Four considerations for adopting a mortgage AI platform (and, yes, you should)

Artificial intelligence/machine learning (AI/ML) isn’t able to leap tall buildings (yet), but it can drive cars, write comics, teach history and—if that weren’t enough—explore the universe from quantum to cosmic scales faster than the human mind can.

On a more earthly level, there’s no doubt AI/ML has the capacity to help lenders reduce operational costs, increase employee bandwidth, improve the customer experience and provide better documentation validation and management.

AI and ML have the potential to automate “stare and compare” tasks and help employees optimize their workflows. That’s because AI/ML brings a new dimension to lender technology platforms. AI and ML can analyze vast amounts of data using complex algorithmic logic to “learn” over time and continually improve performance. That differs considerably from traditional mortgage software, which uses structured, rules-based processing to determine outcomes.

Adopting an AI/ML-based platform must take these differences into account, especially when anticipating future regulatory oversight of the algorithms and decisions regarding fair lending practices. It should also fit well into your strategic business plan to smooth operations, reduce risk and more. You should have these needs well identified before talking with a vendor about their products.

What is artificial intelligence and machine learning?

AI/ML is a general field of computer science that includes many categories.

  • Artificial intelligence is a computer’s ability to perform a given task that is otherwise accomplished using human intelligence.
  • Machine learning involves the creation of algorithms and methods that can “learn” or get better when provided with more data.
  • Natural language processing (NLP) allows machines to read and use human languages. Document classification based on the words identified on a page is an example of NLP.
  • Deep learning is a type of machine learning algorithm that uses multiple layers of model elements to derive a solution.
  • Predictive analytics is a broad category for application of techniques including data mining, modeling and machine learning in business to seek patterns in potentially large sets of data to make predictions about future or unknown events.

Top considerations for a successful AI and ML adoption strategy

Dark Matter Technologies understands this future well and works with lenders across the country to help them develop strategies and guardrails to safely expand their use of AI/ML technologies. From these interactions, we’ve identified four factors that customers should have to successfully prepare for an AI/ML platform:

A lot of good data—Because AI/ML is powered by data, financial institutions are realizing that having quality data is crucial to training their models and gaining more accurate AI performance. Your AI/ML strategy needs to start with an assessment of your data and data architecture. Lenders need to build an infrastructure that facilitates the sharing of data with their AI/ML models.

A performance-monitoring plan—AI/ML managers should conduct regular reviews of the data used for training to ensure that it is sufficiently representative of production data. Managers need to have processes to quickly evaluate the performance of new models in production and ways to take corrective action if needed.

Stakeholder involvement and education—Managers should provide stakeholders across the organization with educational materials and regular briefings. Using AI/ML will impact executive decision-makers, compliance, legal, human resources (HR) and audit teams and a successful program will provide them with knowledge of AI/ML-specific risks and issues.

Vendor compliance transparency—AI/ML has recently been subject to heightened concern by regulators. To proactively address future regulatory demands, responsible vendors should take an active role in developing AI algorithms with stronger explanatory capabilities from the inception. Explainability requires more than a description of a model’s features. Your vendor’s data scientists need to clearly identify the source and composition of training data, provide transparency about how models operate, and be able to document and report on measures taken to detect and correct model bias.

Plan today for a more productive tomorrow

AI/ML is not magic that will solve every business problem and deliver every digital experience without human involvement. Machines can’t do all of your tasks without any human oversight. Contrary to what AI alarmists are saying, AI/ML is not remotely close to human intelligence; it can only do what it is instructed to do within the framework of a specific business problem or question. In essence, AI/ML is often highly sophisticated educated guessing. While AI can greatly increase productivity and enhance the mortgage lending experience, humans will always need to monitor, train and validate the models—as well as handle exceptions.

What AI/ML can deliver is faster insights, greater operational efficiency and enhanced customer experiences across the loan lifecycle. But to use AI/ML effectively, your objectives, data, technology and stakeholders must be aligned. Planning today for future regulatory requirements can help you safely and confidently adopt more sophisticated AI/ML technology to maximize your operational performance and thrive in a competitive mortgage market.

Increasingly, lenders are outsourcing AI/ML planning and services to companies like Dark Matter to guide them in this important transformation. While AI/ML is unique, with research, sound planning and the right investment, it can provide many significant competitive advantages to take your business to the next level.

To learn more about AI/ML in the mortgage industry and how you can safely adopt it in your technology strategy, watch our video interview with Dark Matter EVP of Product, Legal and Compliance Blake Gibson.