Consumers are resistant to friction in their customer experience journeys, whether they are buying appliances, vacations, vehicles, or applying for credit.
Next-gen data and decisioning technology is crucial for financial institutions to focus on growth while meeting consumer needs and expectations, and effectively managing risk.
Unfortunately, there are a number of myths that persist in this area, eroding financial institutions’ ability to compete and thrive – and keeping consumers from the frictionless, rich, and relevant experiences they deserve.
Let’s examine these myths in detail:
Myth #1: Traditional Credit Data is the Entire Story
Traditional credit data is rarely enough to paint an accurate, holistic picture of a customers’ creditworthiness. Alternative data sources, including mobile/telco information, rent and utilities data, social media/web presence, and open banking info can help organizations gain a more comprehensive view of a potential customers’ financial health as well as their ability and willingness to pay.
There exists tons of data out there — often residing in siloed environments — making it difficult to access and costly to integrate into credit decisioning. While it can be easy to assume that more data is the answer, the key to optimizing your data strategy is having the right data at the right time.
Seventy-four percent of decision-makers surveyed said they struggle with their organization’s credit risk strategy because data is not easily accessible, and 70 percent say alternative data is not easily integrated into their current decisioning system. The use of alternative data to supplement traditional credit data (primarily bureau data) is critical to not only giving organizations a more accurate, real-time view of their customers’ creditworthiness, but it also expands the lending market. By saying “yes” to individuals who may have lower traditional credit scores, financial institutions are improving financial inclusion and ensuring greater access to financial services while growing their business.
Myth #2: Upgrading Decisioning Technology is Too Costly
It’s easy to assume that changing decisioning technology will involve massive upfront investment from an organization (not to mention the fear of ‘wasting’ previous investments in legacy technology). But organizations can ill-afford not to upgrade. It’s important to realize the additional cost savings with self-sufficiency when changing decisioning workflows and launching new products.
Cost pressures are everywhere, so it’s not surprising that financial institutions are reluctant to consider changing technology platforms. But it’s important not to let the fear of past investments hold an organization from moving forward. Given increased competition, demanding consumer expectations, and a shifting regulatory environment, next-generation decisioning technology is key. The cost of doing nothing can negatively impact organizations efficacy in acquiring new customers, keeping existing customers, preventing fraud, and satisfying compliance requirements. Put simply, non-action is a non-option. Upgrading decisioning technology results in a lower total cost of ownership, due to eliminating product launch and iteration delays that lose customers, the ability to automate risk decisioning workflows for more efficient processes, and improved fraud detection/prevention.
Myth #3: It’s Too Difficult to Overhaul Current Systems
Organizations should look for decisioning solutions that can run in parallel with their current software, or for ways to orchestrate their data more efficiently with a data ecosystem. Early wins can create buy-in with other departments and lines of business when they realize the improved efficiency and overall decisioning.
A phased deployment approach can work well. There are flexible, agile decisioning platforms available that can integrate with or run alongside an organization’s existing workflows and the option to upgrade one line of business at a time. The key is choosing a technology platform that makes this easy with a partner that has experience with swapping out competitive decisioning platforms.
Upgrading Credit Risk Decisioning Technology Means Running the Smarter Race
The biggest challenge financial institutions face is competition – and the subsequent need to power faster and smarter risk decisioning. The key to faster, more accurate risk decisions and the ability to launch new products in less than half the time is next-generation decisioning technology.
Your request for proposals should be designed to identify vendors that can address the breadth and depth of a wide variety of requirements. These include:
- Real-time data access to hundreds of data sources through a single API
- Advanced analytics based on your organization’s unique risk profiles
- Integrated case management for a complete end-to-end perspective on credit applications
- The ability to handle evolving compliance regulations and security demands
- Low-code, business user-friendly user interface that enables self-sufficiency when changing processes and iterating workflows
- Experience with swapping out legacy technology/competitive decisioning platforms to ensure a seamless transition
Leveraging automated, integrated data and more agile risk decisioning technology can improve flexibility, accuracy, and speed to remain competitive and meet regulatory compliance requirements, all while making more informed credit decisions that improve the customer experience.
Kathy Stares, Executive Vice President of North America at Provenir
Kathy Stares is Executive Vice President of North America for Provenir, a global leader in data and AI-powered risk decisioning software, processing more than 3 billion transactions annually for disruptive financial services organizations in more than 50 countries worldwide. As a member of Provenir’s executive team, she is introducing creative account management approaches to support the company’s aggressive growth strategy. With more than 20 years of experience and accomplishments in financial services technology, Kathy brings deep knowledge and curiosity about risk decisioning innovation.