Significant losses can dramatically undermine a lender’s ability to remain competitive. Reducing default is business-critical — and it must start at credit decisioning. But how can financial institutions make an impact when they have too little data and it’s too hard to get the insights needed to more effectively assess risk?
|Consumer and small business credit default is a persistent challenge for banks, credit unions, and alternative lenders. While many people are recovering from the economic upheaval of 2020, default rates are still on the rise in 2022, particularly for bank cards and auto loans, according to S&P/Experian indices.||$25 billion
in loan default volume estimated in 20222
Powering credit decisioning with machine learning (ML) is key. When fueled by multiple alternative data sources, predictive ML solutions make it easier to understand and predict an applicant’s ‘real affordability’ and the likelihood of never-pay default.
Reducing default risk with ML credit decisioning
The path to profitability is approving more creditworthy customers – but that relies on having a more granular understanding of each applicant’s cashflow and payment profile over time. And whether their name is associated with a questionable banking history or other accounts that have historical fraud profiles.
Unlike a few decades ago when lenders were limited to data from credit bureaus like Experian and Equifax, they can now access a myriad of third-party data providers. The ongoing challenge, however, is how to make sense of all that data and put it to work to uncover actionable insights for risk decisioning.
To speed progress, it helps to collaborate with data scientists who have deep expertise in financial services and the risk industry. They specialize in building ML-driven credit decisioning models that can reliably predict credit risk, tapping into current and historical data across internal client sources, payment processing, credit reporting from multiple agencies, and open banking transaction data.
|Using GDS Link’s predictive ML decisioning solution, for example, certain small business lenders were able to increase net funding by $11 million annually, while reducing losses by 57%. And some consumer lenders reduced first payment default by 40%, while declining only 10% of applicants.||$11M Increase
in net fundings annually using ML credit decisioning[Source: GDS Link Client Results]
ML in action – Real-world success snapshots
While most financial institutions know they need to invest in AI/ML, many don’t have the infrastructure or expertise to take advantage of the latest advances. Let’s take a closer look at how two companies made a substantial impact on their bottom line, with the help of data science experts and ML credit decisioning technology.
Consumer lender doubled their loan business within a year
A pioneer and leader in specialty consumer loans needed to minimize risk and improve underwriting for a new unsecured lending product. They had no decisioning or underwriting platform and were not able to build it. Partnering with GDS Link equipped the lender with a modernized credit decisioning platform with ML analytics, aggregated data from a wider range of sources, and automated underwriting workflows for cost-saving efficiency.
With greater agility to effectively assess credit risk and speed underwriting, within one year the lender doubled their loan business to $20M, and less than a year after that had grown to $50M in loans.
Trucking finance specialists lowered costs with higher credit decisioning performance
One of the largest specialty loan providers for truck and trailer financing in North America needed a powerful, yet cost-effective replacement for their aging legacy analytics platform for credit decisioning and loan origination. As it was too expensive to license and maintain the old system, they turned to GDS Link and the ML-powered platform, Modellica.
With a hosted cloud platform that can use either custom-built ML models or turnkey models for fast deployment, the company was up and running quickly. They were able to adapt easily, meeting the same core needs, yet in a cutting-edge decisioning solution – and at a reduced cost, with easier day-to-day management.
Accelerate impact with the right credit decisioning solution with in-built machine learning
To speed time to market, more and more financial services providers are finding technology partners to gain fast, easy access to powerful ML capabilities, with advisory support from experienced data scientists.
Leveraging a hosted ML platform, such as GDS Link’s Modellica Pro, requires very little involvement from a client’s IT team, so it saves time and money. Organizations also benefit from collaborating with GDS Link data scientists who have deep expertise in financial services. It helps financial institutions understand how predictive models work and the best data sources to use, and they can feel confident knowing their ML models are continuously monitored and retuned as needed to keep performing well over time.
Modellica Pro, our hosted lead scoring platform and data science advisory service, delivers the scale, speed and predictive capabilities to help financial institutions significantly reduce risk, drive revenue, and increase competitive advantage.
See ModellicaPro in action – Request a demo
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- Reducing Default with Machine Learning Credit Decisioning