appropriate documents. Lenders and settlement agents not only benefit from improved borrower satisfaction, but also from reduced risk and enhanced efficiencies without having to significantly alter their current processes.

“Right now, mortgage closing requirements are inconsistent and inefficient across the country, and even from lender to lender, or agent to agent,” said Brown. “Expedite Close was designed to meet the challenges of today’s closings, while delivering maximum benefit to our clients and their customers, and making the process – and implementation – as simple as possible.”

Customer Empowerment A Key Driver Of Satisfaction In Mortgage

Typical consumers are fairly comfortable with the digital world we live in today.  These savvy web-surfers confidently search online for products and services; conduct research on the performance and reputation of providers; compare the features and pricing of similar solutions; and execute on their decisions when they are ready.  If interacting with a customer service professional or “robot” is required before consumers can proceed, it can certainly dampen customer satisfaction levels, negatively impact sales and reflect poorly on the company’s brand.


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It’s not that consumers always want to do everything themselves – but they do want to be empowered to act independently if they so choose. They want the website, program or app to be easy to find and understand; available upon demand; and to be so well-designed and intuitive that little to no thinking is required to achieve their desired outcome. 


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Research findings also suggest that customers who are empowered with ready access to the information and functionality they need to conduct business independently tend to have a positive experience with the process, which often translates into a positive opinion of the company and its solutions, and ultimately sales. 


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Freeing Up Data and Information

Traditionally, mortgage servicers have been the primary gatekeepers of mortgage-related customer information, as well as the data associated with their home loans. To access this information, customers must interact directly with their providers, ask them to determine their options, and then request the analysis required to make recommendations about the customer’s best course of action. This accessibility barrier to loan information contradicts the ease of self-service options for other types of information that deliver details faster and more efficiently to customers on demand.

Widespread internet access means that most consumers can visit an extensive number of websites and apps that make self-service both possible and easily accessible. From airline and hotel apps to retail shopping, education, banking and social networking apps, consumers enjoy the satisfaction of doing business online, quickly finding the information they need and completing their transactions.  Today, people can even purchase a car online and have it delivered to their front door – all without speaking to a salesperson!

Of course, there are still a number of business sectors where consumer access to information and analysis for decision-making is difficult, if not impossible. And, many consumers still prefer to interact with a company representative to discuss product and service options before deciding upon a course of action. With today’s digital technology, this personal interaction can now be a choice based upon a consumer’s preference – rather than a requirement for consumers to get the information and answers they need.

Empowering the Homeowner with Information

One of the most important investments many consumers make is the purchase of a home. There are several helpful websites and apps that enable potential buyers to see homes that are for sale; learn about neighborhoods, schools, and crime rates; find out how much taxes are; and even see a 360-degree view of a home’s interior. Real estate agents are readily available to support the buying process with just the click of a button on a website. And, on many sites, lenders stand ready to offer the consumer approval on a mortgage loan. With these self-service capabilities, the consumer can use their personal device (e.g., laptop, tablet or smartphone) to find one of the most important wealth-building assets they will ever own.

Once they’ve secured a loan, homeowners benefit from understanding how to best manage their home loan. Certainly, many consumers can obtain information about their house payments, amount of interest paid, how much equity is in their home and other related information on their bank’s website. What may not be readily apparent to them is what to do with this information or how to manage their property wealth as the years go by.

This is where a mortgage servicing app can become a consumer’s best friend — but not just any app that provides information based upon static data. What is needed is a dynamic application that accesses up-to-the-minute data to give homeowners the information they need for future home- or loan-related decision-making.

Scenario Simulation and More

We know that many, if not most, consumers prefer to access their mortgage information online without the intervention of a customer service rep (unless they ask for one). But we also know that homeowners are not always financially savvy about how best to leverage and maximize the wealth in their homes. To help consumers make the decisions that are best for them, they need current information about their payments, interest rate and equity; how the value of their home compares to similar homes in their neighborhood; and the impact of various financial decisions they could make. And, these details need to be easily accessible and consumable. 

Today’s banking apps certainly provide basic information about the status of a consumer’s existing accounts.  But what if a mortgage servicer provided its customers with an interactive app that could do much more – such as simulate different scenarios regarding how consumers might use the equity they are building in their homes.  Such an app could offer a dashboard that would provide a quick view of market and neighborhood information, as well as the ability to click a button to make a payment. The app could also show consumers what the current (real-time) value of their home is; how a refi would impact their monthly payments or interest savings; and how making an additional principal payment on a monthly basis or in a lump sum would improve the time required to pay off their loan. This app could also show other information, such as current interest rates available or special deals their lenders may have available.

Ultimately, this is the kind of do-it-yourself functionality that mortgage customers want at their point of need. They want to be educated about what they need to know, review different scenarios, compare options, and act when and if they decide to. They want quick access to customer support if they need it, without feeling intruded upon. This is customer empowerment at its finest, and can help build the kind of customer satisfaction – and customer retention – that will last. 

About The Author

Leading-Edge AI In Mortgage Lending

Previously CEO of HeavyWater Inc., the mortgage-focused Artificial Intelligence (AI) provider recently acquired by Black Knight, Inc, Soofi Safavi now serves as Managing Director of Black Knight’s Applied AI group, bringing leading-edge AI and computing capabilities to the Black Knight product portfolio. With over 20 years of experience in mortgage and banking technology, and deep expertise in IT strategy, architecture and machine learning, Soofi is uniquely suited to discuss AI’s role in the mortgage industry.

Q: It seems as though Artificial Intelligence (AI) has resulted in incredible advancements across so many industries, but we haven’t seen the same in the mortgage industry. Why is that?

SOOFI SAFAVI: I would say that we simply haven’t seen it –yet. The mortgage industry is a very complex environment that requires vast amounts of expert knowledge to navigate effectively. That is why there is still so much work to be to be done despite a never-ending quest for increasing efficiency through technology. AI will be key for not only increasing efficiencies, but also identifying and eliminating deficiencies.


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Every day in our industry, we have thousands of experts leveraging technology to perform a series of activities and tasks that are essential to the mortgage process, from origination through servicing. The goal with a mortgage industry-focused AI is to capture that knowledge and replicate it within algorithms via machine learning.

From a high level, AI is about using computer systems that are able to perform tasks normally requiring human intervention. Machine learning is a type of AI that allows computers to learn without being explicitly programmed. It is a technology that uses algorithms to learn from and make predictions on data. It reads, comprehends and draws conclusions based on context to mimic human thinking and build expertise over time.  

An AI that can learn from the experts who are currently performing all of the many tasks involved in the mortgage process can offload much of the manual effort to technology. The mortgage professionals doing that manual work today can then shift into a different role, one more suited to the knowledge worker-based economy of the 21st century. They become teachers and guides of the AI, and make the decisions only humans can make. 


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Q: That raises an interesting question. There is a lot of uncertainty associated with AI. Many feel that individual’s jobs may be at risk if AI takes over much of the manual processing associated with the mortgage industry. What would you say to these people?

SOOFI SAFAVI: My answer would be twofold. First, I’d say look to history. Repeatedly in our industry – and others – the introduction of new technology has spurred fears of machines replacing people. When automated underwriting systems first came on the scene, many underwriters feared for their jobs. In fact, what they did was reduce friction in the mortgage process, allowing for what had been an entirely manual process to become more streamlined. The end result was increased efficiency and throughput, calling for far moremortgage professionals, not less.

While AI is light years ahead of automated underwriting, I expect the same will be true today. In the current environment, there is a significant amount of rigidness in mortgage origination, and people tend to go through this process a handful of times in their life. The potential for reducing friction in the mortgage process has increased exponentially with the advent of AI. What has for many years been a long, exhaustive and laborious process – on the part of the homebuyer as well as those of us in the industry – will see reduced friction via the automation afforded by AI. 


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An AI that has been taught to perform traditionally repetitive functions can do so more quickly and accurately than traditional methods. For example, verifying income, assets and insurance coverage; all traditionally manual activities that take hours to complete and are prone to error. Putting AI to work on these stare-and-compare tasks frees up highly-skilled mortgage professionals to focus on creating value, enhancing the customer experience and expanding production rather than simply executing repetitive functions.

Reduced friction equates to increased opportunity, for borrowers and mortgage professionals alike. If you remove that friction, and the underlying operational inefficiencies behind them, the home buying process will become much more fluid. A smoother, simpler process augmented by technology becomes one that can occur with more frequency throughout an individual’s life. And that opens the door to more innovation around products – loan products, technology products, credit products, and more – to support that increased frequency.

I would imagine that, much like was experienced with automated underwriting, a frictionless process will result in more loans being made, and more jobs for skilled professionals using AI-empowered tools. Not only that, but it will result in more jobs across the housing spectrum. All in all, we’re going to be looking at a much more interactive, more fruitful marketplace.

Q: The promise of AI in the mortgage industry seems incredible, but realistically, what sort of resources are required for an implementation of that level? 

SOOFI SAFAVI: The benefits of AI are not dictated by the size of an organization. In fact, mortgage industry players of all sizes can benefit from AI today. Black Knight’s own AI virtual assistant – AIVA – can be brought into an organization in much the same way as any other resource. An originator, or servicing shop, can “hire” AIVA to assist with specific functions or tasks. 

Much like any other employee, AIVA arrives for work with a certain skill set – it’s why she was hired in the first place. Of course, there is also an onboarding period where AIVA learns what is expected of her in this specific role, and is taught the specific process intricacies of a given organization, but after that, she is then deployed in the same way as any of the organizations other employees. And the skills she develops in the process become part of her knowledge base moving forward. 

Of course, at the enterprise level, when an organization’s operations reach across multiple verticals within the mortgage arena, the potential benefits increase exponentially. Rich, deep data is the fuel on which an AI runs, and the more data is available to AIVA, the more implementations become feasible.

But it’s important to stress that AIVA is not something that is only available to the largest lenders or servicers, but it is a resource that can be made available to organizations of all sizes. AIVA is as applicable in origination as she is in servicing, or in other facets of the industry.

Q: So an AIVA in every shop?

SOOFI SAFAVI: Let’s back up a bit, because I think this will be helpful in painting the entire picture. At the point of origination, a great deal of information is gathered on a prospective customer. That information, or some subset of it, is of use throughout the loan lifecycle – from application, through origination, settlement, closing, servicing, and if need be, modification or default management. All of these different players are gathering and processing information, and there is a great deal of overlap.

Each player in the mortgage process needs to receive a full file, and extract their own role-specific data from that document and then push it through their core system, to effectively complete their piece of the mortgage process. When you stop to think about it, for many of the players involved, roughly 60 percent of the information they need, or the calculations they make, mimic – or at least closely align with – activity the originator has already completed. 

Not only can an AI do that analysis and ascertain the 60 percent of information and analysis that has already been done, but it can go further. Rather than starting fresh each time, with the time and cost associated with these activities, those conclusions are presented by the AI, because it’s looking at the entire process holistically. Which adds to the unseen value. 

This points to the larger benefits of AI, its ability to learn. The more information an AI has at its disposal, and the more skills it is taught, the more places in the process it can add significant value. That same 60 percent share of information – perhaps more – that carries over from origination to, say, a title provider, also has value across the entirety of the loan lifecycle, to multiple players involved in the process.  

Not only can AI cut significant amounts of time from the process, it can also make data-based decision-making much more easily accessible for the all of the constituents involved. And that improves the process all across the board.

Q: Any final thoughts on AI in the mortgage industry, particularly for originators?

SOOFI SAFAVI: Black Knight’s first goal for AIVA is to drive down the cost to originate a loan by maximizing efficiencies and eliminating inefficiencies through the introduction of cognitive automation.

Whereas today’s workflow orchestration engines do a fantastic job of increasing efficiencies by alerting users to tasks that must be completed – a bank statement or paystub has arrived and is ready for review – AI can proactively evaluatethat document based upon its understanding of the mortgage lexicon. It leverages that expertise – which is continually expanding via machine learning – and a deeper understanding of associated data/behavior to see if there are any red flags or missing elements and inject a sense of urgency in getting those things addressed. 

Orchestration engines exist to help humans work more efficiently. The intent of AI, and particularly AIVA, is to help them to work less on mundane tasks so their capacity grows.  In short, work less, work better, by delegating some of the work to your virtual assistant. Then, the mortgage professional and his or her expertise shifts to verifying what the assistant has produced, providing the all-important human level of interaction our industry depends on.

Other goals focus on improving servicing functions and creating actionable intelligence, for improved efficiency across a company.

Ultimately, the name of the game is applied AI, not simply AI for AI’s sake. With applied AI, our goal is to bring cognitive automation to where the bulk of work and activity is happening. Our industry has come to accept a 45-day average mortgage cycle time, an $8500-$9500 average cost per loan, and the need for some 15-20 people having to touch a loan to get it through closing and beyond. What we’re trying to do is shake that acceptance and teach AIVA to automate the bulk of that work is being done. Again, rather than simply using AI for AI’s sake, we’re trying to introduce cognitive automation where it will have the biggest possible impact in terms of reducing cycle times and costs. And thatwill transform the mortgage industry.

INDUSTRY PREDICTION

Soofi Safavi thinks:

1: The industry will face a human resources challenge, as it will become increasingly difficult to entice the digital native college graduate who leaves her smart home to commute to work in her self-driving car into processing underwriting documents all day long. 

2: Technological innovation – and perhaps more importantly – adoption will continue to accelerate; expect more cutting-edge innovations. 

3: Along the same lines – much of the technological innovation in the mortgage industry has been on low-hanging fruit (point of sales systems, etc.); we will see innovators begin to tackle the more complicated parts of the process.

INSIDER PROFILE

Previously CEO of HeavyWater Inc., the mortgage-focused Artificial Intelligence (AI) provider recently acquired by Black Knight, Inc, Soofi Safavi now serves as Managing Director of Black Knight’s Applied AI group, bringing leading-edge AI and computing capabilities to the Black Knight product portfolio. With over 20 years of experience in mortgage and banking technology, and deep expertise in IT strategy, architecture and machine learning, Soofi is uniquely suited to discuss AI’s role in the mortgage industry.

Total Tappable Equity Falls For First Time Since Housing Recovery Began

The Data & Analytics division of Black Knight, Inc. released its latest Mortgage Monitor Report, based on data as of the end of October 2018. This month, Black Knight looked at full Q3 2018 data to revisit the U.S. home equity landscape, finding that quarterly declines were seen in both total equity and tappable equity, the amount available for homeowners with mortgages to borrow against before hitting a maximum 80 percent combined loan-to-value (LTV) ratio. Ben Graboske, executive vice president of Black Knight’s Data & Analytics division, explained that the decline is being driven by home prices pulling back on a quarterly basis in some of the nation’s most expensive housing markets.


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“After seeing a significant slowdown in its growth from the first to second quarters of 2018, the amount of tappable equity fell by $140 billion in Q3 2018,” said Graboske. “That is the first decline we’ve seen since the housing recovery began, and its cause can be traced directly to softening home prices in some of the nation’s most expensive – and equity- rich – markets. Indeed, tappable equity fell in 60 of the 100 largest markets, including 12 of the top 15. Three markets in California alone – San Jose, San Francisco and Los Angeles – accounted for 55 percent of the total net decline. Add Seattle into the mix, and you see that just four markets were behind two-thirds of the net reduction in tappable equity. All were areas where home price growth has far outpaced the national average in recent years, but in which prices fell in Q3 2018 – from as little as one percent in Los Angeles, to a 4.6 percent drop in San Jose.


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“Of course, there is still $9.8 trillion in total home equity in the market, some $5.9 trillion of which is tappable. That’s $571 billion more than in Q3 2017, and tappable equity remains near an all-time high. It’s also important to remember that in general third quarters are relatively flat as far as home prices are concerned, and that tappable equity is up on an annual basis in 98 percent of major metro areas. But the fact remains that affordability concerns are beginning to have an impact on home prices, particularly in more expensive markets, and as a result, on homeowner equity as well.


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Interestingly enough – although for-sale inventory is up on an annual basis for the first time in four years – an analysis of listings on mortgaged properties suggests that homeowners reluctant to put their current homes on the market due to ‘rate lock’ or ‘affordability lock’ may still be holding down available inventory by about six percent. By constraining the supply of available homes, this in turn may be countering what might otherwise be greater downward pressure on home prices.”

Other results from the quarterly equity data showed that just 1.8 percent of homeowners remain underwater, owing more on their mortgages than their homes are worth. For those with equity, the average homeowner with a mortgage has $191,000 in equity in his or her home. Among those with tappable equity, the average amount available to borrow against is $136,000. In total, over 50 million homeowners with mortgages have some amount of equity in their home, 43.6 million of which have tappable equity – a decline of approximately 272,000 from this time last year.

About The Author

Mortgage Delinquencies Rebound Strongly in October

According to data from Black Knight, after last month’s spike, mortgage delinquencies rebounded strongly in October, falling 8.2% from September and nearly 18% from last year. There were 165K fewer past due loans in October than the month prior. It wasn’t just early-stage delinquencies that improved, either: seriously delinquent loans (90 or more days past due) hit a more than 12-year low after falling 14K from last month and 90K from last October. Continued improvement in hurricane-related delinquencies associated with Harvey and Irma are contributing to the strong year-over-year improvements.


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Foreclosure starts did see a monthly increase, but keep in mind they were coming off of last month’s nearly 18-year low. And even with an uptick in starts, the number of loans in active foreclosure fell slightly from last month, and is down by 24% from last year. There are now just 267K loans remaining in active foreclosure; 1K fewer than last month and 81K than last month. Finally, and somewhat surprisingly, mortgage prepays (now driven more by housing turnover than refinance activity) increased 14% from September. Even so, they were still 29% below last year’s level.


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The report from Black Knight found that:

>>After rising sharply in September, mortgage delinquencies fell by 8.2 percent in October and are now down by nearly 18 percent from the same time last year

>>Serious delinquencies – loans 90 or more days past due – fell by 14,000 from last month and 90,000 from last October to hit a more than 12-year low


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>>Improvements in hurricane-related delinquencies associated with Harvey and Irma – which spiked in late 2017 – are contributing to the strong year-over-year improvements

>>Despite foreclosure starts seeing a monthly increase from September’s nearly 18-year low, the number of loans in active foreclosure fell slightly from September and has decreased by 24 percent from last year

>>Prepayment activity – now driven primarily by housing turnover – climbed 14 percent, but remains 29 percent below last year’s level

The Power Of Artificial Intelligence In The Mortgage Industry

These days, it’s hard to miss the buzz about artificial intelligence (AI) and its impact on industries such as health care, automotive, education, financial services and retail, to name just a few. From the ability to diagnose diseases – to the development of driverless cars – the potential applications of AI are extraordinary. In our daily lives, we already are experiencing the use of AI when we communicate with customer-service chat bots, ask Apple’s Siri for information, perform Google searches, or use navigation apps to help avoid traffic, as a few examples.

Despite all the recent discourse about AI, this technology is certainly not new. There are countless examples of AI use over the past several decades, including the reliance of commercial jet flights on AI to power autopilot, and internet bots that index web pages. But the more recent interest, innovation and investment in AI are due to a combination of factors – including greatly increased computational power, big data, greater infrastructure speed and scale, open source technologies and advancements in machine learning techniques.

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And today, the mortgage industry is able to reap the benefits of this incredible technology. For example, HeavyWater, which was recently acquired by Black Knight, is a provider of AI and machine learning-based capabilities specific to the financial services industry. The company has already built a platform that completes business tasks using synthetic read-and-comprehend analysis and conclusion skills, and applied these capabilities to the loan origination process.

What is Machine Learning?

The terms “machine learning” and “artificial intelligence” are often used interchangeably, however, there is a distinction between the two. Using a very broad definition, artificial intelligence replicates human reasoning through learning, problem-solving and pattern recognition. Machine learning is a subset of AI and is a process by which AI deepens its knowledge through continually performing tasks and processing information.

Let’s consider a simple, industry-specific example. AI-powered machine learning enables technology to “remember” standardized forms. For example, it can review thousands of paystubs and determine exactly where the pertinent income data is located. When the system comes across a paystub that presents an anomaly, it will apply its previously gained understanding to infer the location of the income data needed. Once the technology receives feedback that its inference was correct, it incorporates that information into its knowledge base. The next time it comes across that type of paystub, the system will automatically know where to find the pertinent data.

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Machine learning also leverages big data to gain insights. The more data that is collected and reviewed, the better machine learning solutions become at making predictions.

Applying AI and Machine Learning to Reduce Costs and Turn Times

AI and machine learning already can make a difference in two of the biggest challenges faced today by mortgage originators: costs and cycle times. With the ability to read, comprehend, and draw conclusions based on context, AI and machine learning can perform operational functions more efficiently and at scale.

In fact, machine learning can work on many of the labor-intensive, “stare and compare” tasks performed by humans – such as verifying income, assets and insurance coverage. Machine learning is used to perform these manual activities much faster and more accurately than humans – a task that takes employees hours to complete can be reduced to just seconds with machine learning.

By automating manual routines, machine learning not only expedites the origination process, but also increases volume. While humans can only work a certain number of hours before mistakes begin occurring, machine learning has no limits to the time or energy it can spend performing these tasks. By increasing loan processing volume and reducing mistakes, imagine how machine learning can drive down origination costs – and risk.

AI-powered systems enable processors and underwriters to dedicate more time to addressing exceptions and solving problems, which will improve transaction turn times. Also, AI can help avoid last-minute delays by prompting a lender’s staff to take early action when there is an issue, keeping the origination process moving forward. Additionally, by delegating work to AI-powered technology, a lender’s staff can focus on delivering a more positive and personalized consumer experience.

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As AI and machine learning are used to perform manual, repetitive tasks, allowing mortgage professionals to work on more value-added responsibilities, lenders can increase their focus on their company’s growth strategies. As they scale and reduce the cost per loan by keeping staffing levels flat, lenders can invest more in product development, marketing, infrastructure, and other growth-oriented initiatives.

Additional Applications of AI

AI can also leverage visual recognition to image and index a wide variety of documents that are typically reviewed by processors and underwriters, such as tax returns, W-2s, property titles and appraisals. A lender could even use AI and machine learning to better manage vendors. Based on past performance and cost, AI could provide recommendations on which vendors would be optimal for each loan going through the origination process.

Voice-integrated AI brings further opportunities to create efficiencies. This technology could look at information under review, evaluate results and automatically employ interactive communication bots to advise employees of any issue that may need attention. Additionally, via a conversational interface, processors and underwriters could ask for information they need – just as we use virtual assistants like Apple Siri, Amazon Alexa or Microsoft Cortana to get answers. These capabilities certainly could help move a loan through the origination process faster.

Leveraging AI to Enhance Customer Service

Of course, most of us have experienced first-hand how AI is applied in retail to deliver a more personalized consumer experience. For example, when we shop online, we receive targeted product recommendations the next time we visit that site; or receive faster service though chat bots.

To help personalize and enhance the borrower’s experience, lenders can leverage voice capabilities. A mortgage virtual assistant that engages customers by answering questions, walking them through the application process and even offering advice could be employed using voice-integrated AI.

Impact on Jobs

When the subject of AI in the workplace is discussed, it inevitably raises questions about its impact on jobs. Will jobs be lost as a result of these technological advancements?

There is no perfect answer to this question since the utilization of AI is different from company to company. But, it seems certain that future skill sets will be required to support this shifting technological paradigm. As it applies to the mortgage industry today, however, AI can enable professionals to spend less time on remedial work, becoming knowledge workers instead of task executors, and provide additional value to a company.

The Future Is Limitless

AI and machine learning offer tremendous potential to advance the mortgage industry, and we are just beginning to experience the technology’s capabilities. As AI-powered systems ingest more data and perform an increasing number of tasks though machine learning and other techniques, the possibilities are unlimited.

Imagine the power of AI as it learns to handle the entire point-of-sale process and speaks to an applicant directly through a mobile phone; or as it systematically searches a lender’s portfolio for qualified prospects and offers a customized home equity loan or line of credit, and so on. As we all know, the average cost to originate a mortgage loan is exceptionally high – today it is nearly $8,500 according to the Mortgage Bankers Association’s Quarterly Mortgage Bankers Performance Report, and the typical time to close a loan is 41 days. Any opportunities to reduce costs and increase process efficiencies will add value to lenders and consumes.

What’s more, the transformative power of AI doesn’t stop in the originations space. Servicers will also be able to reap the benefits of this advanced technology. For example, the technology could learn how to detect risk and any compliance issues before they occur, enhance loss mitigation decisioning, provide voice integration capabilities to help staff work faster and smarter, and so on. What’s amazing is that these examples only scratch the surface.

Of course, human interaction will always be needed to originate and service loans, as people will still decide how they want to leverage technology and determine the problems that must be solved. Humans must also still play an active role in loan decisioning, identifying which kind of data to consider and determining risk appetite. Furthermore, research indicates that despite all the advances in point-of-sale technology, consumers still want the comfort of human interaction at some point in the process of purchasing what is most likely their largest and most important investment.

AI and machine learning offer great promise and will likely usher in a new era of production excellence. Lenders that take advantage of this advanced technology will be choosing a bold new way to address origination costs, improve turn times and transform their origination processes to support a brighter, more successful future.

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May 2018 Sees Second Fewest Foreclosure Starts In 17 Years

Data from Black Knight showed that May saw the second fewest foreclosure starts in more than 17 years (only December 2017 had fewer…by about 500), and the number of loans in active foreclosure continues to fall. Just 303K mortgages are currently somewhere in the foreclosure process, a 15-year low. At the current rate of improvement, foreclosure activity will be back at pre-crisis averages (2000-2005) early in Q3 of this year.

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Meanwhile, improvement continues among hurricane-affected mortgages. In fact, delinquency improvements in hurricane-affected areas more than offset slight increases seen elsewhere, bringing the national delinquency rate down to its lowest level in 15 months. All in, hurricane-related delinquencies fell by nearly 30K in May (-20%) with the highest rates of improvement in Irma-affected areas of Florida (-27%).

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Black Knight Looks To Revolutionize Mortgage Servicing

Black Knight, Inc. has introduced LoanSphere Servicing Digital, a powerful and innovative new solution to help mortgage servicers deepen customer relationships and increase retention. LoanSphere Servicing Digital delivers detailed, timely and highly personalized information to customers about the value of their homes and how much wealth can be built from these real estate assets. A consumer-centric solution, this interactive tool gives customers the ability to easily perform tasks and find information related to their mortgages, while providing a platform for continual engagement between servicers and their customers.

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“Our goal with LoanSphere Servicing Digital is to give our servicing clients an engaging, consumer-centric tool for customer retention,” said Anthony Jabbour, CEO of Black Knight. “For many people, a house is the single greatest asset they’ll ever own. With that in mind, and employing a ‘design thinking’ approach, we’ve developed a solution that lets our clients provide their customers with ongoing, detailed information about their loans and homes, as well as the tools to help manage the wealth they have built in their homes.”

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Offering useful information specific to a customer’s mortgage, property and local housing market, LoanSphere Servicing Digital gives customers the tools to make more informed financial decisions related to their homes. In delivering this information, Black Knight draws upon the servicer’s data via the company’s comprehensive, end-to-end LoanSphere MSP system, as well as Black Knight’s industry-leading property records database; advanced analytics; and automated valuation models. The app – which features loan, home and neighborhood dashboards – presents information in a clear, intuitive design, with easy-to-use navigation that has been built for and tested by consumers.

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“Increasing engagement and providing tools that add value are key to deepening the servicer-customer relationship,” said Joe Nackashi, president of Black Knight. “By providing anytime, anywhere access to an array of customer-specific information and functionality, LoanSphere Servicing Digital enhances the consumer’s servicing experience and adds value on an ongoing basis, which results in higher retention rates.”

LoanSphere Servicing Digital provides customers with easy access to specific information about their mortgages, such as type of loan, interest rate and estimated PMI drop date. It allows mortgage customers to make payments, view detailed payment history and perform other self-service functions within the application. Customers can also explore various “what-if” scenarios, including options for building equity more quickly or the relative benefits of paying down or refinancing their loan. In addition, LoanSphere Servicing Digital provides up-to-date and valuable neighborhood information, such as recent sales, local school data and demographics, as well as transaction and lien history on the property.

The white-labeled solution can be branded to match the servicer’s brand identity, and will be offered as both a native mobile app and responsive web design. By providing loan and home information to customers wherever they are, when they need it most, LoanSphere Servicing Digital helps servicers regularly engage customers with insightful, value-add information that enhances the borrower relationship and supports customers’ financial well-being.

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Home Equity Is On The Rise

Industry dynamics are improving in some areas. For example, this month Black Knight revisited the nation’s equity landscape, finding that as home prices continued to increase so has the amount of tappable, or lendable, equity available to Americans with mortgages. Black Knight defines tappable equity as the total amount of equity a homeowner with a mortgage has available to borrow against before reaching a maximum loan-to-value ratio (LTV) of 80 percent. As Black Knight Data & Analytics Executive Vice President Ben Graboske explained, rising home prices have pushed the total amount of such equity to a record high.

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“As home prices continued their upward trajectory at the national level, the amount of tappable equity available to homeowners with mortgages continued to rise as well,” said Graboske. “Tappable equity rose by $735 billion over the course of 2017, the largest calendar year increase by dollar value on record. At $5.4 trillion, total tappable equity is also the highest on record and 10 percent above the previous, pre-recession peak in 2005. An estimated $262 billion in tappable equity was withdrawn in 2017 via cash-out refinances and home equity lines of credit (HELOCs), also reaching a new post-recession peak. Still, Americans seem more reserved in tapping their equity than in years past, withdrawing less than 1.25 percent of all tappable equity available in Q4 2017 – a four-year low. Of that total, 55 percent was tapped via HELOCs, the second lowest such share since the housing recovery began. However, as interest rates rise, it is likely that we will see the HELOC share of equity withdrawals increase as well.

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“At the start of 2018, some 55 percent of all tappable equity was held by borrowers with first-lien interest rates below the going 30-year rate. Following the nearly 50 basis points rise in interest rates we’ve seen since the start of the year, that share has ballooned to 75 percent. While rising rates tend to dampen utilization of equity in general, the market is poised for a strong shift toward HELOCs, as they allow borrowers to take advantage of growing equity while holding on to historically low first-lien interest rates. Over half of all tappable equity – approximately $2.8 trillion – is held by borrowers with credit scores of 760 or higher and first-lien interest rates below today’s prevailing rate, which creates a large pocket of low-risk HELOC candidates.”

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The data also showed that risk remains relatively low among cash-out refinance originations as well. The average cash-out refinance borrower in 2017 had an average credit score of 744 (down from 750 in 2016) and pulled $68,000 in equity (up from $64,000) with a resulting loan-to-value ratio (LTV) of 66 percent. Approximately 40 percent of remaining cash-out refinance candidates – those borrowers with both tappable equity and current first-lien rates of 4.5 percent or higher – have credit scores above 760. As borrowers with higher credit scores tend to have higher average equity amounts, approximately 50 percent of all tappable equity among borrowers with first-lien rates of 4.5 percent or higher is held by that group.

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