How Advia Credit Union used Faraday’s data and AI to generate $2.7M in new auto loans in 90 days

At a glance

• The core challenge: Advia Credit Union knew their members needed auto loans and other financial products — but couldn't identify which members to target, when, or with what offer, and couldn't risk fair lending violations in the process.
• The approach: Enriched historical member data with the Faraday Identity Graph, built a propensity model trained on past auto loan outcomes, and ran bias mitigation to ensure fair treatment across protected classes before deployment.
• The results: Application rate climbed from 1.19% to 5.18% in 90 days — generating $2.7M in new auto loans without hiring a data scientist or building proprietary modeling infrastructure.

—

Advia Credit Union had a wallet share problem. Their members were financially active, but they weren't always channeling that activity through Advia. The average American holds eight financial products — but only 19% of Americans have three or more products with their primary financial institution. Advia knew their members needed auto loans, mortgage refinancing, and debt consolidation products. What they didn't know was which members to talk to, when, and with what offer — and how to make sure they captured that business before another institution did.

Adding to the challenge, Advia's lead data scientist had recently left the organization — making it clear that building this kind of targeting capability in-house wasn't a realistic near-term option. What they needed was a way to get smarter about their existing member base without rebuilding a data science team from scratch or running afoul of fair lending regulations.

The wallet share problem

Credit unions and banks face a structural challenge that most consumer brands don't: their best growth opportunity is sitting right inside their existing member base, but activating it requires navigating a uniquely complex data and compliance environment.

First-party data tells you what a member has done with your institution. It doesn't tell you where they are in their financial life, what products they might need next, or how likely they are to act on a given offer. That missing context is what turns "grow wallet share" from a strategy into a guessing game — and what leaves most institutions relying on broad campaigns that erode member trust faster than they build revenue.

Balancing growth with fair lending compliance

Financial services is one of the few industries where getting targeting wrong doesn't just waste budget — it creates legal exposure. The Equal Credit Opportunity Act (ECOA) prohibits discrimination in credit transactions based on race, color, religion, national origin, sex, marital status, age, or receipt of public assistance. Any scoring model used for member targeting must be built and validated with these constraints in mind.

The risk isn't always intentional. Historical member data often reflects past patterns of underrepresentation, and a model trained on that data without bias mitigation can quietly perpetuate those patterns at scale. This is why many financial institutions either avoid data-driven targeting altogether or deploy it without the guardrails they need.

Faraday treats bias mitigation as a foundational step, not an afterthought. Before any model is trained, Faraday rebalances historical data to correct for underrepresentation across demographic groups and monitors the resulting scores for fairness across protected classes. The result is a model that's both more accurate and more compliant — not a tradeoff between the two.

For Advia, this was as important as the performance numbers. A campaign that drives loan applications but creates fair lending exposure isn't a win.

How Faraday built Advia's member scoring

Advia's auto loan campaign started with a data connection. They linked their historical member data — including records of which members had previously taken out auto loans — to the Faraday Identity Graph (FIG): a continuously maintained dataset covering 240M U.S. adults across 1,400+ verified consumer data points, spanning demographics, financial signals, property data, life events, and lifestyle indicators.

That enrichment step is what transforms a member record from a transaction history into a full consumer profile. FIG adds the context that first-party data alone can't provide — household income, life-stage signals, financial behavior patterns, and more — giving the scoring model far more signal to work with.

With enriched data in place, Faraday built a propensity model trained on Advia's historical auto loan outcomes. The model learned the profile of members who had taken out auto loans in the past — and used that pattern to score every current member by their likelihood to apply for one now. Bias mitigation ran in parallel: Faraday identified demographic imbalances in the training data and rebalanced the model to ensure fair treatment across protected groups before any scores were deployed. Faraday validated model performance using holdout testing before deployment.

The output was a ranked list of members by auto loan propensity — with the top percentile identified as the highest-priority targets for Advia's campaign.

The results

Advia used Faraday's scores to segment their member base and target the top percentile through personalized email and direct mail campaigns (see what integrations are available for these types of campaigns on our integrations page). The benchmark they were working to beat: a 1.19% auto loan application rate. And here’s what they got:

30-day results: Application rate reached 2.41% — more than double the baseline — generating $1.1M in new auto loans.

90-day results: The application rate continued climbing to 5.18% after 90 days, producing a total of $2.7M in new auto loans.

That's more than a 4x improvement in application rate, achieved without hiring a new data scientist, without building proprietary modeling infrastructure, and with a compliance posture Advia could stand behind.

Expanding predictive scoring across the entire portfolio

The Advia story started with auto loans, but the same approach applies to any financial product a credit union or bank wants to grow. FIG enrichment, propensity scoring, and bias mitigation can be applied to mortgage refinancing, debt consolidation, credit cards, savings products, and more.

Each product becomes its own scored outcome: who among your current members is most likely to need this right now? Layered together, those scores give your team a clear view of which offer is most relevant for each member at each stage of their financial life — and the confidence to act on it without compliance risk.

For a broader look at how banks and credit unions use customer context across their member base, see our guide to predictive AI for banks and credit unions.

Ready to grow wallet share?

If you want to talk through how customer context could work for your institution, talk to a Context Consultant. Or if you'd rather get started on your own, try it on buy.faraday.ai.