As data proliferates, mortgage companies have the opportunity to convert raw data into actionable insights that improve their processes. Traditionally, this task was performed by data analysts or business intelligence analysts. Using spreadsheets or dashboards, these highly data-literate professionals would respond to market movements or regulatory events by analyzing data.
AI-driven analytics has the potential to change all of this. It applies machine-driven techniques to automatically analyze data as it is generated and proactively respond to events. It knows which data to look at, the end goal, and how to get there.
AI-driven analytics can be a game-changer for mortgage processes like loan origination when applied correctly. However, the technology is still in its early days, and organizations need a robust data foundation to run analytics efficiently.
What Is the Role of AI-Driven Analytics in Mortgage?
To answer this question, let us consider the meaning of AI-driven analytics, also known as augmented analytics, in certain fields.
AI-driven analytics refers to a branch of data science where an AI engine works like a human data analyst to proactively investigate the data for business solutions. It is an improvement upon augmented analytics, where cognitive technologies are used to improve the data analysis tools but not replace the data analyst’s manual efforts.
As we mentioned, AI-driven analytics is still an emerging technology, unlike more mature solutions like workflow automation that mortgage companies are already familiar with.
But the purpose is the same: automate key processes so there are fewer errors and human executives are free to focus on other tasks.
Consider the underwriting and loan origination process, for example. After every quarter or fiscal year, human analysts need to pour over historical data records and market research to adjust risk thresholds. AI-driven analytics can do this automatically by analyzing data at pre-programmed intervals to achieve the desired outcomes.
Through machine learning (ML), AI tools could even learn when to apply analytics and make recommendations. This reduces the onus on mortgage teams to constantly watch out for market shifts and movements so they can make a timely response.
3 Ways AI-driven Analytics Can Improve Mortgage Processes
Once sufficiently mature, AI-driven analytics can be beneficial in the following use cases:
1. Loan origination
Loan origination is one of the most data-heavy processes in mortgage for several reasons. The first and most obvious one is analyzing borrower risk. Hundreds of variables make up the risk profile of a mid-sized to large borrower, and human analysts usually only scratch the surface. AI-driven analytics, on the other hand, is both scalable and dynamic. It can handle large volumes and also automatically update risk models when new data comes in.
Matching leads with the right loan officers is another data-driven use case in loan origination. In a volatile economy, lenders need to maximize every lead they can capture. AI-driven analytics can match prospects with the loan origination officer most suitable for them.
Finally, artificial intelligence can also help make better ecosystem decisions in loan origination. Using AI, companies can predict potential loan origination volumes and demand patterns to understand the type of network they may need in a specific region — brokers, realtors, etc.
2. Workflow automation
Most workflow automation in mortgage is rigid and based on pre-programmed bots. With AI-driven analytics, that would no longer be the case, since artificial intelligence is capable of making autonomous decisions. This allows it to handle exceptions in automated workflows and not reach out to a human executive whenever something goes wrong.
AI-driven analytics can transform workflow automation in areas such as default servicing. These are complex processes to navigate, involving large amounts of data and regulatory challenges.
AI can predict servicing requirements even before they occur and help mortgage companies stay one step ahead. During difficult situations like foreclosures, AI-driven analytics can recommend the best course of action and execute it through workflow automation.
3. Fraud detection and preventing false positives
Another application of AI-driven analytics is in fraud detection processes. Mortgage companies have typically relied on isolated rules and signals to detect fraud without a lot of context. This can cause loopholes that bad actors are bound to exploit. It can also lead to false positives that end up inconveniencing legitimate borrowers.
To take a simple example, traditional analytics may associate certain times of day with fraudulent transactions. If a borrower works the night shift banks during those hours, there is a possibility that they will be flagged for fraud.
AI-driven analytics would be capable of assimilating massive datasets that factor in the broad context of a transaction and not just one signal. Since it acts automatically, you do not have to wait for scheduled scans or manual detection. Artificial intelligence can pick up on small but significant anomalous signs and closely monitor the situation for fraud while weeding out false positives.
Preparing for the AI Revolution
Artificial intelligence is transforming nearly every mortgage process, from loan origination up until the very end of the value chain. Advances like generative AI, AI-driven analytics, and intelligent workflow automation will soon become the norm. This will reduce human efforts for repetitive or data-intensive tasks while making more room for strategic decisions and growth.
At Nexval, our team of 1000+ subject matter experts is constantly pushing the boundaries in AI innovation. Our bespoke workflow automation solutions can reduce your efforts by several thousands of hours while improving the quality of mortgage processes.
Talk to our mortgage tech experts to know more.