Loss Mitigation Under NCUA Examination
TL;DR A credit union’s loss mitigation policy was likely written against a letter that is only half current. NCUA Supervisory Letter 13-02 still governs what examiners expect in a workout policy today. One piece of that same letter, the TDR classification and reporting requirement, has not applied as written since credit unions adopted CECL. This […]
Core Deposit Analytics for Community Banks
TL;DR A community bank already reports a core deposit number every quarter. Almost none of them can say, on any given day between those reports, exactly which accounts are driving it. This piece lays out what the FFIEC counts as a core deposit, why that definition is harder to apply than it looks, and what […]
NCUA 2026 Supervisory Priorities: The Delinquency Line
TL;DR A credit union does not get to choose whether delinquency becomes an exam topic this year. NCUA already decided that for every federally insured institution, in writing, at the start of the year. Letter 26-CU-01 names the number, states it plainly, and tells examiners where to look. This piece lays out exactly what the […]
Deposit Flight Risk: Seeing It Before the Quarter Closes
TL;DR A deposit concentration report tells you what left last quarter. It names the relationships that shrank, the share of total deposits they held, and the dollar amount that walked out the door. None of that helps the quarter you are currently in. A flight risk model is built to answer a different question: which […]
Machine Learning in Credit Underwriting: The Four Questions a Demo Won’t Answer
TL;DR: Every machine learning credit underwriting vendor says its model is explainable. Almost none of them have been tested against the moment that claim matters most. An applicant gets denied, your compliance team has to generate the adverse action notice, and the reason on that notice has to be real. Regulation B requires a creditor […]
Choosing a Provider for Data-Driven Underwriting Models
TL;DR A sales demo for an underwriting model is built to look good. The model scores a handful of sample applications correctly, the dashboard looks clean, and the vendor walks through a roadmap slide covering explainability and governance. None of that tells you whether the platform can run a production underwriting decision for a regulated […]
Insurance Analytics and AI: A Practical Map
TL;DR Most guides to insurance analytics and AI organize their content the same way: a chapter on underwriting, a chapter on claims, a chapter on distribution. That structure describes what each use case does. It says almost nothing about what two projects in the same chapter can require of a team, because a model that […]
Data Science in Insurance: Where It Earns Its Keep
TL;DR Ask an insurer’s data science team what they are working on and the list is usually long: fraud models, pricing models, churn models, a dozen experiments in various states of progress. Ask how many of those models are changing a decision today, and the list gets short fast. Data science in insurance earns its […]
Insurance Claims Automation: What AI Handles and What It Escalates
TL;DR Ask an insurance claims leader what “AI in claims” means and most reach for a demo showing a claim settled in minutes. That demo is real, and it also describes a minority of claims. Industry research puts full straight-through processing, a claim handled start to finish with no human touch, at around 7% of […]
Why “AML Platform” Means Something Different to Every Vendor
TL;DR Ask three AML vendors what their “platform” covers and you get three different answers. One vendor means a full compliance suite, sanctions screening, transaction monitoring, case management, and SAR filing, all under one roof. Another means a monitoring or risk-scoring engine built to plug into a suite the institution already runs. A third means […]
