AVMs are software-based pricing models that often use public records to estimate the value of a home or other real estate. Commonly known AVMs used by consumers are Zillow’s Zestimate and Redfin’s Estimate, but there are many more of these sophisticated AVMs on the market.

“The difference in predicted home values that consumers see and the accurate values that often take them by surprise could be due to a disconnect between marketing and underwriting goals.”

Today, it’s an accepted norm to show homebuyers or sellers an automated home value that is accurate enough to get their attention. But these values often fail in the level of accuracy needed to be usable for a consumer’s financial transaction. This results in a rude awakening for a seller who expects to list their home at the value they’ve grown accustomed to — or a homeowner who is dreaming about the project they can finance with their current level of equity, only to be approved for a lower loan amount.

Misleading information

The difference in predicted home values that consumers see and the accurate values that often take them by surprise could be due to a disconnect between marketing and underwriting goals. There are many business reasons why marketing and underwriting operate in silos. Highly accurate AVMs built for underwriting purposes cost more to produce than a marketing-first approach.

Marketing-based AVMs are often displayed for free to consumers in order to generate leads. Some companies may even go as far as showing a less accurate value as clickbait, hoping the homeowner will become a lead just to fix what they know to be true about their home.

It’s time to rethink this current norm, especially in today’s market where affordability is a challenge for so many. AVM providers have a larger opportunity to reduce friction in housing finance and provide viable alternatives to labor-intensive processes.

“AVMs that harness large amounts of data can perform more calculations than a human in a matter of seconds, and they provide an objective approach to value.”

Known low-quality values ultimately erode consumer confidence, particularly if no context of value accuracy is provided. To make confident decisions involving real estate, both accuracy and transparency are needed to understand a property’s value. AVM solutions need to provide a precise indication of confidence that allows someone to act upon the value with an expected outcome. It matters not only to real estate professionals but to consumers, financial institutions and investors.

Transparent approach

When consumers are shopping for homes or checking the value of their own home on their favorite site, an expectation has been created that an answer will always be available. This helps to create loyalty and confidence for return visits.

Imagine if you typed questions into Google and the searches frequently turned up zero results. Chances are, you would look for a different search option. It’s better to see something — even if it’s irrelevant to your original search — than to see nothing. The human brain wants to see results.

The problem comes when these results are not accompanied by some context of how accurate the model’s prediction actually is. AVMs are typically designed to predict a fair market sales price or an appraised value for a given property. Predicting the outcome before a sale or appraisal has happened can be a powerful tool for gauging the timing of getting a loan, listing a home or making a purchase. Knowing with certainty that a value is accurate, and that there’s a high likelihood it will be within a margin of error for a future sale or appraisal, is what actually empowers consumers to act upon the data.

Telling a friend you are 50% sure you will arrive at their house on time for dinner, versus being 98% sure, will probably change their expectations and actions. A homeowner equipped with the knowledge about the accuracy of an AVM can make informed decisions about their finances. They can better choose the timing to take advantage of their home equity, which might enable them to renovate their home, consolidate debt or send a child to college. Potential buyers with this context can ensure they are financially prepared to purchase a home. This reduces friction in the process and ultimately leads to fewer failed transactions.

With a transparent approach to communicating confidence, there will be an increased need for highly accurate AVMs to be used directly by consumers, instead of today’s two-tiered approach. Valuation accuracy can be the difference between a frustrated, discouraged homebuyer and a well-informed one. The combination of cloud-computing power, more available property data and modern technologies such as machine learning make it possible for AVM providers to increase accuracy while providing confidence in the value prediction.

Increased usage

AVMs are being increasingly used in home equity lending. With increased accuracy, instant results and lower costs compared to an appraisal, AVMs are especially suited for underwriting these loans.

When lenders market to consumers using a highly accurate AVM, they present consumers with realistic expectations. When an AVM is sufficient to satisfy underwriting requirements — typically on smaller loan sizes — it creates a more streamlined lending process and leads to better borrower outcomes.

For conforming mortgages, AVMs are not currently accepted as a replacement for an appraisal, but they can be used by underwriters in tandem with an appraisal to verify a home’s value and flag for overvaluation or undervaluation. AVMs that harness large amounts of data can perform more calculations than a human in a matter of seconds, and they provide an objective approach to value.

Historically, AVMs have been blind to a property’s current condition, which is why pairing them with a physical inspection has been key for using AVMs in lending decisions. Recent advances by innovative AVM providers and AI photo technologies have evolved the approach to include property condition as an input to the model, which produces a more accurate result. As AVMs become more accurate over time, underwriters will be able to rely on them further, and they will be used to determine whether an appraisal is required for the level of risk tied to a specific loan.

Accurate picture

Accurate AVMs can also change the way mortgage servicers interact with borrowers. AVMs help to determine when a homeowner can remove their mortgage insurance, assuring that borrowers aren’t paying for it longer than necessary. Removing the insurance requirement can help a borrower reduce their monthly payment and better understand their current equity position.

The low cost of AVMs also means that the values of properties in a servicing portfolio can be updated more frequently. This provides better tools that enable servicers to deliver the right options to current borrowers and inform them of additional opportunities to make use of their equity.

Close to 90% of mortgage holders have interest rates lower than 6%. This has created a lock-in effect where homeowners are prone to focus on improving their current property using available equity rather than moving to a different home. Servicers that use accurate AVMs can play a big part in empowering homeowners to understand all of their options and make good decisions.

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It’s understandable that some would see benefits to providing consumers with mediocre information all of the time, rather than great information some of the time. The recent evolution in AVM innovation means that there is no longer a need to compromise.

Machine learning, data availability and low-cost, massive computing power provide the ability to move past today’s two-tier system and focus on giving consumers direct access to underwriter-quality AVM values. This is an exciting development for homeowners and prospective buyers alike. Whether you’re a lender, originator, underwriter, servicer, investor or consumer, it’s OK to raise the expectation of accuracy rather than deal with the norm of missed expectations. And that is good news for everyone. ●

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By adopting a proactive approach and leveraging the right technology tools, originators can not only survive but thrive in this environment. The real estate market has always been cyclical, with ups and downs. The current downturn, however, presents unique challenges. With fewer homebuyers in the market and stricter lending criteria, originators are finding it harder to close deals.

“Instead of waiting for the market to turn around, proactive originators are looking for ways to maximize their efficiency and reach potential buyers more effectively.”

This situation calls for a shift in strategy. Instead of waiting for the market to turn around, proactive originators are looking for ways to maximize their efficiency and reach potential buyers more effectively. This is where successful mortgage professionals use technology to grow business for themselves. Here are some of the freshest technologies that cutting-edge originators use to grow their mortgage business.

Pioneering AI

LoanPass.AI is pioneering the use of artificial intelligence in the U.S. lending industry with its AI chatbot. This technology serves as an automated loan officer, enabling borrowers to confidentially shop, price and lock in loan approvals. The chatbot is designed to interact with borrowers using a set of customized questions that lead to deterministic responses from a product and pricing engine, which contains each lender’s specific pricing, eligibility and underwriting guidelines.

 The technology positions LoanPass as a transformative tool in the mortgage industry, offering lenders a way to enhance their operational efficiency, reduce costs and improve customer engagement, all of which are critical factors to business success. The company’s chatbot automates the loan generation process, which traditionally required a loan officer or call center.

This automation allows for efficient connections of borrowers to lenders, potentially increasing the volume of loans processed. Lenders can customize the chat sessions in real time, from the questions asked to the decisioning rules applied, giving them control over the loan origination process and the ability to tailor it to specific business needs.

The chatbot combines three knowledge sources: the lender’s product, pricing and eligibility guidelines; a general information knowledge base; and the extensive knowledge from ChatGPT. The chatbot can provide quick, accurate and personalized responses to enhance the borrower’s experience.

Indispensable insights

In today’s digital age, data is king. Mobility Market Intelligence (MMI) provides indispensable mortgage and real estate data, allowing originators to make data-driven decisions. In addition, building and maintaining referral relationships with real estate agents is crucial to lead-generation efforts for lenders and originators.

Given the vast number of agents, originators often find it challenging to optimize their referral outreach. To address this, many lenders are turning to data intelligence tools. MMI is a top player in providing data intelligence for the mortgage and real estate sectors.

It offers an extensive database that allows enterprise clients to pinpoint partner networks that align with their goals. It provides detailed insights into lenders, originators and real estate agents based on regions and specific transactions. By understanding market trends and buyer behavior, originators can tailor their strategies to meet current demands.

MMI assists originators in the competitive real estate market by helping them identify potential real estate agent partners and differentiate themselves from competitors. Features include the ability to search and filter agent transaction data, track agents with new daily listings, and inspect the LO Agent Wallet Share to see what percentage of an agent’s business they receive.

MMI provides tools for originators to stay in touch with borrowers in meaningful ways. This includes listing alerts for properties they’ve dealt with, custom property intelligence pages for borrowers and triggered alerts when mortgage credit checks occur.

Seamless journey

SimpleNexus was acquired by nCino in January 2022 and underwent a rebrand in September 2023. The nCino Mortgage Suite now provides the best of both worlds to mortgage lenders and originators. The system is designed to enhance the profitability of financial institutions.

The mortgage process can be complex, with multiple stages and stakeholders involved. The nCino Mortgage Suite simplifies this process by offering an end-to-end solution that automates various stages along the way. The robust platform brings together elements of the mortgage process, offering financial institutions a streamlined, efficient and data-driven approach to mortgage lending.

The suite streamlines the entire mortgage process, from origination to closing, making it more efficient and reducing manual tasks. In today’s fast-paced world, borrowers demand convenience. By offering a modern and seamless customer journey, nCino enhances the overall experience.

Originators have access to valuable data insights, and they can leverage this data to make informed decisions and improve their strategies. The suite provides an automated solution for managing incentive-based compensation, eliminating the need for complex spreadsheets and chaotic emails.

Social outreach

Social media is a powerful tool for reaching prospective clients, but it can be time-consuming and challenging to navigate. SocialCoach offers a solution that keeps lenders compliant across social media platforms. The system is a comprehensive social media management platform tailored for the mortgage industry.

It aims to simplify and automate social media marketing for originators, ensuring that the content they post is compliant with industry regulations and standards. The platform is trusted by leading brands in the mortgage sector.

SocialCoach allows originators to post and schedule content directly to their social media accounts, saving them hours of work every week. It supports various platforms, including Facebook, Instagram, LinkedIn and TikTok. This functionality ensures consistent online presence and compliance.

With the Video Catalyst tool, originators can tap into the power of TikTok and Instagram Reels. SocialCoach writes video scripts, and after an originator records from their phone, the platform takes care of editing and posting. The platform offers features to ensure that the content posted by originators is compliant. It provides built-in compliance parameters, allowing companies to decide which terms are flagged or blocked.

SocialCoach offers an analytics dashboard where companies can view activity and engagement metrics. This includes companywide social insights and personalized metrics for each originator. Understanding these key performance indicators for social media are crucial.

Proactive mindset

Arguably, one of the most important ways that successful mortgage professionals are moving the needle in this market is through mindset. Success in the current market requires a proactive mindset. Originators must actively seek out opportunities to grow their mortgage business. They need to leverage technology tools to maximize efficiency.

Originators must be willing to continuously learn and adapt. By embracing this mindset and utilizing key technology tools, they can better ensure that every minute spent translates into business growth. Here are three easy ways to start a mindset shift.

First, educate yourself continuously by staying up to date with the latest industry trends, regulations and technologies. Attend workshops, webinars and conferences to expand your knowledge by learning from industry experts.

Next, set clear goals. Define your short- and long-term objectives. Break down these goals into actionable steps and monitor your progress regularly. Third, actively network with Realtors, homebuilders, financial planners and other industry professionals. A strong network can provide referrals and insights into market trends.

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The bottom line is that current real estate market conditions demand that mortgage originators be more strategic and efficient with their time. By leveraging tools like LoanPass, Mobility Market Intelligence, nCino’s Mortgage Suite and SocialCoach, originators can navigate market challenges, optimize their operations and see tangible growth in their business. The future belongs to those who are prepared with the right tools and mindset. These loan officers will not only survive but thrive. ●

The post Seize the Future with the Right Attitude and Tools appeared first on Scotsman Guide.

“Today, small-business lenders are potentially facing increased costs for expanded data requirements.”

Since the late 1970s, banks and other lenders have been reporting key pieces of information regarding lending practices to regulatory authorities under requirements of the Community Reinvestment Act (CRA) and Home Mortgage Disclosure Act (HMDA). The goals of these programs are to increase transparency in lending and to ensure that banks are meeting the needs of the communities in which they are located.

Today, small-business lenders are potentially facing increased costs for expanded data requirements. The Consumer Financial Protection Bureau (CFPB) has issued a proposed rule for Section 1071 of the Dodd-Frank Act that, when finalized, would require financial institutions to collect and report more information about small-business credit applications.

The draft of the regulation released last year states that the new rules are intended to provide a comprehensive view of small-business lending, and to make sure that banks and nonbanks are serving small businesses fairly. The new rules also call for reporting on minority-owned, women-owned and LGBTQ-owned businesses.

The prospective rules to Section 1071 face various legal challenges, some of which argue that the proposal is too burdensome and expensive. At the same time, there is a congressional push to block the CFPB’s proposal, with the House and Senate recently passing a joint resolution to overturn the new Section 1071 rules. President Joe Biden reportedly intends to veto the legislation.

Automation efficiencies

Whatever the final rule requirements end up being, it appears that more scrutiny of small-business lending data is on the way. This has resulted in many bank and nonbank lenders turning to data validation automation as an alternative to scaling up compliance and quality control teams.

Recent legal challenges may have put the timing of the final ruling in flux, but preparation is still crucial for financial institutions. They must address increased regulatory requirements in a challenging macroeconomic environment that also demands finding creative ways to cut costs and become more operationally efficient.

Confidence in data accuracy is key for the preparation to comply with Section 1071. Bank and nonbank lenders should assess their current workflows, identify any gaps — including their ability to collect data — and identify any limitations in their current business systems. It is vital to understand the capacity, constraints and requirements for 1071 reporting while also understanding the organization’s banking systems and how data is currently collected and stored.

A common challenge for mortgage lending workflows is having an efficient way to request and organize borrower documents into a loan origination system (LOS). Automation can ease the inefficiencies tied to document organization by automating the classification step. This will help ensure that documents are in the right place and allows the system to automatically place documents in folders, repositories or loan origination systems. Many times, documents are received and combined into one file, such as a large PDF format, that must be broken down into individual sections and filed into their correct digital folders.

Furthermore, loan processes often require manual “stare and compare” of borrower documents. In other words, mortgage professionals are forced to look at documents side by side and visually discern the differences. This method leads to mistake-prone loan documents, frustrated borrowers and lengthy processing times.

Decreasing manual steps

Automation drastically decreases the time spent on manual labeling, sorting, stacking and reviewing of loan packages, and it eliminates manual data entry. It provides a lift for the underwriting team by automatically extracting key information from small-business financial documents, including tax returns, and allows for faster credit decisions.

Automated loan processing and onboarding, in addition to the collection and validation of data, simplifies downstream audits such as preclosing and post-closing checks. It will also help with compliance audits from HMDA, CRA and the expected regulations in Section 1071 of the Dodd-Frank Act.

Machine learning can automatically extract key data from loan packets, check for missing information or signatures, and compare it to the system of record. It can also make sure the data is congruent across both the systems and final loan package.

The automation process can take these audits even further by triangulating data points across multiple sources. For example, in the case of a compliance audit, data validation processes will be expanded to look at the documents, the LOS, a document repository and, ultimately, a compliance report such as the CRA loan application register or a future Section 1071 loan application register. The method ensures higher-quality reporting, storage of documents and accurate data across multiple systems.

The typical time and cost savings from automated commercial mortgage lending improves margins by cutting expenses on a per-loan basis. As time spent on tasks decreases dramatically, so can the cost per loan. This will allow financial institutions to position themselves to be fully prepared for audits, changes in the market and expansion opportunities. It will also speed up the adoption of changes in regulatory requirements by creating a completely scalable compliance review and reporting process.

Managing labor costs

To satisfy upcoming CRA modernization and Section 1071 requirements, commercial mortgage lenders are faced with the option of greatly increasing the size of their teams (and adding the associated costs) or investing in automation for a fraction of the cost. Not only can automation easily find, consolidate and track reported data points under the expected requirements to comply with Section 1071, but it also allows lenders to retain key compliance employees while attracting new and experienced compliance professionals.

Compliance professionals carry the heavy burden to report accurate data, which can lead to mountains of manual data reviews. Such work can create a vicious cycle of burnout and rising costs. There is a significant amount of change management required for institutions to train commercial mortgage originators, underwriters and others to ensure all data points are collected at application.

The use of automation drastically reduces human errors, providing data integrity and confidence in meeting regulatory requirements. Consequently, compliance professionals can focus on higher-value tasks and lenders can avoid investing in additional compliance personnel in a challenging labor market. Efficiencies created by automation bring cost savings across the commercial mortgage lending process, supporting loan growth without the need to hire additional employees.

Avoiding risk

The legal fight involving Section 1071 will most likely be decided by the U.S. Supreme Court. However the case ends, there still exists a ticking clock for commercial mortgage lenders to prepare their data for the level of accuracy required to meet government regulatory criteria. If the data is not properly managed, prepared and presented, it could adversely affect lenders through reputational risk and large fines.

Risk mitigation is undoubtedly the most important aspect of increased compliance regulations. Future-proofing with automation allows for a full loan review instead of having to use a risk-based approach of sampling. Automation reduces false positives by limiting human interference, so humans only look at exceptions instead of all documents, further decreasing risk for the institution.

The automation of compliance with CRA and Section 1071 regulations ensures high rates of accuracy. Consequently, financial and reputational risks are mitigated by ensuring that fair lending analyses are conducted with accurate and timely data.

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As lenders cautiously prepare for future regulations, a solid foundation of automation will provide them with the confidence needed to emerge from the new regulatory requirements with more efficient methods of operation. Financial institutions owe it to their customers and employees to be ready for the changing regulatory environment. Those that adapt with new ways of solving challenges will emerge with a competitive advantage in the world of small-business lending. ●

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Evolving into a nationwide phenomenon with meaningful benefits for both investors and the communities they serve, the real estate investment landscape experienced steady growth between 2013 and 2023, primarily due to local mom-and-pop investors. Growth in real estate investment also creates opportunity for mortgage originators. These deals can be funded with residential transition and debt-service-coverage ratio (DSCR) loans.

“Despite the near-term headwinds in the market, the future is bright for real estate investors.”

 Knowledge of this market is a useful tool if you currently offer these products or are considering them for your arsenal. Real estate investing has a deep history, presents unique loan scenarios and promises a bright future.

Market history

Fix-and-flip home renovations might be a ubiquitous concept today, thanks to HGTV. But the phenomenon of acquiring properties to update and resell them really took off in the 1980s, when economic downturns and dwindling stock market returns led to a surge in home foreclosures.

Rather than let these properties go to waste, investors took notice of the profitable opportunity. They began purchasing foreclosed homes with the intention of renovating and reselling them for a profit once the housing market showed signs of recovery.

Throughout the 1980s and ‘90s, this trend was propelled by private financing that fueled a growing interest in renovation of older properties. Inspired by TV shows like “This Old House” and encouraged by emerging retail giants like Home Depot and Lowe’s, many new homeowners began to undertake DIY renovation projects that paved the way for the YouTube channels and TikTok videos of the modern era that are focused on house flipping.

Today, flipping is seen as a viable profession as investors have gotten the cycle of purchasing, renovating and reselling properties down to a science. The financial crisis of the late 2000s triggered a surge in private debt as the primary financing source for individual real estate investors, especially in the fix-and-flip sector. Private lenders began offering short-term bridge loans that became a hit for borrowers, due to the quick approvals and more lenient credit criteria when compared with traditional financing methods.

Trends over time

According to real estate analytics company Attom, the percentage of homes purchased for flipping purposes rose from 5.8% in 2020 to 8.4% in 2022. These investments yield varying returns but generally prove profitable, with year-end 2022 Attom data showing an average gross profit of $67,900.

Where these investments are happening has changed a lot in the past decade. The most popular residential markets of 2013 — including Houston, San Francisco, and Bethesda, Maryland — have now been superseded by markets like Atlanta, Raleigh and Dallas-Fort Worth, which topped the National Association of Realtors’ 2023 list of the hottest markets. Fix-and-flip transactions have increased from 4.6% of all U.S. single-family home sales in 2013 to 7.2% of all sales in third-quarter 2023. The current wave of fix-and-flip activity is being driven by several trends.

First, an increasing number of households are seeking move-in ready homes, driving the demand for renovated single-family properties. Existing home inventory has decreased steadily over the past decade. Although supply entered an upswing from the ultra-low inventory early in the COVID-19 pandemic, the 3.6 months of supply at the current sales rate in October 2023 was down from five months a decade earlier. Low supply has created opportunity for real estate investors to provide housing solutions.

Second, 60% of real estate investors are small-scale, mom-and-pop investors and business owners who prioritize investments in their local communities, according to Kiavi data. They play a pivotal role in revitalizing neighborhoods through renovation and repurposing of under-improved homes, bringing a community-based mindset to their projects.

Third, sustained demand for rental housing has created a steady stream of cash flow for real estate investors. The number of single-family renter households has increased from 40.2 million (or 30%) of all U.S. households in 2013 to 45.2 million (or 35%) in 2022, per census data. This provides investors with a consistent source of income.

Lastly, the aging U.S. housing stock presents an opportunity for real estate investors to renovate older homes and meet the growing demand for turnkey properties. This helps create more affordable housing options and future opportunities for homeownership.

Short-term forecast

Technology and data are the future of real estate investing. Data-driven technologies, including advanced artificial intelligence (AI) and machine learning models, are set to empower investors by dismantling traditional borrowing hurdles, automating time-intensive processes, and delivering swift and more tailored financing.

Today’s borrowers want access to personalized, transparent pricing and on-demand capital. Advanced technologies synthesize extensive data sources to reveal insights that enable investors to make faster, more informed decisions. Precise assessments of factors such as after-repair value ultimately increases the likelihood of success for each project. This efficient use of data clarifies potential risks and rewards, providing a structured pathway for investors to have lucrative and successful projects.

AI and machine learning models are becoming more sophisticated, providing clarity to lenders about primary risk factors tied to the investor, property and local market conditions. These models create better overall outcomes from an underwriting perspective and empower investors to successfully exit projects.

Although interest rates won’t be returning to historic lows anytime soon, there remain plenty of opportunities for investors looking to grow their business. Given the low inventory of homes for sale and the number of buyers looking for move-in ready homes, fix-and-flippers did well in 2023. And we can expect to see this trend continue in the coming years.

Despite the near-term headwinds in the market, the future is bright for real estate investors. From fix-and-flip projects to long-term rentals and new construction, each real estate investment helps to revitalize neighborhoods and provide much-needed turnkey housing, all while enabling investors to achieve their wealth-creation goals. ●

The post A Decade of Transformation and Growth appeared first on Scotsman Guide.

These tools, as they continue to advance, are projected to drive a 7% (or $7 trillion) increase in global gross domestic product and boost productivity growth by 1.5 percentage points over a 10-year period, according to Goldman Sachs. Even now, AI and machine learning are revolutionizing the mortgage sector by streamlining processes, improving risk assessment and reshaping the lending landscape.

“Welcome to the future of mortgage origination — a future where AI and machine learning spearhead progress.”

These technologies are making processes more efficient, fueling an era of increased accuracy, reduced risk, and better experiences for lenders and borrowers. Allied Market Research reported that the global mortgage market, which generated nearly $11.5 trillion in 2021, is projected to reach $27.5 trillion by 2031, with a compound annual growth rate of 9.5% from 2022 to 2031. A main driver for this projected growth is the increased investment in software that speeds up the mortgage application process.

Navigating the complexities of this technological evolution will enable the mortgage industry to examine some of its existing challenges while ensuring that the benefits of AI are realized without compromising ethics or fairness in lending practices. Welcome to the future of mortgage origination — a future where AI and machine learning spearhead progress.

Seismic shift

The loan origination process has historically been a labor-intensive and time-consuming effort. Mortgage originators have had to scrutinize mountains of paperwork, verify financial documents and manually evaluate creditworthiness — a lengthy process that could take several weeks. The arrival of AI and machine learning, however, has brought about a seismic shift in how this process is executed, offering a host of benefits.

One of the most notable advantages of AI and machine learning in mortgage origination is the automation of repetitive tasks. Intelligent algorithms can now handle tasks such as data entry, document verification and information extraction that once required substantial human involvement. This cuts the workload for mortgage originators and reduces the chances of errors that accompany manual data entry.

The loan origination process also becomes considerably more efficient with AI and machine learning. Algorithms can analyze massive quantities of data in a fraction of the time it would take a human, facilitating faster loan approval times. Borrowers no longer have to endure long wait times for decisions on their applications, resulting in a more positive experience.

 “Ethical AI development is imperative to avoid bias, discrimination and unfair lending practices.”

In addition, AI and machine learning support a more borrower- focused approach. These technologies enable lenders to provide personalized services and faster response times. A borrower can receive real-time updates on the status of their application, the result of a more transparent and less stressful process.

AI and machine learning algorithms can analyze a multitude of data points far beyond what traditional approaches could accomplish. These technologies consider financial data and factors like borrower behavior and online digital history. This broad analysis results in more informed lending decisions, increasing the probability of approved loans that manual processes may have overlooked.

The adoption of AI and machine learning in mortgage origination can lead to substantial cost savings. Lenders can allocate resources more efficiently and reduce the need for extensive manual labor. These savings can be passed to borrowers through lower fees and interest rates.

Weigh risks

Risk assessment is a pivotal stage in mortgage origination. Traditionally, lenders relied heavily on financial data such as credit scores and income verification. Today, AI and machine learning integration unlocks a wealth of digital data sources, offering a complete understanding of borrower risk.

AI and machine learning are expanding risk assessment capabilities by examining a borrower’s online digital history, which comprises social media activity, mobile device usage, payment systems and online transactions. This provides insights into an applicant’s financial behaviors and lifestyle choices that were not previously visible.

AI algorithms identify elusive patterns and anomalies in a borrower’s digital history, enabling highly informed lending decisions. These algorithms can recognize responsible financial behavior and detect potential issues like erratic income sources or unusual spending habits, considerably minimizing a lender’s default risk.

Additionally, AI acts as a vigilant protector, combating fraud by continually monitoring online activities and transactions. AI quickly detects anomalies and suspicious patterns, safeguarding both lenders and borrowers.

AI’s objectivity and consistency decrease the potential for human error, generating more reliable risk assessments. Customized risk profiles tailored to an individual’s circumstances offer a more equitable lending environment while faster decisionmaking benefits borrowers.

Eliminate errors

Mortgage originators can modernize operations and improve lending practices by implementing AI and machine learning solutions. These advanced technologies can contribute to a more equitable and efficient lending ecosystem by reducing costs, eliminating errors and mitigating bias. Responsible AI adoption supports principles of fairness and accuracy in the mortgage industry while producing multifaceted rewards.

Traditional mortgage origination processes are resource-intensive, requiring ample human labor to perform tasks such as data entry and document verification. AI and machine learning automation markedly reduce the need for manual involvement. This improved operational efficiency gradually lowers overhead costs, aiding originators in allocating resources more effectively.

Manual processes are susceptible to human error — and in mortgage origination, errors can be costly. AI and machine learning excel in consistency and accuracy, eliminating the likelihood of errors in tasks that can be automated. This results in a more dependable origination process, benefiting lenders and borrowers by preventing costly mistakes.

Bias in lending, such as digital redlining, is a challenge associated with these technologies. AI and machine learning systems can be designed for transparency, auditability and continuous fairness monitoring. Ethical AI development practices and diverse, representative datasets ensure that lending decisions are based on objective criteria rather than the perpetuation of historic biases. Systematic audits and oversight are key to maintaining fairness and compliance.

Prudent navigation

The adoption of AI and machine learning in mortgage origination produces transformative benefits, but unique challenges call for prudent navigation. Because AI and machine learning greatly depend on borrower data for risk assessment and automation, ensuring the privacy and security of data is paramount.

Lenders must employ robust data encryption, secure storage practices and strict adherence to data protection regulations. Building trust through transparent handling practices is critical to assure borrowers of their data’s safety.

Ethical AI development is imperative to avoid bias, discrimination and unfair lending practices. Using diverse and representative datasets for training, routinely auditing algorithms for fairness, and maintaining transparency in lending decisions are critical steps in establishing ethical AI practices and ending digital redlining.

The highly regulated mortgage industry demands strict adherence to rules and standards. AI and machine learning integrations must align with these regulations, requiring close collaboration with legal experts to certify compliance, particularly when AI-driven decisions have financial implications for borrowers.

Maintaining transparency in lending decisions is of great importance since AI and machine learning algorithms operate in ways that can be difficult to understand or interpret. To build trust, borrowers must have explanations for how these technologies are used in lending processes.

While automation is a key advantage, human oversight remains essential. Striking the right balance between automation and human intervention affirms that AI-driven decisions support organizational goals and consider complex cases or exceptions.

AI and machine learning technologies evolve rapidly. Keeping pace with advancements and adapting systems accordingly are ongoing challenges. Investments in ongoing training — and having a keen eye for evolving best practices — are vital to remain competitive and compliant.

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Integrating AI and machine learning into mortgage origination marks a profound shift in the lending landscape that offers promise, opportunity and challenges. AI and machine learning will modernize the origination process by providing operational efficiencies, faster approval times and better client experiences.

Borrowers benefit from faster decisions while lenders enjoy cost savings and enhanced accuracy. By implementing these technologies responsibly and addressing challenges diligently, mortgage originators can lead the industry toward a more competitive, compliant and borrower-centric future. ●

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Today’s digital landscape has created a particular expectation among consumers regarding their interactions with businesses and industries. Many modern consumers expect secure, seamless and streamlined lending experiences. This is especially true when consumers are being asked for personal data such as income and employment information.

Unfortunately, these consumer expectations are not always met. Some financial institutions utilize income- and employment-verification methods that can prolong the process. This can consume excessive amounts of time and require the consumer to be directly involved in the verification process.

Bank transaction data, user-provided payroll credentials, and paper pay stubs and W-2s are all forms of data that can be used for verifying income and employment. A loan, however, is placed on hold until all verification requirements are completed. The estimated time to return this data to mortgage lenders — and the originators who work with them — can sometimes be days, depending on the borrower’s completion rate and participation.

Faster decisions

Borrowers and lenders should consider leveraging solutions that offer seamless income and employment verification. Utilizing automated digital verification solutions allows for faster lending decisions than with other verification methods, creating an easier verification process for borrowers and lenders.

One significant advantage of instant verification is that it does not require clients to provide private bank account credentials, or to create separate accounts and manage the process themselves. It also lessens the time it takes for lenders to obtain copies of necessary documents.

Under the Fair Credit Reporting Act, lenders must have what is called a “permissible purpose” to obtain employment information. Additionally, consumers can give lenders permission to obtain income information concurrently with their loan application. This means that mortgage lenders can pull an automated employment or income report with little hassle for the borrower or their company.

Tedious tasks

Generally, financial institutions have had preferred ways of managing requests to verify an individual’s employment or income. A lender may handle these requests directly or turn to third-party applications for verifications.

These third-party application processes may require more steps than are necessary in the instant verification process. For example, both bank transaction data and consumer credentialed data ask for consumers to permit access to data at the time of loan application. Consumers are then prompted to access third-party applications to search and link to their financial institution. This may pose several challenges and potential risks.

Third-party applications often ask consumers to provide a username, password or even their bank account number. Following loan approval, these applications may continue pulling and sharing data for both the consumer and the financial institution, unless the consumer proactively revokes access to their bank accounts, which can be burdensome. When working with third-party applications, consumers may not be able to identify exactly what data within their account was accessed.

Granting access to consumer data in this way can be a security risk, especially if consumers share their login credentials for a bank or payroll account. It also makes clients take a more active role in the verification process by making it mandatory for consumers to set up a separate account within a third-party platform.

Employer- or employee-provided verifications also run into several issues. Employers, in some cases, might manage employee requests for verifying income and employment. If an employee decides to apply for a loan, a lender might have to request proof of employment or income directly from an employer. This could lead to a stalled verification process if an employer does not respond immediately to the verification request. Similarly, when employees are responsible for handling their own verification requests, the manual processes can become tedious, especially if an employee must gather proof of employment, or proof of income from pay stubs, W-2s or tax returns.

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Instant and trusted employment and income verification can help a mortgage lender receive timely and up-to-date information. In today’s digital world, lenders should take full advantage of solutions that streamline the decisionmaking process, mitigate risk, and provide borrowers with a faster and more frictionless verification process. ●

The post Remove the Roadblocks for Verifying Income appeared first on Scotsman Guide.

Let’s start with how and why AI is used. The “how” is pretty simple. There are multiple AI services available, including ChatGPT, Claude and Jasper. You might already be using Grammarly, which bills itself as a free AI writing assistance tool.

“There are benefits to AI. If you have writer’s block when putting together a social media post on reverse mortgages, this might be a way to break it.”

Once you create an account (many are free), you can teach it to learn your writing style. Simply type in something to the effect, “Do you want to learn how I write?” The prompt is no more complicated than an internet search bar.

Now for the “why.” AI has been publicized as being wickedly smart and a true timesaver due to its ability to learn and grow in the future. There are specific concerns about AI’s use in mortgage and business lending. These include the potential to insert bias in the mortgage process, as well as possible security and privacy risks. The late physicist Stephen Hawking even warned about AI as a would-be threat to humankind, although these existential worries are thankfully not as immediate.

Overly informal

Setting aside these concerns, ChatGPT was given a writing example and a prompt to produce marketing materials on a loan program. The prompt stated, “Using an informal tone and a humorous example, write a social media post about reverse mortgages.” Here’s what the AI tool produced (emojis were removed):

Hey, peeps! Let’s talk about reverse mortgages, or as I like to call ‘em, “The Robin Hood of Mortgages”! Picture this: You’re retired, living it up in your dream house, but your bank account is starting to resemble a dried-up raisin. Reverse mortgages are like that lifeboat you didn’t even know you had!

Here’s the scoop, without the snooze-fest: Instead of sending your hard-earned cash to Mr. Banker every month, the bank sends YOU a check! It’s like they’re saying, “Thanks for being a homeowner; here’s some cash for your troubles!”

Let’s get down to brass tacks with a hilarious example: Meet Aunt Edna. She’s 75, loves her cozy cottage, and is always up for bingo night. But Edna’s wallet is flatter than a pancake, and she’s thinking, “How can I afford more bingo and maybe a fancy hat collection?”

Enter the reverse mortgage! Edna turns her humble abode into a cash cow. The bank coughs up some dough, and she can keep living in her cottage, rent-free, until the cows come home. The best part? Aunt Edna doesn’t have to pay back a dime until she’s sipping her morning coffee in the great bingo hall in the sky.

But before you start dreaming of all the fancy hats you could buy, remember to chat with a mortgage wizard to understand the ins and outs. Reverse mortgages have some twists and turns you need to know about! So there you have it, folks! Reverse mortgages can be your golden ticket to financial freedom in your golden years. Just remember, with great mortgage power comes great mortgage responsibility! #ReverseMortgageWins #CashForLife

Omitted specifics

First, the obvious: Yikes. Among other things, it’s a very rare occasion that one would address older clients as “peeps.” That writing style is more in line with a teenager trapped in high school in 1995 than a professional mortgage originator’s writing style.

That said, there are benefits to AI. If you have writer’s block when putting together a social media post on reverse mortgages, this might be a way to break it. The technology also wrote the post in about 10 seconds. Reportedly, it does learn a user’s writing style as more examples are put in. In the example above, it might not have had enough previous content to match the requested writing style.

There are pitfalls to AI content too. How was it generated? Is it unique content or does it plagiarize a post from elsewhere? If it’s original, who owns the content? A mortgage originator is potentially on the hook from the Consumer Financial Protection Bureau (CFPB) for advertising unsubstantiated content.

It is also incorrect in stating that “Aunt Edna doesn’t have to pay back a dime until she’s sipping her morning coffee in the great bingo hall in the sky.” With a reverse mortgage, if Aunt Edna dies and her spouse is still living, in an eligible circumstance, they get to continue living in the home as their primary residence. The post also doesn’t speak to the fact that Aunt Edna isn’t allowed to convert the home into an investment property. In most situations, if she moves out, the loan becomes due.

What about using AI to produce a shorter post on X, formerly Twitter, to help drum up reverse mortgage interest? The simple prompt of asking ChatGPT to write a Twitter post on reverse mortgages produced this result:

Considering a #ReverseMortgage? It’s like tapping into the hidden treasure chest of your home equity! But remember, with great power comes great responsibility. Before you dive in, consult a financial advisor (sic) and weigh the pros & cons. Your home, your future! #Retirement

Falls a little flat, doesn’t it? How many likes or shares will that post get? Likely, the inside of a doughnut.

Regulatory caution

What about lead generation? AI companies boast about the ability of these tools to help mortgage originators find leads. Below is what ChatGPT says when prompted, “Write about using AI to bring in new mortgage leads and how to communicate with clients.”

The AI Wand: Turning Data into Leads. Imagine AI as the magician’s wand, waving over a sea of data, and voila — out pops a list of potential clients. AI has the incredible power to sift through massive amounts of information faster than a blink of an eye. It identifies prospects who are actively looking for mortgages, like a hawk spotting its prey from miles away.

The Rabbit Hole: Where AI Finds Your Leads. But where does AI find these leads, you ask? Well, it’s a bit like a scavenger hunt. AI scours social media platforms, real estate websites, and even good old Google to discover people who have shown interest in buying a home. It’s like finding Easter eggs — except instead of candy, you get valuable leads.

AI vs. Manual Search: The Tortoise and the Hare. Now, let’s talk speed. Imagine manually searching for leads as being the slow and steady tortoise, while AI is the speedy hare. The tortoise may eventually find a few leads, but AI has already raced ahead and gathered a bouquet of them. AI saves you time, and as we all know, time is money in the mortgage game.

That all sounds well and good. But if you use AI to bring in leads, are you sure that you aren’t taking an action that potentially discriminates against some clients based on their race, religion, sex or other characteristics?

Meta (formerly Facebook) already landed in trouble with the Federal Housing Administration over discrimination with its targeted advertising system that uses AI. And CFPB Director Rohit Chopra has warned that more regulation is called for with AI-based lending decisions.

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Bottom line, artificial intelligence can be and will be a timesaver for originators and many other mortgage professionals. Originators can rely on AI to create content, but the person using the content is responsible for what’s published. Be brave, but be careful. ●

The post ‘Hey, Peeps’ Isn’t the Best Conversation Starter appeared first on Scotsman Guide.

In Episode 022 of the Scotsman Guide Author Showcase, Carl White interviews Chris de la Motte of Sonar about his article, “AI Is Already Shaping the Industry’s Future,” in the November 2023 issue of Scotsman Guide Residential Edition.

Chris de la Motte is CEO and co-founder of Sonar, the groundbreaking mortgage experience platform celebrated for revolutionizing the digital mortgage landscape. Backed by FirstMark and Liberty Mutual, Sonar is an integrated loan origination, point of sale software, product pricing engine and customer relationship management platform, powering the entire mortgage journey from application to closing. Prior to Sonar, de la Motte was president of Simplist, one of the fastest- growing digital mortgage companies. Visit Sonar at yoursonar.com.

The post Author Showcase: Chris de la Motte, Sonar appeared first on Scotsman Guide.

Originally described by the mortgage lender as an “outage” in messages sent to customers, the Oct. 31 incident has yet to be termed a “cyberattack” in the company’s official verbiage, although it acknowledged in a filing with the Securities and Exchange Commission (SEC) that “an unauthorized party gained access to certain technology systems.”

The company posted a notice on its website about the incident, announcing that it immediately initiated response protocols, deploying containment measures to protect customer data and shutting down certain systems as a precautionary action. Law enforcement was notified and an investigation “with assistance from leading cybersecurity experts” was launched.

As of late this week, Mr. Cooper has announced that some of its systems have resumed normal operations. The company has pledged that it is at work keeping sensitive customer information secure. If any customers are affected, they will be notified and provided with identity protection services provided by Mr. Cooper.

Mr. Cooper is unable to process payments while its systems remain down. Per the company, electronic payments for loans will process as soon as systems return to standard operations.

“Rest assured, you will not incur any fees, penalties or negative credit reporting related to late payments as we work to fix this issue,” the notice stated.

The company also said that rate locks will be honored, and rates and fees will not be impacted by the breach. The notice reiterates that Mr. Cooper’s “Close On Time” guarantee, which promises borrowers a check equal to their first month’s principal and interest payment if their loan doesn’t close on or before the contract closing date, remains in effect and will be honored regardless of the impacts of the incident.

The investigation is ongoing, but according to the SEC filing, Mr. Cooper doesn’t believe that the breach will have a “material adverse effect” on financial results.

The post Mr. Cooper sustains ‘cybersecurity incident’; some systems now back online appeared first on Scotsman Guide.

The potential of AI knows no bounds — it spans from enriching the customer experience to amplifying operational effectiveness and output. AI’s capacity to expedite task management benefits both mortgage processors and originators by boosting daily accomplishments. The realm of AI already encompasses underwriting, automated document scrutiny, fraud detection and more. And the evolution toward digitization continuously molds the trajectory of the mortgage industry’s future.

To stay competitive, you must begin to harness AI within your mortgage enterprise. These resources are not intended to supplant a human role but rather to simplify it. Through the adoption of AI-driven technology, you can manage increased workloads without compromising on excellence or customer service. For mortgage originators, the incorporation of AI has brought about significant changes by promising improved accuracy, efficiency and client experience.

Mortgage originators play a critical role in facilitating the lending process, acting as intermediaries between borrowers and lenders. Their responsibilities range from evaluating loan applications to assessing creditworthiness and determining appropriate lending terms. The introduction of AI-powered tools into this domain has significantly impacted the way these professionals operate, enabling them to make more informed decisions and deliver higher levels of borrower satisfaction.

Risk assessment

One of the primary areas where AI has made a substantial impact in lending is risk assessment. Mortgage originators rely on assessing the creditworthiness of borrowers to determine the terms and conditions of a loan.

AI-driven algorithms have proven invaluable in analyzing vast amounts of data to predict credit risk more accurately than traditional methods. These algorithms consider a wide range of factors, including credit history, income, employment stability and even behavioral patterns, enabling originators to make more informed lending decisions.

By leveraging AI-driven risk assessment tools, originators can identify potential risks and opportunities that might have been overlooked through conventional, manual methods. Assessment tools not only enhance accuracy but also speed up the decisionmaking process, allowing for quicker responses to loan applications and reducing the time that clients spend waiting for a decision.

The integration of AI into mortgage lending has also opened up avenues for creating personalized borrower experiences. Originators can now utilize AI-powered chatbots and virtual assistants to interact with borrowers in real time, addressing queries and concerns promptly. These AI-driven communication channels are available 24/7, ensuring that borrowers receive the assistance they need whenever they require it.

AI can analyze client data to understand individual preferences and behaviors, enabling mortgage originators to tailor their services accordingly. This personalized approach not only improves homebuyer satisfaction but also fosters stronger relationships between borrowers and originators.

Data analysis

Mortgage origination involves extensive documentation and paperwork, which can be time-consuming and prone to human errors. AI-powered optical character recognition (OCR) technology has revolutionized this aspect of lending by automating the extraction and digitization of information from physical documents. This technology not only reduces the risk of manual errors but also significantly speeds up application processing times.

By automating document-related tasks, mortgage originators can redirect their focus toward value-added activities, such as building relationships with borrowers and providing strategic financial advice. The optimization of tasks through AI-driven solutions enhances overall operational efficiency and improves the originator’s ability to handle a higher volume of loan applications.

The capability of AI to manage and analyze data has demonstrated great value in predicting market trends and facilitating well-informed decisionmaking. For mortgage originators, this translates to enhanced strategic planning and the ability to align lending practices with market dynamics. AI algorithms can analyze vast datasets of historic and current market information to forecast potential economic shifts, interest rate changes and other indicators that could impact lending decisions.

With predictive analytics, originators can proactively adjust their strategies, ensuring that their lending practices remain competitive and flexible to changing market conditions. This approach positions them as trusted advisers to borrowers, offering insights that can influence the timing and terms of loan applications.

Ethical concerns

While the integration of AI offers undeniable benefits to mortgage originators, it also raises ethical concerns. The use of AI-based algorithms in lending decisions must be closely monitored to prevent bias and discrimination. It’s essential to ensure that the datasets used for training these algorithms are diverse and representative, thereby preventing the perpetuation of existing biases.

Transparency also becomes crucial for maintaining trust between originators and clients. Borrowers need — and have the right — to understand the factors that influenced the lending decision. AI systems should be designed to provide transparent explanations for the outcomes they produce, letting borrowers understand why a particular lending offer was made.

As AI technology continues to evolve, its role in mortgage lending will expand. The combination of AI with other emerging technologies, such as blockchain, could revolutionize the way mortgage contracts are created, managed and executed. Blockchain’s transparent and secure nature could enhance the integrity of mortgage transactions, reducing fraud risks and streamlining the process.

In the realm of mortgage lending, AI has proven to be a game-changing innovation for originators. From improved risk assessment and streamlined document processing to personalized customer experiences and predictive analytics, the impact of AI is undeniable. Mortgage originators are well positioned to harness the power of AI to elevate their services, optimize operations and adapt to ever-changing industry dynamics.

With the adoption of AI comes the responsibility to ensure fairness, transparency and ethical use. Originators must maintain a balance between leveraging the benefits of AI and upholding ethical standards while navigating these new tools. As technology continues to advance, mortgage originators who embrace AI with a focus on its responsible implementation will undoubtedly thrive in this new era of lending. ●

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