The digital transformation of the financial services industry is one of the biggest things happening in the banking industry. The way people access and receive credit is quickly changing, and this change will affect all credit-focused lenders, including mortgage lenders.
Mortgage rates declined in 2019 and plummeted in 2020. In December 2020, the Federal Home Loan Mortgage Corporation (FHLMC, or “Freddie Mac”) reported that the average rate for a 30-year fixed-rate mortgage was 2.68%, but changes in the economy and the need for stabilization pushed the rates up. While this is good for lenders, it's causing problems for borrowers. Credit bureaus are seeing record numbers of requests for data from mortgage lenders. Using credit reports to evaluate loans is slow and inefficient, though, which results in challenges when processing the volume of submissions. To keep things moving, borrowers are sometimes getting loans they may not otherwise have received due to low credit scores or other issues.
Financial lenders are under constant pressure to grow loan volume while reducing bad debt expenses in order to improve net income. Accurate credit modeling is critical to making this possible, but traditional approaches are limited in their ability to handle changing customer circumstances. This can lead to suboptimal results. Even a modest improvement in credit modeling accuracy can have a significant effect on loan volume, bad debt expenses, and net income.
Data processing time is a critical factor for credit models in this fast-moving market. Determining whether a borrower should be approved for a loan requires the evaluation of several borrower traits that change as the borrower’s life progresses. Ideally, credit assessment should be possible at any point in time with thorough accuracy, but current modeling techniques struggle to keep pace with the dynamic nature of borrowers’ behaviors. This, combined with the volume of submissions from mortgage lenders, is overwhelming credit bureaus. They need a better solution.
Katana’s Graph Intelligence Platform boasts exceptional data ingestion and processing rates in addition to AI-enhanced analytics, mining, and querying to bring financial services a comprehensive solution for problems beyond just credit modeling. Fintech and digital transformation are quickly changing the banking landscape and, considering the tremendous amounts of banking data, efficiently leveraging data in a digital environment is critical to success. For more information about how Katana Graph is serving the financial industry, check out our Credit Modeling datasheet.
Whether structured or unstructured data, Katana Graph can greatly improve the insights and opportunities uncovered from your organization’s data. Click to schedule a meeting.