As we established in the previous chapters, the concept of AI is fundamentally straightforward: a machine capable of taking actions and making decisions akin to human thought processes
The benefits of AI are equally clear.
AI helps organizations streamline business processes and workflows, enhancing decision-making and achieving greater efficiency and productivity. This allows people to focus less on administrative tasks and more on strategic initiatives, leveraging their unique skill sets to achieve significant goals.
However, the execution and innovation of AI—making it practically functional for businesses—can be quite complex. While AI technologies simplify many jobs by automating manual and repetitive tasks, considerable effort goes into making these technologies work effectively.
How Does AI Work?
Initially, AI machines were limited to following step-by-step instructions in computer programs.
Today, AI technologies are more autonomous and "intelligent" because they are built using data science. IBM defines data science as combining “math and statistics, specialized programming, advanced analytics, artificial intelligence (AI), and machine learning with specific subject matter expertise to uncover actionable insights hidden in an organization’s data.”
While Artificial Intelligence and data science are distinct concepts and practices, they are closely intertwined.
When building AI technology, programmers use algorithms that guide platforms and applications in processing input data and converting it into output data. These algorithms are designed to be flexible, enabling machines to follow different workflows as needed.
AI algorithms allow machines to make decisions using data, including real-time inputs. They “combine information from various sources, analyze it instantly, and act on the insights derived from that data.”
Avoiding “Garbage In, Garbage Out”
The old saying, “garbage in, garbage out,” is especially relevant in data science and AI. For AI-driven machines to be effective, they need precise and well-informed programming guided by algorithms built exclusively with high-quality input data.
No matter how “intelligent” a machine is, it needs a comprehensive set of rules and logic—robust, future-proof computer programming—to perform at a human-like level.
Given data's dynamic nature, programmers and data scientists cannot simply “set it and forget it” when developing algorithms and programming AI-based machines.
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Overcoming Data Quality Issues: Integrating Humans into the Technology
Ethical AI use today requires data scientists to perform quality checks—regular audits to identify duplicates, outliers, missing information, corrupted files, and even typos.
While the frequency of human intervention varies, consistent human oversight is always crucial to maintaining data integrity. A combination of sound programming and regular data checks ensures that AI tools can access complete, accurate, relevant, and high-quality data, producing the intelligence organizations need to remain competitive.
As discussed in greater detail later in this book, AI isn’t magic. Companies must balance human performance and machine output to capitalize on AI's opportunities.
Ensuring the Best Data Sets for AI in Mortgage Banking
An organization’s success with AI depends mainly on its data quality.
Your AI tool must consistently access the correct data to produce precise and accurate insights. Data silos—isolated data collections independent from other information sources and untouchable by different stakeholders—pose significant challenges. These silos are often stored in a single location, whether digital or physical.
Ensuring consistently high-quality, integrated data is challenging but crucial. The number of data sources available to mortgage bankers has grown exponentially in recent years and will continue to do so, making data management quite complex.
The process begins with a deep assessment of your data—identifying where it resides and conducting an honest review of its quality.
From there, you can take steps to ensure that your AI technology works with optimal data sets:
Adjust Your Data Governance Model to Maximize the Power of AI
Data governance involves “the collection of processes, policies, roles, metrics, and standards that ensure an effective and efficient use of information.” It helps establish data management processes that keep your data secure, private, accurate, and usable throughout its lifecycle.
Companies should revise their data governance policies to ensure that all input data sets are accurate, consistently formatted, complete, timely, and relevant. Establishing data quality standards, processes, and oversight roles is crucial for optimizing the AI experience.
Conduct Regular Data Audits
Organizations with a dedicated team of professionals overseeing data quality will reap the greatest rewards from AI solutions. This team should perform regular data audits to ensure data handling adheres to established governance models.
Routine audits empower organizations to “uncover silos, access issues, or areas where a greater depth or breadth of collection would be beneficial.”
A specialized team monitoring data quality can identify and execute necessary improvements and upgrades. Providing these professionals with appropriate resources, such as data quality technology solutions, is also essential.
Establish an Integrated Digital Ecosystem
Mortgage banking organizations should create a digital architecture where all technologies can communicate and share data. The more integrated these systems are, the less likely data silos will form.
For example, if data from an underwriting technology can automatically populate within loan processing systems, the chances for inconsistencies and miscommunications are significantly reduced. An integrated digital ecosystem provides a single source of truth, optimizing AI performance and output.
Work Exclusively with Trusted Data Sources
Because AI technologies access all your data sources, it is essential to work with vendors who guarantee that their data is always up-to-date, accurate, and comprehensive. Additionally, ensuring proper permissions for using and distributing the data is crucial for compliance.
For instance, partnering with a reliable data provider will ensure that AI solutions do not pull disparate data sets, leading to inconsistent insights. A trusted data source can enhance the reliability of the AI system’s output.
By aligning your data management practices with company goals and regulatory compliance, you ensure that your AI technology is best positioned to facilitate transformational operational efficiency and productivity.
With these foundational elements, mortgage banking organizations can harness AI's full potential, driving efficiency, improving decision-making, and ultimately enhancing the customer experience. Stay tuned as we delve deeper into the intricate world of AI and its specific applications within the mortgage industry in the upcoming chapters.
