Data Science is disrupting Mortgage Industry in an unprecedented manner and Mortgage banks are investing heavily to build infrastructure and technology to gather data, store them and use the data to extract important insights. The result is a win-win situation for both lenders as well as borrowers. Here are some ways in which Data Science is changing Mortgage Industry by aiming to reduce costs, reduce risk and increase profitability.
Best Fit Loan products:
Mortgage banks can now model loan performance based on different set of data including profitability, risk factor, default rate, demographic data, user statistics etc.
Loan products are modelled based on the data on past loan performances on a collective basis.
Pricing and mitigation of individual loan products are designed by taking into account demographic factors, current economic environment, individual user data.
Mortgage banks can increase the profitability and reduce the risk factor since these data models can accurately predict the loan performance depending on the data available.
Borrowers are also equally benefitted since the individual loan offerings will be tailor made based on the data on user statistics like income statements, W2 returns, age, demographic data, employee information etc.
Faster Decision making:
Upheavals in data storage and data computational technology provide Mortgage banks options for larger data storage and faster computations.
More data on users translates to greater assessment of risk and profitability factors.
Faster computational capabilities empower Mortgage Banks with real time data processing as new information flows in.
Better understanding of risk and profitability factors as well as real time information extracted from latest data leads to faster mortgage decisions and strategies.
Prequalification:
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.
Therefore Mortgage banks can look beyond traditional metrics to explore new business opportunities while minimizing risk.
Based on the new insights loans can be offered to segment of population who are broadly eligible but are denied loans when evaluated based on classic traditional metrics.
Pattern recognition:
Data visualization tools and Business Intelligence tools can derive emerging trends and patterns by employing user data and transaction records.
These patterns recognition helps Mortgage banks in fraud detection, tracking user growth, evaluating profitability and risk metrics.
Pattern recognition based on user’s age, demographic data and timelines helps Mortgage banks to change and implement new strategies based on changing conditions.
Predictive Analytics:
Statistics about various Key Performance Indicators (KPI) are derived from the large and complex set of past data by employing various data analytic tools by Mortgage banks.
A new loan request from a borrower is evaluated based on the predictions made by these analytics.
These analytics mainly pertains to default risk and profitability metrics.
For further information on the scope of Data Science in Mortgage Banking feel free to contact us at 1800.846.1619 or write to us at info@takefiveconsulting.com.
