Despite enormous potential, mortgage companies are mostly lagging behind when it comes to data analytics adoption and business intelligence. Research suggests that only 3% have the data maturity needed for predictive decisions, and 37% have just begun their data journey. Yet, analytics, artificial intelligence, and business intelligence tools are racing forward in terms of their evolution – making it essential for the mortgage industry to catch up. One such advancement is decision intelligence.
What Is Decision Intelligence in Mortgage?
The term decision intelligence was popularized by a 2019 book called “How Decision Intelligence Connects Data, Actions, and Outcomes for a Better World” by Dr. Lorien Pratt, who, along with Mark Zangari, is regarded as the originator of the field. It refers to the use of data science in a strategic manner – accelerated by artificial intelligence (AI) and machine learning (ML) – to improve every decision by studying and predicting cause-and-effect outcomes.
Gartner defines decision intelligence as “a practical domain framing a wide range of decision-making techniques bringing multiple traditional and advanced disciplines together to design, model, align, execute, monitor, and tune decision models and processes.”
Importantly, the field is part of the Gartner Top Strategic Technology Trends for 2022, making it vital for mortgage providers to understand and implement the concept.
In simple terms, decision intelligence in mortgage can be defined as the use of data science, social science, managerial science, and other disciplines to predict how an action or circumstance will lead to a certain outcome. Cognitive technologies like AI and ML aid decision intelligence by making these predictive data models more accurate. The ultimate goal is to enable 100% mortgage process automation by allowing machines to make incredibly reliable decisions.
How Can Decision Intelligence Impact the Mortgage Industry?
There are many ways in which decision intelligence can impact the mortgage industry. Mostly, it will bring about several incremental improvements at critical junctures of the decision-making process. This includes:
1. Reacting better to Black Swan events
Acts of God like the COVID-19 pandemic or a natural disaster are difficult to predict and navigate. Existing data models can no longer inform mortgage decisions, and providers must constantly adapt to accommodate new laws, such as the CARES Act. In this case, decision intelligence systems can ingest new and emerging data in near-real-time, to support in-the-moment recommendations. Mortgage providers can therefore stay agile even in challenging market conditions.
2. Minimizing waste and mitigating revenue leakage
Minimizing waste was always a top priority for business leaders managing mortgage operations, and it has never been so important as in this current inflationary period. Decision intelligence can help evaluate existing working models to pinpoint waste and inefficiencies.
Can certain cost reductions accrue outsized value? Are there neglected areas of value generation lying idle in the business? Are resources being underutilized? Can mortgage process automation offload unnecessary effort? Decision intelligence provides highly accurate projections to answer these and other questions, which helps minimize waste.
3. Identifying strengths and weaknesses in your workforce
Workforce analysis and optimization is another way that decision intelligence can help mortgage providers. The industry is currently looking at an aging workforce and a yawning skills gap, especially when it comes to using digital tools. Routine tasks like data entry and document checks remain effort intensive, particularly in the absence of mortgage process automation. A decision intelligence model can help make better decisions about the workforce – e.g., layoffs/recruitment, outsourcing, reskilling, etc.
4. Optimizing property valuation
Typically, property valuation depends on numerous variables, ranging from market conditions, property preservation, ownership history, debt-to-income ratios, and much more. The sheer breadth of variables combined with manual work can lead to inaccuracies. In contrast, a decision intelligence model can cover more ground, and it does so in an automated manner. It can factor in a wide variety of data sets – including unstructured or big data – to provide highly accurate estimations.
5. Streamlining customer experience and enabling mortgage service automation
Finally, mortgage customer experience (CX) is a key area where decision intelligence can make a massive difference. It can help create better segmentation and target mortgage products as per demographics. A decision intelligence framework will be able to pinpoint high-value customers, potential defaulters, and other borrowers requiring extra attention. Then, you can use mortgage service automation tools to target these cohorts and deliver tailored CX and servicing processes.
How Mortgage Providers Can Get Started with Decision Intelligence
There are a couple of prerequisites to note before adopting decision intelligence systems. First, they rely on extremely robust technology capabilities combined with digital-era strategic skills. For instance, a mortgage company with a data fabric will find it easier to get started than a lender starting from scratch. It is also advisable to partner with an experienced mortgage technology provider like Nexval, which offers tech-enabled products and services and also brings sufficient interdisciplinary experience.
Also, cybersecurity is a primary concern – as decision intelligence relies on extensive data modeling that could endanger borrower privacy, compliance with data laws, and intellectual property. As a result, you may need to undertake a cybersecurity risk assessment before building systems that are meant to ingest, process, and analyze large volumes of financial data. To learn how, speak with our Tech Gurus today.
Frequently Asked Questions About Decision Intelligence
Q1. What is the difference between decision intelligence and business intelligence?
Business intelligence helps an organization in analyzing, visualizing, and extracting useful insights from tools to create a report about pre-defined questions whereas decision intelligence transforms those insights into recommendations that helps an organization in decision-making.
Q2. Why is decision intelligence important?
Using mere business intelligence tools is not enough. Organizations need to get contextualized and actionable insights. That is when Decision Intelligence can help with more quantifiable recommendations leveraging artificial intelligence and machine learning to make accurate decisions faster.
Q3. How can organizations use decision intelligence?
Organizations can use decision intelligence to:
- Understand problems in depth and do adequate research to solve the problem
- Calculate the expected return on investment of the recommended decision
- Make the decisions according to your organization’s capabilities and resources