Off-us data has transformed how lenders think about risk, growth, and competition. But like any powerful tool, it’s easy to misuse. In our work with clients, we’ve seen three common pitfalls — and the practices that can help avoid them. Pitfall 1: Misinterpreting Benchmarks Looking at competitor performance is incredibly valuable — but dangerous if not translated correctly. One lender assumed higher rates would automatically mean worse performance. Instead, benchmarking revea

Lenders know their own portfolios inside and out. But when it comes to growth, risk management, and product strategy, the smartest players don’t just look inward — they look outward. “Off-us” data — information about customer behavior and lending performance outside a company’s own book — is becoming a critical lever for success. Here are a few ways we’ve seen lenders use off-us data to sharpen decisions: Benchmarking Performance One lender in Asia used bureau data to compare

Most lenders we work with already have a sizable pile of data—application fields, bank data, bureaus, device signals, platform behavior. When building a credit model, not all data is good data. Some data actually carries long-term risk not just to your model, but to your entire business. Here’s a quick guide to evaluating your data. Here’s a snapshot of what we often see on our first call with a lender: Application data : Self-reported income, employer name, product type, pur

One of the most common questions we get about building a credit model is " How much data do I need to make it work? " And like many questions in credit modeling, the answer is " it depends ." In this post, we’ll break down the data volume question across three dimensions: rows , columns , and outcomes , and how those impact your modeling approach. Row Count: How Many Records Do You Need? Most clients asking about data volume are thinking of the row count (the number of histor