Every year, a small but significant portion of mortgage applications in the US is fraudulent in nature, costing mortgage businesses anywhere between $3 and $5 for every dollar of fraud loss. In 2021, research indicates that 0.83% – i.e., 1 in every 120 applications – contain fraud. Worryingly, this was a 37.25 year-over-year increase and much higher than pre-pandemic levels. As refinancing opportunities increase and home buying demand resurges, mortgage fraud will be a key challenge in 2022.
Fortunately, advanced technologies like artificial intelligence (AI), machine learning (ML), mortgage RPA, and automation can address this issue. Automation can help detect and prevent mortgage fraud without straining already overworked human executives, which can improve your bottom line. Over time, automation and analytics models that are fed with high-quality data will be able to detect mortgage fraud in real-time and curb it at the origination phase itself.
How Can Mortgage Automation Help Detect and Prevent Fraud?
There are six ways in which automation technology aids in mortgage fraud mitigation:
1. AI can automatically identify trends
A common challenge in mortgage fraud detection is that the data exists in disparate documents with different formats. AI can filter information from various documents (including unstructured ones), extract recurring data sets, and map trends. Automated trend mapping is the foundation for smarter mortgage fraud detection, as it provides a baseline for comparing transactions.
2. RPA bots can rule out false positives
False positives make it difficult to efficiently handle mortgage fraud since human executives have too many red flags to investigate. Robotic process automation (RPA) bots can follow up on seemingly suspicious transactions to rule out false positives. For example, if a loan applicant regularly makes transactions during odd hours, the bot can investigate each transaction event without adding to your human executives’ existing workloads.
3. Automated document reviews can detect tampering
A common example of mortgage fraud is when applicants tamper with eligibility documents – a major risk for business and commercial borrowers. An automated solution can check and double-check data entry fields to identify if there is an instance of tampering. It would be able to flag potentially false identities, find inconsistencies, etc. at a pace that human loan executives cannot match. DocuChief by Nexval is an example of a platform that can automate document reviews almost entirely.
4. Automation can cover a far wider breadth of data sets
The underlying principle behind mortgage fraud detection is to highlight and analyze borrower data against independently verifiable records. Without automation, there is an inevitable cap on how many data sources a human executive can analyze in a day. That’s why mortgage automation is so important – it can verify property data, employer information, third-party watchlists, borrower self-assessment, business health information, and much more in a fraction of the time.
5. Machine learning (ML) can enable automated fraud detection in real-time
The faster a lender detects fraud, the lower will be the losses incurred per instance of fraud detection. Currently, every $1 of fraud loss costs you $4 in additional expenditure, up from $3.25 in 2019 and $3.64 in 2020. Using machine learning, it is possible to create data analysis and classification models that can prioritize and detect loan fraud scenarios. Custom ML models can have automated data pipelines that become more accurate over time, eventually detecting fraud even as it occurs.
6. Automation can help predict loan defaulters
Fraud detection and prevention is not only necessary in the origination stage – it also has a role to play after a mortgage is disbursed, since fraudulent borrowers may default on loans. A predictive model can automatically identify fraudulent patterns weeks or even months before the first default payment takes place. This provides mortgage businesses with a competitive edge.
Why is Mortgage Automation for Detection/Prevention of Fraud Important?
As per industry statistics, loan fraud is rampant across sectors and the problem will intensify in a volatile economy. In 2020, US auto lenders were exposed to $7.3 billion in fraud, while mortgage businesses faced a staggering $20 billion in lending fraud exposure. With concerns around a looming recession in the US in 2022-2023, the risk of fraudulent borrowers will go up.
At the same time, legitimate borrowers and home buyers are looking to economically rebound after a challenging period during the pandemic. Outdated fraud detection systems that rely on human effort rather than mortgage automation will bring down the customer experience for this group, cause delays, and topple the workload balance for loan executives.
Explore Mortgage Automation with our Tech Gurus at Nexval
In 2022, mortgage automation adoption does not have to be complex or costly. Cloud-based solutions allow lenders to deploy low-hardware footprint solutions. The right automation partner will customize RPA bots, AI/ML algorithms, and automated workflows to suit your requirements. At Nexval, we bring over two decades of industry experience combined with specialized expertise in quality, risk, and compliance management. These are in sync with the recent and upcoming regulations in the US mortgage sector, backed by our team of 1000+ technology SMEs.
To stay ahead of fraud risk in 2022, speak with our Tech Gurus today.