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How Can Lenders Boost Accuracy of Mortgage Loan Requests in 4 Steps?

Do you struggle with slow mortgage origination, manual data entry and incomplete loan request submissions? You are not alone.
Natalia Slota
June 24, 2024

Despite the ongoing digitisation of financial services, mortgage origination remains manual and paper-based. This leads to significant inefficiencies and errors which in turn slows down the overall origination process.

Low first-time-right (FTR) rates in the industry indicate that first submissions often miss essential data or contain mistakes, leading to time-consuming rework.  For example, according to our analysis in 2023, it took on average 26 days for a loan advisor to complete a loan request (lead time) with an average first-time-right of 25%. One of the main reasons is manual data entry errors resulting in low-quality loan requests. This problem is compounded by inefficient communication between loan advisors, analysts and underwriters to correct mistakes, leading to further delays.

Good mortgage technology can boost the FTR rate, ensuring that loan requests are submitted correctly from the get-go, without missing data or errors. Navigating users with good UX, utilising 3rd party integrations as well as incorporating AI in the process can help lenders to boost loan request quality. Here’s how financial institutions can do it in 4 actionable steps:

Step 1: Ensure Loan Advisors Know Data Requirements

Loan advisors should be fully aware of the data and document requirements necessary to apply for a mortgage product. Building a clear workflow for this process ensures that advisors understand what is needed right from the start. Additionally, by optimizing the navigation and design of the loan request module, advisors and analysts can be guided through the necessary steps, ensuring they know exactly what to do next. This reduces mistakes and the need for repeated corrections, saving time and improving the accuracy of submissions.

Step 2: Support Advisors with Third-Party Verification and AI-Powered Data Extraction

Why request someone’s identity details when you can verify their identity through accessible data sources? With the increasing availability of data, integrating third-party verification providers can significantly enhance your loan process. Key data types that can be verified through APIs include identity verification, risk assessment, collateral valuation, tax information, income and liability profiles, and KYC compliance.

For example, integrating providers such as itsme for borrower verification in Belgium, allows for quick and accurate checks. Key loan data can also be sourced from risk data providers such as Schufa in Germany and collateral data from PriceHubble or Rockestate. Additionally, banks’ databases and KYC providers like Onfido can also be used for improved accuracy, benefiting both lenders and borrowers.

However, not all loan request data can be retrieved through third-party data sources. By utilizing Generative AI (genAI), lenders can automatically extract all relevant data points directly from uploaded documents. Oper’s genAI models verify the information and flag any discrepancies, allowing advisors to update the loan request with just one click. This not only accelerates the workflow but also minimizes manual effort and errors, ensuring a more efficient loan request process.

Step 3: Implement Dynamic Checks

Dynamic fulfilment ensures that loan advisors see only the relevant fields based on the selected loan products, reducing the likelihood of missing or incorrect data. Additionally, using technology to perform dynamic checks on the entered data ensures it meets the necessary quality standards. A dynamic fulfilment engine guides the loan advisor by matching the data with the loan application requirements, significantly increasing the quality and accuracy of the submission.

Step 4: Continuous Improvement Through Data Analysis

Continuously monitoring FTR rates and optimizing data collection and extraction processes, lenders can help identify recurring issues and areas for further optimization. By using expert mortgage technology, analysing data and speaking with borrowers, lenders can steadily improve their first-time-right rate, leading to much more efficient processes.

Quantifiable Goals

Improving the first-time-right rate of mortgage loan requests is a tangible way for lenders to boost productivity. By optimizing the loan request process, implementing dynamic fulfilment, and continuously refining processes, lenders can achieve significant time gains and more accurate outcomes. These steps not only enhance the accuracy and speed of loan processing but also contribute to a more streamlined and effective lending operation.

At Oper, we tackle the FTR challenges head-on by developing user-friendly borrower and advisory platforms, utilizing 3rd party integrations and incorporating genAI features. Our key goal is to provide fast and reliable fulfilment by increasing FTR scores from 25% to 90% and decreasing the time to fulfil a loan request significantly. This means that almost all loan requests submitted for the decision will be correct after the initial submission, making fulfilment almost 60% faster on average.

Do you want to learn more about how Oper can help you boost efficiency and reduce operational costs? Watch the video:

Ready to take the next step and implement Oper in our lending business? Book a demo with our expert today:


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