Mortgage banks and lenders are underutilized the data that is available in their silos from alternate data sources like CRM data, uploaded documents, transactional and conversational logs and social media data. Moreover mortgage industry has many processes which are repetitive, predictive and stable involving homogeneous data. ML and AI can be leveraged in mortgage industry to automate these processes, handle large amount of structured and unstructured data for analytics and streamline the document management.
Impact of AI in Mortgage Industry
Improve borrower experience, satisfaction and turnaround time along with building core competencies.
Improve productivity and engage LOs through automation, RPA and analytics.
Better understanding of risk and profitability factors as well as real time information extracted from latest data leads to faster mortgage decisions and strategies.
Traditional metrics like credit score can be largely supplemented by other metrics like social media data, financial purchasing patterns, household spending, credit card usage etc. in the process of prequalification.
Statistics about various Key Performance Indicators (KPI) are derived from the large and complex set of past data by employing various AI tools.
Use Cases of AI in Mortgage Industry
Reviewing completeness of uploaded documents: Using technologies like Natural Language Processing (NLP), machines are able to read the uploaded and review for completeness, ensuring that the information entered in the application is consistent with the one available in the uploaded documents and fraud detection in general.
Automating document management: Intelligent document capture tools that utilize supervised machine learning technology can normalize, classify, separate, extract, validate and export metadata from documents.
Borrower prepay assessments: In this scenario, AI tools would examine all available information or data (again, financial and non-financial) to predict the probability of a borrower refinancing or retiring a loan (due to a move or home sale).
Enhancing borrower experience: Mortgage Banks can provide to clients tailored loan plans based on the suggestions made by Algorithms working on behavioral data of the clients.
Determining Credit worthiness: Using advanced machine learning to comb through vast sources of alternative data (other than FICO score and income) to predict an individual’s creditworthiness. Their machine learning algorithm turns all these alternate data into a credit score, which banks and other lenders can use.
Search based Analysis: Using browsing data to determine the credit worthiness of thin file borrowers” — those with no or little credit history.
Neural Networks: Using neural networks to improve the predictive modeling for risk assessment and profitability.
Chatbots and CRM Tools: Using Speech recognition technologies, Deep Learning algorithms can glean through the conversations of customers with intelligent Chatbots and CRM tools where they can spot missed business opportunities and notify Customer Service Managers.
Steamlining loan process: Using modern data science to eliminate administrative overhead and delays by complete automation of Loan process.
Sensing Business Opportunities: AI algorithms on a regular basis can review the credit scores of applicants who were turned down earlier due to low scores but have made an improvement because of change of in the status of a criterion (say employment status). Loan officers can be notified about such applicants so that they can go back to such an applicant and offer them a new opportunity to apply.
Adapting to compliance: Changes in regulatory compliance can be easily adopted into the process if the lending process is completely automated with the backing of Machine Learning technology.
Major Challenges in implementing AI in Mortgage Industry
Data velocity, quantity, and complexity typical of unstructured data can prove to be the major technological challenge.
Constant changes in regulatory compliance rules.
Managing process changes within the mortgage bank.
User adoption of AI tools within the organization especially by users who are technically not capable to process the data at the right speed or granularity can prove to be another major hurdle.
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