Our Take
Finastra is selling visibility into a known problem (application dropout) as a packaged analytics product, but the only proof point is one lender's anecdotal process fix.
Why it matters
Mortgage origination is a high-friction, data-rich process where even small conversion gains move the needle on unit economics. Lenders are hungry for any signal that points to fixable leaks in their pipeline.
Do this week
Lending ops: Audit your own application exit data for the top 3 drop-off points before talking to vendors, so you know what a real diagnostic tool should surface.
Finastra launches Data Insights 2.0 for mortgage lenders
Finastra, a financial software vendor, has released Data Insights 2.0, an analytics dashboard built into its Originate Mortgagebot platform. The tool is designed to help banks and credit unions identify where mortgage applicants abandon the application process.
The product draws on anonymised data from over 1,000 mortgage originators to provide peer benchmarking and performance comparison against industry baselines. Key features include real-time tracking of application exit points, conversion analysis by borrower demographics and credit score, channel performance metrics, submission timing analysis, and geographic heat maps of activity. Dashboards include export functionality.
UnitedBank, a customer, reported that after using Data Insights 2.0 to identify drop-off points, the bank fixed its mobile experience and adjusted communication timing, resulting in improved completion rates (specific uplift not disclosed).
Mortgage lenders know they leak borrowers; the question is where and why
Mortgage origination is a long, multi-touchpoint process with well-documented abandonment: rate shopping, document collection, underwriting delays, and poor mobile experiences are known culprits. Most lenders have fragmented data across origination systems, email, and loan management platforms, making it hard to pinpoint friction without custom reporting.
A consolidated view of where applications stall, segmented by channel and borrower profile, is operationally useful. The peer-benchmarking angle is the real sales lever: if you see your completion rate lags the cohort by 8 percentage points and you can see that 40% of your dropoff happens at document submission on mobile, that creates pressure to act.
What remains untested: whether the benchmarking data is granular enough to isolate root cause (e.g., is the mobile friction due to UI design, or because you're asking for documents earlier than peers?), and whether fixing observed friction points actually moves conversion rates. UnitedBank's result is a single data point without controls or independent verification.
Separate signal from vendor convenience
Before adopting any analytics offering, map your own application funnel manually first. Identify the top 2-3 exit stages. Then ask: can this vendor's dashboard show me why borrowers leave at that stage, or just that they do? Peer benchmarks are useful only if the peer cohort is genuinely comparable (same loan type, geography, credit profile, funding model).
If the product is primarily a UI wrapper around data you already have in your origination system, the ROI depends on how fast your team can turn insights into process changes. Measure before you sign: track completion rates for 30 days using the tool, identify one friction point, fix it, and measure again. If you see movement, renew. If the benchmark looks stale or the cohorts are too broad, escalate that to the vendor before committing.