3 Keys to Successfully Applying AI/ML to Servicing Operations
Many companies feel pressure to hop aboard the artificial intelligence (AI) and machine learning (ML) train for fear of falling behind. Implementing AI in your servicing operations, however, isn’t something to do haphazardly. AI isn’t appropriate for every business challenge, and there are still many aspects that aren’t well understood. Additionally, if you’re looking for a one-and-done solution – AI isn’t it. Regulatory changes and market conditions will affect your AI model, so you must be prepared to continuously evaluate and update. This requires a steady stream of new data to avoid letting your model become irrelevant.
With these points in mind, there are many applications of AI to address mortgage servicing challenges, such as customer dissatisfaction or back-office inefficiencies. For example, Black Knight will leverage AI in its Customer ServiceSM solution to predict why a borrower may be calling customer service. By assessing loan data, the customer’s conditions, recent mortgage activity and more, Customer Service will give support representatives a prediction for why the borrower is calling. Providing relevant data along with the prediction and a confidence score delivers necessary model explainability. This application of AI may contribute to more productive customer support interactions, higher customer satisfaction and reduced call times.
If you’re considering applying AI to your servicing operations, consider the following keys to success.
Clearly Define Your Challenges
Many servicers are so eager to use AI they forget to define their pain points. This is a critical step. The defined challenge determines required data inputs, which, in turn, inform the configuration of the AI model.
Not every challenge can, or should, be solved with AI. If a question can be answered with a simple query or procedural code, for example, AI probably isn’t the best way to go. On the other hand, AI can be quite helpful in determining the best way forward in nondeterministic situations where there are more than one potential course of action.
Identify Methods to Gather Quality Data
AI is all about data, and your implementation success will be determined by the quality of the data you feed into your model. This is especially pertinent in mortgage servicing, due to the tremendous amount of data involved.
After you’ve identified the challenge you want to address, you can determine what data is required to make an informed decision. Then, it’s time to identify sources and collect dependable data.
Data quality is characterized by consistent formatting, minimal elements and complete values. Don’t forget to normalize your data, which means translating the values to a common scale.
Be Prepared to Explain and Update Your AI Solutions
Explainability should be a standard part of your vocabulary if you plan to use AI in your servicing operations. Given the Consumer Financial Protection Bureau’s increased scrutiny of AI, you should be able to explain how you obtained your predictions, and keep this in mind when building, validating and training your AI models.
Explainability also quantifies the ability of a model to produce accurate outputs.
Is AI Right for Your Operations?
AI is complex, but it offers the potential to solve equally complex problems. Remember to have a solid grasp on the challenge you are addressing, collect quality data accordingly, and stand prepared to thoroughly explain your process – and you’ll be well on your way to successfully implementing AI in your servicing operations.
Are manual tasks slowing down your processing speeds and adding unnecessary costs to your operations? AIVA® – Black Knight’s virtual assistant powered by AI and ML – helps eliminate repetitive tasks to save lenders time and reduce operating expenses. Learn more about AIVA.
