Mortgage processing

What is mortgage processing?

Mortgage processing is the end-to-end processing of the home loan by lenders, banks, and financial institutions—managing and automating processes from start to end: applications, approvals, disbursement, repayment, and closing. Traditionally the process requires banks to collect applications, validate credit limit based on history, perform document verification, making underwriting decisions, and approving or rejecting the application.

The major elements that decide the qualifying of the mortgage processing are the 4C’s - capacity, capital, collateral, and credit.

Typically, the people involved in mortgage processing are buyers (the home loan applicants), mortgage processers & underwriters, lenders (banks, financial institutions, etc), and secondary market participants.

Why is mortgage processing complicated for lenders?

Banks and lenders find mortgage processing difficult for many reasons.

12. Dealing with a lot of paperwork for every application – appraisals, credit statements, forms, etc. On top of that, manual data entry and cross verification makes it even more challenging.
13. Involves multiple teams and personnels – compliance, appraisers, underwriters, processing officers, and more, where everyone must co-ordinate with each other.
14. The chances of fraud and identity verification are too high, especially with validating authentication of identity and documents submitted.
15. Usage of legacy systems, lack of automation, and minimal visibility on workflows and progress.
16. The need to improve customer experience and reduce processing time so they could thrive amid heavy competition.
17. Have to comply with lot of regulations like GDPR, HMDA, FCRA, etc.

The role of ML & AI in mortgage processing

AI and ML can make a huge difference in mortgage processing to bring speed, accuracy, and automation into the otherwise slow, mundane, and chaotic process.

Document review and data extraction: AI can be used to get information out of documents, be it PDF, documents, or physical statements and automatically update them in the system. This minimizes the manual data entry process as well as eliminate errors, cutting down data entry time up to 50 to 80%.

Risk assessment: Credit risk assessment is a major part of mortgage loan processing. AI risk models could be employed to check the likelihood of a borrower repaying the loan, checking beyond credit scores like income history, spending patterns, etc.

Fraud detection: ML models detect anomalies and strange patterns in data, which indicate fraud behavior. For example, it can alert the triage team when there is an identity mismatch, forged documents, or any other suspicious patterns.

Virtual assistants: If borrowers have any questions while applying for a loan, chatbots and virtual assistants can be available for them 24/7, helping them find answers. No need to set up a whole team.

Automated underwriting: Data-driven decision making can help underwriters make the right decisions, finding out red-flags and deal breakers faster.

Compliance monitoring: Financial institutions need to ensure that they follow compliance and lending rules. Analytics and AI based compliance checks is the way to monitor and alert compliance failures.

Predictive analytics: Predictive analytics is a resourceful solution to identify loan defaulters automatically, so that banks could take proactive steps and do smart portfolio management.

Benefits of AI in mortgage processing

Faster approvals: AI could lead to fast loan approvals up to 50 to 70%, while reducing the possibility of errors.

Cost-efficient operations: Utilizing AI for mortgage processing can lead to cost-efficient processes, allowing banks to expedite the processing time, minimizing risky investments, and preventing cost wastage due to fraud.

Scalable: AI and automation make it easier to handle large volumes of applications without having to hire too many resources.

Digital-first: Modern consumers want more convenient, fast, and digital-first operations for mortgage borrowers.

Better risk assessment: AI could trace more borrower information looking for clues about creditworthiness and chances of risk, making it more efficient than traditional risk assessment.

Less burden on customer service agents: Setting up 24/7 knowledge center and AI-powered virtual assistants reduce the burden on customer servicing teams.

Audit readiness: AI can ensure that all regulatory frameworks are met while processing a mortgage, saving millions of dollars wasted on penalties.

AI-based mortgage processing – who is it for?

• Modern, futuristic mortgage processing solutions are beneficial for many, including the borrowers and consumers.
• Lenders, banks, and finance institutions
• Secondary market participants and investors
• Loan servicers and mortgage brokers
• Loan officers and mortgage officials
• Underwriters, appraisers, and other decision makers involved in mortgage processing.