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dotData

Provide end-to-end data science solutions to help enterprise leverage data for business innovation.

dotData helps businesses that are just getting started with predictive analytics and more mature organizations that have established data science and data engineering processes. Our core technology automatically converts data from data warehouses and lakes into data marts and feature tables by exploring the relationships between varied data tables with hundreds of columns and millions of rows.

09/01/2026

Imagine a hidden 0.443% segment of your portfolio carrying a staggering 50.0% default rate—without your standard decisioning engine ever flagging it. 🚨

When risk drivers begin to stack, traditional metrics fall short:
• Secured Credit Card Driver: 39.3% Default Rate (vs. 20% baseline)
• Education Loan Driver: 25.6% Default Rate
• Stacked Overlap (0.443% of records): 50.0% Default Rate

Speed alone won't catch these high-risk overlaps—Signal Discovery will.

Protect your portfolio from hidden blind spots before they impact your bottom line. Read our full breakdown and see how to spot these stacked risks early: 👇

08/27/2026

Tier averages are hiding 2–3× default risk inside your auto portfolio—and most pricing models are completely blind to it.

Here’s why:

1️⃣ The Hidden Risk: Within every credit tier, specific sub-segments carry a default probability double or triple the tier average.
2️⃣ Outdated Models: Standard pricing models were built on last cycle’s averages, meaning they can't catch these micro-risk pockets.
3️⃣ The Solution is Already There: You don't need new data—it already exists inside your LOS, LMS, and bureau feeds.

What’s missing is the analytics infrastructure to join those data streams together and turn hidden risk into clear, examiner-traceable rules.

Read the full deep dive on optimizing auto loan pricing models below 👇

08/25/2026

📉 The average national FICO score fell again—from 718 to 715.
While a few points might sound like minor "macro noise," it reveals a massive hidden risk for auto lenders. 🚨

Here is the problem: Every origination scorecard is calibrated at a fixed point in time. But when the overall population distribution shifts, your cutoffs quietly start mis-sorting new borrowers without triggering standard validation tests.

Consider this:
🚘 The median new-auto score dropped from 724 to 716 in a single quarter.
🗓️ Recalibrating a traditional scorecard takes quarters.
⏳ A booked auto loan lives 60 to 72 months, but the score that approved it was only accurate for one single day.

If you wait for quarterly scorecard reviews, you are acting too late. Lenders need continuous discovery to spot portfolio drift and adjust pricing or policies while there is still time.

See where your current scorecard might be decaying before it impacts your bottom line:
👉 https://hubs.ly/Q04tXdZ60

Read more below ⤵️

08/21/2026

🏦 Is your AI credit decisioning platform examiner-ready?

While many platforms promise millisecond automated underwriting speeds, 65% of lenders still face major hurdles getting their multi-table data AI-ready. More importantly, black-box models are increasingly exposing banks to compliance risks.

Our blog covers the essential 3-gate framework every Chief Risk Officer needs to evaluate before adopting an AI credit platform:
1️⃣ Multi-table relational data ingestion
2️⃣ Glass Box output (deterministic, traceable SQL rules)
3️⃣ Seamless integration with existing Loan Origination Systems (LOS)

Don't wait for your next regulatory examination to uncover the gaps. Learn how dotData’s Signal Intelligence delivers the transparent insights that traditional scorecards and black-box engines miss.

Read more below ⤵️

08/18/2026

Speed was never the missing element in auto lending. 🚗⚡

When auto loan delinquencies hit 5.60% (the highest reading in the NY Fed series), most lenders respond by shopping for speed. They adopt "AI loan approval" tools to approve applications faster with fewer manual touches.

The problem? A faster wrong answer is still wrong.

Approval engines execute your current scoring model—they don't rewrite it. If your model misprices a segment, automating decisions simply multiplies the number of mispriced loans.

Check out the full infographic below for a module-by-module breakdown on why speed alone fails to reduce portfolio losses ⤵️

08/13/2026

Traditional credit boxes are increasingly exposed as early-stage auto loan delinquencies accelerate across the industry. Relying on static, siloed applicant data makes it incredibly difficult for lenders to identify modern risk factors like income inflation or dealership anomalies before funding takes place.

At dotData, we are helping auto finance institutions protect their P&L capital with Precision Underwriting. By programmatically evaluating data across multiple relational sources, risk teams can uncover non-obvious default indicators and deploy transparent business rules in minutes—with zero IT disruption.

Learn how to safeguard your portfolio and upgrade your credit scorecard architecture ⤵️

08/11/2026

Most "AI-powered lending platforms" are not solving the same problem.
If you compare vendors side-by-side, you're likely treating three structurally different tools as interchangeable:

🔹 Input validation (catches fraud early)
🔹 Decisioning automation (executes existing scorecards faster)
🔹 Signal discovery (finds risk patterns your scorecard was never built to test for)

The danger? Standard "best platform" rankings test speed and approval lifts. They don't show you the hidden blind spots in your underlying model.

When a model misses an emerging risk signal, it doesn't fail a test—it drifts. Quarter over quarter, your portfolio silently reprices risk you never caught.

Discover how to surface hidden risk patterns before a vintage closes ⤵️

08/06/2026

Market averages tell you what’s happening to everyone else. Signal Intelligence tells you what’s happening to you. 🚗🏦

With auto delinquencies on the rise, simply knowing that "risk is up" isn't enough to protect your institution. You need to know which specific dealer tier, vehicle type, or borrower profile is shifting—and you need to know it now, not in three months.

Our latest blog explores how to move beyond "Broad AI" to find actionable insights:
✅ Detect Model Decay: Spot when your scorecard is drifting before it causes losses.
✅ Explainable Fair Lending: Provide the transparent logic regulators like the CFPB now demand.
✅ Faster Action: Turn complex data joins into deployable rules in hours, not weeks.

Whether you're a credit union looking to protect your members or a subprime lender looking for hidden growth, it's time for a more surgical approach to portfolio monitoring.

Get the full breakdown below ⤵️

08/04/2026

Speed without precision is just an expensive mistake. 📉

Executing a 2-year-old scorecard at 10x speed doesn’t fix portfolio drift—it just accelerates mispriced risk.

Right now, most CROs evaluating "AI lending platforms" are testing for a single metric: decision velocity. But moving an applicant through a queue faster while relying on outdated risk assumptions only multiplies your loss rate in a compressed margin environment.
Speed is automation. Precision is Signal Discovery. They are not the same job.

Are you building for speed, or are you building for better signals?

Read our latest breakdown on balancing velocity with precision in credit underwriting below⤵️

07/31/2026

Is your dealer scorecard truly protecting your portfolio? 🧐

Most auto lenders face a "deadly data lag"—by the time a dealer’s performance looks bad, the damage is already done. In our new blog, "Why Dealer Performance Scorecards Fail in Auto Lending," we dive deep into the pain points that cost lenders millions in annual revenue.

From moving beyond simple volume metrics to uncovering "soft" operational losses, discover how dotData’s AI technology helps you find the signals others miss.

Check out the roadmap for strengthening your dealer relationships and protecting your P&L below ⤵️

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Tuesday	9am - 5pm
Wednesday	9am - 5pm
Thursday	9am - 5pm
Friday	9am - 5pm

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