Machine learning and AI have finally started to come into their own in the last few years, bringing disruption to one industry after another.
The world of finance has been no exception. In particular, the global mortgage industry, which is process-heavy and data-reliant, has started to see the effects of a whole new generation of machine learning tools and platforms. The nature of that industry presents some unique challenges and opportunities that do seem to make AI a natural fit, which is accelerating adoption of the technology worldwide. Here’s a look at how machine learning and AI are changing the mortgage industry around the world.
Assisting With Risk Management
According to US mortgage giant Fannie Mae, about 27% of all mortgage lenders are already using some form of machine learning or AI to assist with mortgage originations. The organizations they’ve surveyed overwhelmingly report interest in the tools as a way to augment fraud detection and borrower risk assessment processes. It’s an area that could use some reinforcement, too, as incidents of fraud in the mortgage applications process have been rising in recent years in the US and much of the rest of the world. Already AI and ML systems have shown great promise in combating the problem, by bringing real-time financial data analysis into the process to root out signs of fraud. One startup is even using biometric data captured during the mortgage application process to detect possible malfeasance – and reported a 97.6% success rate in their trial runs.
Matching Lenders with Clients
Artificial intelligence is also shaking up another major part of the global mortgage industry by taking on the role of the broker. It’s an important function in an industry with intense competition borne from the ongoing clash of big banks and newer fintech upstarts. For consumers, that competition helps drive down costs and create opportunities, but it’s not always easy to figure out who is offering the best deal at any given moment. That’s creating a whole new class of mortgage brokers using machine learning to connect lenders with borrowers faster and more efficiently than ever before. The key has been the development of algorithms that learn from real-time data streams to determine which mortgage product best fits the borrower’s circumstances. As the platforms mature, they’re bringing more lenders into the fold, multiplying their efficacy.
Lowering Fees and Approval Times
Last but not least, machine learning and AI is chipping away at two of the major pain points of the traditional mortgage market: cost and complexity. The latest innovations have already reduced the time it takes for a borrower to be approved for a mortgage from weeks or months down to as little as eight minutes, which could never have happened without the help of AI-powered approval automation. The time savings isn’t the only benefit, though. Silicon Valley startup Lenda also reports that their average customer saves approximately $48,000 on fees and additional interest compared to those using traditional mortgage lenders, and they believe there’s still room for improvement.
Innovation at Every Level
In short, the latest in machine learning and AI systems are changing the nature of the global mortgage industry at every level, bringing innovations that are producing fundamental change for both lenders and borrowers. It’s important to realize that all of the benefits we’ve seen thus far are still just the tip of what’s likely to be a much larger iceberg. Over the course of the next few years, it’s easy to foresee a convergence of the innovations mentioned here to create an industry that bears little resemblance to the one we know today – enabling efficiency, speed, and security at a scale nobody could have imagined in years past.