As borrower demands and origination costs are rising, mortgage lenders are turning to data, analytics, and artificial intelligence (AI) in order to make better and faster business decisions. The implementation of AI in mortgage industry will help mortgage lenders to use analytics for making real time operational business decisions such as identifying fraud detection, assessing borrower credit worthiness or lead nurturing for high value prospects for marketing campaigns.
Why should Mortgage lenders adopt AI?
Mortgage lenders can adopt AI to:
- Assess credit worthiness of borrowers.
- Improving employee productivity.
- Optimizing sales and marketing efforts.
- Identifying upselling opportunities.
- Detect fraud and prevent them.
- Adopt operational improvements while controlling the costs.
- Make faster and better intelligent decisions to drive opportunities.
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 nonfinancial) to predict the probability of a borrower refinancing or retiring a loan (due to a move or home sale).
- Enhancing customer experience: Mortgage Banks can provide to clients tailored loan plans based on the suggestions made by Algorithms working on behavioural 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 technology, 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.
- Streamlining 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.
Challenges in adopting analytics driven approach in mortgage industry
- Challenges in aligning data driven technology with the overall business strategy.
- Concerns about data security and data privacy.
- Data velocity, quantity, and complexity typical of unstructured data can prove to be the major technological challenge.
Looking for more information on using AI in mortgage industry or need help in implementing the right technology, leave us a message.
