Problems in the validation of mortgage origination documentsShare
The major problems that are still burdening lenders and underwriters are;
Using modern technologies like artificial intelligence will help eradicate such problems with a fool-proof methodology.
The 3C check process refers to determining the overall completeness, correctness, and consistency of the documents submitted. This enables the processing of loan applications in real-time, which means faster loan sanctions.
Completeness refers to validating a document, like a bank statement, to see if all the information fields required are complete. Post automated classification, the extracted data is checked to ensure the presence of the required data on documents.
To address this problem, Digilytics RevEl uses one-shot learning.
The mortgage industry is facing five (5) key challenges that are setting pressure towards a paradigm shift.
There is no easy way for consumers to identify, at an early stage, those products for which they qualify”, which impedes both the consumer’s ability to find a suitable deal and intermediaries’ ability to find them the best deal promptly.
This lack of transparency in lending criteria is one of the reasons why about 30% of consumers missed out on being able to use lower-priced mortgage products for which they would be eligible. The most obvious way to make more information available is using tools intermediaries could use to identify, early on, those products for which the consumer is or is not likely to be eligible.
This enables faster recognition of how complete the documents are.
Correctness refers to the accuracy of the data in the documents. Post automated completeness check, the system can verify the correctness of the extracted data. This requires a thorough evaluation of the data captured, which means there cannot be any scope for errors in reading the data. E.g.: Date check on Payslip etc. For this, Digilytics RevEl uses intelligent data capture.
Intelligent Data capture:
Consistency refers to checking for consistency in the bank statement information fields. Checks for consistency of information across documents. eg: Name check across bank statements and application form. Rules can be configured and can be easily updated owing to regulation changes etc. Documents are validated against lending principles, lending criteria and regulatory requirements, acting as an assistant for underwriter.
Once a bank statement is recognized during the extraction phase, it is then cross-referenced with any other valid document, to check for inconsistencies across various information fields. A post-extraction rules engine is configured, to find out the inconsistencies in the document.
E.g., Let’s say a borrower has given a bank statement as address proof. Document-level cross-referencing can compare the bank statement with any document, like a driver’s license, and validate the borrower's address.
This helps raise red flags if there are critical discrepancies in the bank statement.
Digilytics™ RevEl for Financial Services is an AI-enabled revenue elevation product built on the Digilytics™ platform. It is available as a SaaS-based solution with an artificial intelligence dashboard and can be plugged into any loan origination system (LOS). For more information about Digilytics RevEl, visit our dedicated website here.While you are here, other top articles you might be interested in 1. Tech Enablers in the Mortgage Industry 2. The Future of Computer Vision, Machine Learning and Artificial Intelligence in Mortgage Industry 3. How industry 4.0 principles can work in the favor of mortgage origination? 4. Top 5 Real Challenges in Building Predictive Models in Mortgages 5. 5 Ways in which Mortgage Lenders can Leverage Digital Lending for Good 6. The Power of Intelligent Digital in the Mortgage Industry by Digilytics AI 7. Whitepaper on Data Lakes: A new approach to managing data 8. Applying industry 4.0 principles to mortgages 9. Intelligent Digital Income and Expense Verification by Digilytics AI 10. Digilytics AI is one of the top 30 ML-powered start-ups 11. Top Challenges in Implementing a Learning Interface 12. First time right applications for mortgage lenders – Expectations vs Reality 13. Key Takeaways from the Webinar on Intelligent Affordability Services 14. Making First Time Right Applications a Reality in Mortgage Lending 15. AI and the Transformation of the Mortgage Industry