Making a big purchase, such as a car or home, can be stressful for everyone involved, from doing the due diligence to identifying a good lender. Everyone wants to make the process smooth while mitigating risks.
Banks and lenders also have more data to work with when making a lending decision, from payment histories to income to buyer behavior. Analytics solutions provide organization, visualization, modeling, and decisioning capabilities to understand these data points. Although most organizations know how to collect the data, fewer create analytics models and place them into production, costing valuable ROI.
Modeling and decisioning technologies provide the perfect duo to help organizations make that sound investment. Here are three reasons why:
The speed of the market is changing
The last few years have shown us that markets can change instantly, driven by geopolitical, economic, political, and natural sources. A resilient digital transformation strategy is essential to navigating uncertain and uncharted waters, especially when customers seek clarity and guidance.
Robust model management and decisioning solutions make pivoting possible, providing business leaders with the tools to serve customers quickly while updating expectations. Taking days or weeks to adjust only results in lost revenue and lowered trust.
Using the entire AI and analytics lifecycle is a competitive advantage
Data management is a relatively mature industry, having grown in importance over the past three decades as user data increases. On the other hand, strategically using that data remains a challenge as business users, data scientists, and analysts debate how to translate insights into action.
For decades, SAS has helped customers understand how to use their data through the AI and analytics lifecycle. This framework considers how any organization collects, manages, and uses data.
Model management and decisioning are towards the end of that lifecycle, where solutions such as SAS® Model Manager and SAS® Intelligent Decisioning support governing and monitoring model efficacy, building decision flows, and incorporating business rules to benefit the business.
Organizational leaders who use this last segment of the AI and analytics lifecycle are setting themselves up for long-term success.
Why model management?
As institutions increasingly normalize data and AI solutions across their enterprises, they’ll recognize that just as their data benefits from being validated, cleansed, and governed, their modeling artifacts require the same disciplined approach.
By enabling data scientists and business analysts across different business units to register, train, deploy, monitor, and update models in a centralized and highly visible system, organizations create a foundation for repeatable AI processes. This approach accelerates asset discovery, simplifies collaboration and ensures AI-driven practices are disseminated responsibly throughout the enterprise.
For highly regulated industries, a centralized model registry provides an organized and transparent solution for risk analysts and regulators to easily access models, helping to address and mitigate regulatory concerns. Furthermore, as market conditions inevitably evolve, strong model management systems can notify stakeholders of necessary updates, ensuring affected models are adjusted promptly.
This proactive approach allows financial institutions to remain operational and competitive while managing risk in a dynamic environment.
Automation enables a refocus
High demand always strains any organization, and financial institutions face the greatest challenges when interest rates are low. As a result, automation is becoming a greater focus for business leaders as they look to streamline costs while preserving a similar level of customer service.
Automation is possible when efforts are supported with modeling and decisioning solutions. Take a home lender, for example, that faces many loan requests. With their understanding of their customers and associated risks, their data scientists can create models that show a range of credit and risk-related factors to approve, reject, and hold for further review based on current market conditions.
Decision automation can handle tailored actions from both rejects and approvals based on predefined business logic in real time and loan officers have more time to dive deep into the cases that require more attention.
Closing thoughts
It’s true that big purchases, such as a home or vehicle, can be anxiety-inducing. Modeling and decisioning solutions used together – such as SAS Model Manager and SAS Intelligent Decisioning or SAS® Decision Builder on Microsoft Fabric – make the process smoother by enabling faster, smarter, and more personalized decisions.
These tools help organizations meet customer expectations while staying competitive by automating workflows, ensuring compliance and using the full AI and analytics lifecycle. Simply put, they make complex financial decisions easier – for businesses and their customers.