Maintaining profitability in the loan origination process is currently among the top priorities for mortgage providers. Yet, loan origination profitability has fallen substantially in 2022, according to Fannie Mae, despite cost-cutting measures. In this climate, it is essential to use technology to analyze loan performance so that you can maintain your margins and unlock new efficiencies. Fortunately, there are several useful tools that can help you do just that.
Why is Loan Performance Analysis a Prime Candidate for Digitization?
Analyzing loan performance in mortgage has traditionally been a data-intensive process. Lenders must consider information dating back 10-30 years to build statistical models that can predict risk and anticipate the performance of a loan with a degree of accuracy. This includes financial analysis tools like common size statements (which regularize income data), ratio analysis, and benchmarking.
However, most of these activities – whether in the loan origination process, during servicing, or for default loans – are stuck in legacy gear. Outdated tools like spreadsheets and non-user-friendly database management systems (DBMS) like MS Access are still widely used to make sense of loan performance metrics.
This approach has three drawbacks:
- Spreadsheets and other manual tools are time-consuming. Data-literate mortgage experts must spend enormous time and effort filling out forms, creating and running analysis models, and generating reports.
- Legacy tools are not meant for integration. They can either fetch data from one source, or you need to set up each and every integration from scratch. This makes it difficult to scale or upgrade the systems used in the loan origination process (i.e., the data source).
- You lose out on new opportunities since you are unable to utilize artificial intelligence (AI). Legacy tools are not AI-ready. Yet, AI engines can ingest massive volumes of data, run complex algorithms at lightning speed, and factor in many more variables when analyzing loan performance.
In contrast, cutting-edge digital systems are designed for a data-driven world. They can significantly increase the scope and depth of analysis of loan performance metrics while requiring less work from human executives.
6 Ways Technology Can Help Analyze Loan Performance
If you’re looking to modernize your loan performance analysis systems, here are the top tech tools to include:
1. Tools like Power BI to visualize loan performance data
Typically, mortgage data resides in table-like repositories with data fields, rows, columns, and other organizing tools. It is impossible to quickly make sense of data in this format unless you spend hours or even days combing through it all. In contrast, Power BI can automatically create charts, graphs, and other visualizations that provide a high-level understanding of data with the option to dive deeper.
2. Dashboards and data exploration tools to focus on the right loan performance metrics
Tools like Power BI and Tableau offer another key advantage when predicting performance at the loan origination process with dynamic dashboards. You can have several dashboards for your key loan performance metrics like average origination value, pull-through rate, decision-to-close time, abandoned loan rate, etc., broken down as per region and customer type.
3. Application programming interfaces (APIs) to cut across data silos
Another way technology helps analyze loan performance is through integration. APIs are bits of code that act as connectors between two different systems, opening up a channel of data flow. This means that you can break down data silos and analyze a variety of systems, factors, and variables together – from social media to income statements, and historical records.
4. Data lakes that can incorporate big data sources
Manual and legacy loan performance analysis usually leave out big data, which is the vast ocean of unstructured information that exists all around us. Fortunately, data lakes can accommodate big data from a variety of helpful sources such as social media, photographs and screenshots, PDF files, user behavior, etc. This makes the analysis more accurate.
5. AI for high-volume processing and natural language insight generation
Artificial intelligence-based data models are capable of running highly complex calculations very fast. Consider a linear programming approach that can evaluate various resource constraints and motivating factors to mathematically provide you with an optimized situation. Using AI, you can not only run such problem-solving algorithms but can also get insights in a natural language so that it is easily actionable.
6. The cloud to minimize infrastructure costs and drive agility
Finally, the cloud is an essential pillar for modernized loan performance analysis. On-premise infra (servers, mainframes, cooling systems) are costly to maintain, especially when they run resource-intensive data operations. In addition to hosting systems on the cloud, you can also take advantage of Software-as-a-Service (SaaS) loan performance tools and dashboards.
Getting Started with Technology-Empowered Loan Performance Analysis
Analyzing loan performance is a core mortgage activity, and now, you have all the tools to optimize it. It is a good idea to start with the loan origination process, where you can predict the performance of loan applicants and reduce future risk. The tools we discussed are also useful across the servicing lifecycle – for example, when you need to mitigate the risk involved in default servicing.
Importantly, the digital transformation of data analysis in mortgage does not require you to “rip and replace” existing systems. A mortgage technology partner like Nexval can bring specialized domain experience, combined with technical expertise in AI, DevOps, automation, and the cloud, to build and integrate the most optimal analysis engine for your needs.
As borrowers and third-party systems generate huge volumes of data, there is an opportunity to refine loan performance metrics and obtain a clear picture. Speak with our Tech Gurus to learn how.