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Buying a home should be an exciting milestone, not a chaotic journey into the unknown. However, many buyers find the home buying and loan processing experiences intimidating. Complex loan application processes, high volumes of paperwork, a long loan approval waiting period, and closing delays are some of the leading causes of buyer dissatisfaction.
According to a 2022 Finicity report, 89% of respondents in a survey of 1095 participants believed that loan applications were stressful, and 72% were surprised at the volume of paper processes still occurring in the various mortgage stages.
This report is a lesson for the real estate industry to deliver quick redress and ensure a shorter closing time. Lending and real estate companies must adopt intelligent process automation tools (IPA) to keep up with market trends. While robotic process automation can speed up documentation in the home buying process, machine learning models are far more capable of streamlining workflows, detecting anomalies, and deriving hidden insights from historical data.
Read further to understand the critical role that ML algorithms play in mortgage automation.
Intelligent process automation (IPA) is a set of technologies to automate and streamline digital processes. The technologies include robotic process automation (RPA), computer vision, and digital process automation (DPA), which incorporate artificial intelligence (AI) and machine learning (ML) to perform intelligent analysis.
The purpose of IPA is not only to minimize repetitive and manual rule-based repetitive tasks but also to provide human-like precision and intelligence to robotic automation. It goes beyond predefined rules and workflows while managing the data flow and derives insights from hidden patterns in structured and unstructured documents.
The real estate industry that deals with complex listings, agreements, property valuations, appraisals, mortgage processing, and related documentation tasks can greatly benefit from the assistance of IPA tools. These tools can manage large datasets from complex documents such as mortgage applications, income proof, credit reports, lease terms, agreements, and property documents, to name a few.
While traditional RPA tools automate repetitive, high-volume tasks, intelligent process automation tools enhance decision-making by simulating human behavior and judgment.
Here is a detailed overview of mortgage and real estate processes involving lenders, buyers, lawyers, appraisers, and real estate agents.
The buyer chooses a property based on online listings, newspaper articles, and references from real estate agents.
After negotiations and offering a price to the seller, the buyer contacts a lending company, and the loan officer reviews their credit history, bank statement, income, debt, etc., to recommend a suitable loan program.
The process where the borrower applies with personal and financial details and information of the property concerned.
A loan officer reviews the borrower’s information across bank statements, pay stubs, credit reports, etc. to assess their assets, liabilities, and creditworthiness.
Performed by a legal company to confirm the rightful owner while ensuring there are no legal disputes
A licensed appraiser performs a valuation of the property to determine if its true value matches the loan amount requested
The required paperwork and documentation go to the underwriter, who makes a final decision by evaluating the borrower's application and documents.
After conditional approval, the loan processor collects any outstanding documentation, coordinates with third parties (such as insurance companies or appraisers), and prepares the loan file for final review.
Once all the conditions, including closing disclosure and loan estimates, are met, the lender issues a clear to close.
After contingencies are met, the transaction proceeds to the closing stage. At closing, the buyer and seller, along with their respective real estate agents and any necessary representatives, gather to finalize the sale.
Document processing challenges can result in prolonged timelines, increased stress, potential financial consequences, and additional administrative burdens for buyers, lenders, and real estate agents.
Let’s look at some of the biggest challenges.
Real estate and mortgage transactions involve paperwork such as deeds, contracts, disclosures, credit reports, and bank statements. Gathering, managing, and verifying an extensive range of documents requires hyper-focused vigilance, expertise, and support from resources. Such efforts usually take attention away from more strategic decision-making that can help the company grow and reap profits.
Document processing challenges can lead to unforeseen delays that affect loan closure and transaction timelines. Repetitive tasks take significant time and effort, including data extraction from listings, property documents, bank statements, pay stubs, credit history, valuation, and tax reports.
If not handled systematically, prolonged documentation leads to closing delays, thereby frustrating both buyers and sellers awaiting the agreed-upon closing date stated in their contract. Buyers often pay higher interest and lose their mortgage affordability if there is a delay due to errors, inaccuracies, or missing documents. This impairs the customer experience, and real estate agents usually find themselves on the receiving end of buyer/seller mistrust.
Besides quick turnaround, buyers expect compliance with regulatory and compliance frameworks. Mortgage processors and real estate agents face challenges navigating the complex web of industry regulations and federal laws. It gets further completed as they need to maintain speed, precision, and accuracy in the documentation.
Data blunders cost billions, and there are enough instances to prove it. One of the most prominent examples is the 2008 US housing bubble. While several factors culminated in the disaster, there’s no denying the aspect of poor risk assessment, which relies heavily on error-free documentation.
Due to inefficient document processing, lenders often lose sight of errors and inaccuracies that could indicate possible fraudulent activity. Buyers can suffer substantial financial losses or become victims of identity theft, ruining their relationships with lenders and real estate enterprises.
The various data points emerging from loan processors, underwriters, borrowers, appraisers, real estate agents, and legal advisors require streamlined collaboration. The same becomes challenging to maintain due to communication delays or a lack of standardization for seamless data transfer.
Apart from automating routine business processes and freeing up time and resources, IPA uses its cognitive capabilities to derive more strategic value from your processes and workflows.
Here is a detailed breakdown of its benefits.
IPA fast-tracks the mortgage processing lifecycle by reducing manual effort and increasing efficiency in documentation and verification. It automates and streamlines data extraction, validation, reviews, and underwriting tasks by identifying relevant information, even from unstructured documents like loan applications, lease terms, agreements, invoices, etc.
Automated document analysis also saves considerable time and resources by flagging inconsistencies to prevent expensive errors. With fraud and anomaly detection algorithms, IPAs can identify deviations and suspicious patterns, reducing financial risks for all parties involved.
According to Forbes, a leading institution continues to save 11 million USD every year on credit, verifications, and underwriting using an AI-based tool.
By automating documentation and review, intelligent process automation tools reduce the reliance on manual labor and operational costs. It frees up real estate professionals, loan processors, and underwriters to focus on more strategic tasks such as quality control, cross-selling, and shaping personalized buying experiences.
With well-trained AI-powered systems, you can keep track of sentiment analysis across digital and social shopping platforms to identify customer needs. Through real-time visibility, lenders can enhance the customer experience and foster loyalty by offering personalized account servicing. At the same time, agents can stay updated with market, inventory, and customer trends.
IPA also increases customer satisfaction through faster response rates, quicker information retrieval, and reduced processing time.
In 2021, JP Morgan was penalized $125 million by the SEC for insufficient record-keeping and communication in business transactions. This case highlights the costs of poor compliance. To address such challenges, implementing machine learning-based automation tools can be valuable. It creates a comprehensive audit trail, accurately classifies and stores information, and mitigates risks associated with inadequate record-keeping.
You can streamline the document processing workflow by automating the classification and extraction of information from related documents. IPA leverages technologies such as OCR to convert scanned texts and images into machine-readable formats. It then analyzes the data to extract relevant fields such as name, address, loan amount, interest rates, etc.
It later classifies the documents based on their types and purposes. By training machine learning models on a labeled document dataset, IPA can learn to recognize patterns and automatically assign the appropriate category or label to incoming documents. For example, IPA can classify documents as loan applications, income statements, property appraisals, legal contracts, or tax documents.
By leveraging the power of NLP, you can use the tool to automate communication with borrowers, providing timely updates on loan status, document requirements, and closing information. The AI-powered chatbots respond quickly and efficiently to customer queries and provide personalized recommendations.
Moreover, machine learning algorithms enable the chatbot to analyze user feedback, identify areas for improvement, and adapt to changing borrower requirements.
IPA automates underwriting by analyzing credit reports, income data, and property information. It uses predefined rules and algorithms to assess credit risk, property valuation, and loan-to-value ratios, helping underwriters make faster and more accurate decisions.
It can also perform quality control checks on appraisal reports to detect anomalies and potential errors and ensure their accuracy and reliability.
IPA leverages historical data and machine learning algorithms to generate insights on market trends, customer behavior, and portfolio performance. It enables organizations to make data-driven decisions, identify areas for process improvement, and provide customized recommendations to clients. Investors can gain valuable insights, make data-driven decisions, and enhance their real estate portfolios' overall performance and profitability.
The technology reduces lenders' post-closing review and compliance costs by providing real-time reporting, automating data extraction, flagging potential issues, and streamlining compliance processes. These automated solutions improve efficiency, accelerate the review process, and facilitate prompt corrective actions.
One of the first steps during implementation is understanding the faults in your current document processing system and the larger business goal. Understand how the new rollout will fit into the organization’s larger scheme of priorities.
If you are a real estate company, the most significant challenges could be gathering property information, verifying its details, and creating error-free listings.
The real estate industry has a vast nexus of stakeholders that comprises buyers, sellers, agents, underwriters, and appraisers, to name a few. You only have to imagine the significant data points that crop up with every interaction among these players. And thus, high-quality data that does not carry errors, inconsistencies, or biases is critical to ensuring a smooth closing.
Prioritize data validation, cleansing, and preprocessing before training your ML algorithms.
The real estate industry has several regulatory frameworks that govern buying, selling, and lending activities. Some examples include the Equal Credit Opportunity Act (ECOA), the Fair Credit Reporting Act (FCRA), and the Fair Housing Act (FHA).
Failure to comply with these regulations often invites huge penalties, adding up to millions. Hence, engaging regulatory experts, establishing audit trails, implementing internal governance, and conducting continuous monitoring are some preventive measures to safeguard compliance.
The process also involves protecting your customers’ data through privacy-preserving machine learning techniques and spreading organizational awareness concerning the best data safety practices.
Fine-tune your implementation and avoid model degradation by constantly monitoring and updating your data. Important KPIs include straight-through-processing rates (STP) rate, accuracy rate, processing speed, cost savings, etc. These indicators measure the impact, efficiency, and ROI of your implementation.
IPA also relies on analytics to identify hidden trends and patterns in the data. You can further leverage these insights to detect more processing bottlenecks in the system
Begin with small-scale pilot projects. By starting small, organizations can test and validate the IPA solution, learn iteratively, refine the system, and demonstrate its value before scaling up. This approach allows for gradual adoption and minimizes organizational resistance.
Additionally, it is essential to communicate the plan and benefits of the new system to stakeholders. Organizations can encourage adoption and ownership by involving critical individuals in the workflow design process and demonstrating the practical benefits of the IPA solution.
By leveraging intelligent process automation, lending and real estate enterprises can transform their operations to attain maximum efficiency. It empowers agents, loan processors, and underwriters to become more strategic in their decision-making.
Similarly, buyers experience faster and smoother applications with reduced waiting periods. Their increased satisfaction drives retention, business growth, and profitability.
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