bankingciooutlook

Developments in Mortgage Technology to Reshape Consumer Lending

Banking CIO Outlook | Monday, September 19, 2022

In an era of digital transformation, when mortgage lenders face the common obstacle of a shortage of time and resources, it is essential to improve efficiency through data collection and analysis, process automation, and related technology systems.

FREMONT, CA: Mortgage financing has never been more accessible. Post-pandemic, customers across the financial landscape have different expectations—with low-interest rates and the greatest value of gross mortgage advances since 2007, it has become essential for lenders to provide a responsive, individualized, and automated experience to capitalize on the market.

Keeping up with the competition means adopting mortgage technology that helps lenders meet their customers' changing needs, such as artificial intelligence (AI), machine learning (ML), robotic process automation (RPA), and application programming interfaces (APIs).

Big data and analytics enhance customer perception

The World Economic Forum projects that by 2025, 463 exabytes of data will be created, equivalent to 212,765,957 DVDs per day.

Data and analytics are crucial tools for the modern financial market, and mortgage lenders who lack a defined data and analytics strategy miss opportunities to accelerate growth, control costs, and mitigate risk.

By becoming more data-centric, mortgage lenders may investigate loan applicants in a far more sophisticated manner, enabling a granular dig into consumer profiles through technologies that examine databases and publicly available information such as tweets and news articles.

By mining data sources and analyzing the results, lenders can gain a deeper understanding of these customers, their financial behavior, and their likelihood of making timely repayments. This directly impacts how mortgage lenders handle their accounts and can aid in cross-selling and mortgage fraud detection.

The more data a bank has, the more informed its decision-making can be—by evaluating new market trends and developing ways to increase profitability through data analysis. Lenders can be more proactive, efficient, and quick to move.

Advantages of AI and ML-based risk analysis

AI and ML enable massive transformations in the mortgage market. Fifty-five percent of executives surveyed by Forbes believe that AI will make their company and the mortgage lending sector more competitive. And 59 percent agree that AI's impact on the industry is now a primary consideration in strategic decision-making.

From chatbots to forecasting to credit evaluation, ML can help lenders automate human activities to improve accuracy, reduce expenses, and increase efficiency. If used for credit compliance, ML must be specially handled; nonetheless, while it appears to be a black box, incapable of explaining in human words the reasoning behind a decision, innovative solutions are overcoming this shortcoming.

The more information a mortgage lender has on a borrower, the more accurately they can evaluate their creditworthiness and the possibility of loan default. Mortgage technology may aid in the risk analysis of loan applications and identify the best method to engage a customer profile, resulting in more accurate and efficient decisions and simplifying the decision-making process.

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