CFO / VP of Finance — the pricing decision-maker

TechCU Rates Peer-set rate position, normalized weekly TechCU Rates exists to replace a recurring manual chore — opening competitor websites one by one, eyeballing posted rates, noting them in a spreadsheet, and repeating whenever someone remembers to — with a maintained, automated, and defensible competitive pricing record. The publication succeeds when the CFO walks into Monday's pricing committee having already read it, the Deposit Manager knows who repriced certificates this week, the Lending VP can answer whether a competitor's advertised auto rate is real, and Marketing has enough lead time on competitor promotions to respond before the window closes. If the report stops arriving, the institution reverts to partial coverage, irregular cadence, and pricing decisions made on stale or structurally misleading data. The mechanism is simple: each issue delivers the competitive picture that would otherwise take hours of manual collection and still come back incomplete. Deposit rates arrive weekly because CD and share certificate pricing is time-compressed — maturity waves don't wait. Loan rates arrive on a slower cadence because auto, mortgage, and HELOC rates move less frequently and the normalization work (adjusting for tier thresholds, fee absorption, LTV conditions) benefits from verification time. Both sides cover the full defined peer set — the 5–10 institutions actually competing for TechCU's members in the same markets — not national averages or asset-size cohorts from call reports. What makes this publication distinct is the normalization layer and the data-integrity discipline behind it. A competitor's posted "4.49%" on a 48-month auto loan may be 4.80% effective once tier thresholds, origination conditions, and relationship discounts are accounted for. TechCU Rates converts posted rates into effective rates so comparisons are real, flags values that require human verification rather than asserting false precision, and builds the week-over-week trend record that turns a snapshot into a defensible pricing narrative. Readers can count on: complete peer-set coverage every issue, normalized rates they can compare without mental adjustment, position expressed as variance against the competitive set, movement flagged with macro context, and honest confidence labels on every data point. Decision-grade competitive rate intelligence for a single credit union's pricing committee. Each issue is the document the CFO reads before the rate-setting meeting — not a research report, not a dashboard, not a data feed. The publication eliminates the manual rate shop, documents the competitive record over time, and provides the position-and-trend view that makes pricing decisions defensible rather than reactive. Competitive deposit and loan rate positioning against a defined local peer set. The core domain is: where does TechCU stand on each monitored product relative to the specific institutions competing for the same members, expressed in normalized effective rates with week-over-week and trend-line context. Products include share certificates and CDs (by term), money market accounts, auto loans (new and used, by term), mortgages (by type and LTV band), and HELOCs. The analytical lens is always comparative position — raw rates are inputs, not outputs. Macro rate context (Fed moves, treasury yields, SOFR shifts) appears only when it explains a competitor's repricing behavior or changes the interpretation of TechCU's position. - National rate averages or asset-size peer cohorts as primary comparisons — these are directional checks only, never the basis of a positioning statement - Financial performance benchmarking (ROA, efficiency ratio, net worth ratio) — this is a rate publication, not a call-report analysis tool - Fee benchmarking beyond what affects effective rate calculation — standalone fee comparisons (NSF fees, ATM surcharges, account maintenance fees) are out of scope - Member-level attrition analysis or internal deposit flow data — requires private data integration not currently available - Strategic or organizational commentary about competitor institutions — the publication reports rates and rate behavior, not competitor strategy narratives - Marketing copy, brand positioning, or member-facing language — this is an internal operational document - Regulatory compliance guidance or examination preparation content CFO / VP of Finance — the pricing decision-maker Owns the rate sheet and chairs the Monday pricing committee. She is either the person who did the manual rate shop last Friday or the person relying on whoever remembered to do it. She reads across all product categories — deposits and loans — because her job is the institution's overall competitive pricing posture, not a single product line. She cross-references rate position against macro movements and competitor repricing patterns to build and defend pricing strategy over time. She is an operator, not a content consumer. She has no patience for narrative analysis, marketing language, or methodology explanation. She trusts tables, computed statistics, and flagged anomalies. If a number appears, she expects to know how it was collected, when, and whether it's assertion-grade or verification-grade. One bad number — a promotional rate treated as a standing rate, or a stale value presented as current — will destroy trust in the entire report. She will not search for this content; it must arrive on schedule and be immediately scannable. A report that requires more than 5 minutes to extract the pricing-relevant signal has failed. She also uses the accumulated time series — the trend of TechCU's position against peers across weeks and months — to formulate and defend pricing strategies to the board. If the gap on 48-month used auto has been widening for six weeks and three peers repriced in response to the same Fed move, that pattern is what she presents. The historical record is as valuable as the current snapshot. Walks into Monday's pricing committee with the competitive picture already read — position by product, who moved, by how much, and whether TechCU is above, at, or below the peer-set midpoint. Can make a reprice-or-hold decision with confidence, and can defend that decision to the board six months later with a timestamped competitive record. Deposit Manager / VP of Lending / Marketing — secondary functional readers Three roles that each read one bounded section rather than the full report. The Deposit Manager monitors certificate and MMA pricing weekly, watching for competitor repricing that could trigger deposit outflows — especially during CD maturity waves when members are actively comparison-shopping. The VP of Lending needs to know whether a competitor's advertised auto or mortgage rate is real or a headline, and how far off TechCU is on the products generating member objections. Marketing needs lead time on competitor promotional activity — CD specials, auto loan campaigns, HELOC introductory offers — to respond before the promotional window closes. All three are time-constrained operators who will read only their section. They trust data presented cleanly in tables over prose explanation. They need anomaly flags and verification indicators just as much as the CFO — the Deposit Manager making a retention decision on a competitor's CD rate needs to know whether that rate is confirmed or needs a phone call to verify. None of these readers will tolerate content that reads like vendor marketing or industry thought leadership; they expect an internal operational document that respects their time. Each reader extracts their product-specific competitive signal in under two minutes: the Deposit Manager sees who repriced certificates and whether TechCU's renewal rate is competitive before the maturity wave hits; the Lending VP has a factual answer when loan officers report members leaving for a competitor's rate; Marketing spots competitor promotions early enough to mount a response. Data-first throughout. At least 80% of every article should be structured data: tables, computed statistics, rate comparisons, deltas, and formulas with visible justification. Where prose appears, it takes the form of executive bullet points — never paragraphs of analysis. Write as an internal operational document, not a publication addressing an external audience. No second-person address to the reader. No introductions, scene-setting, or concluding summaries. Every sentence that isn't a data label or a flag should state a fact, a computed result, or a recommended action. Institutional, precise, flat. The register is that of a finance team's internal memo — technically specific, emotionally neutral, and confident only where the data warrants confidence. No enthusiasm, no urgency language, no persuasion. When a data point requires human verification, state it flatly: "Verify — posted rate may reflect promotional terms." When a position gap is significant, state the gap numerically without characterizing it as alarming or concerning. The publication has no advocacy position. It does not recommend that TechCU match, beat, or ignore competitor rates. It presents position, movement, and trend. Where a repricing decision is implied by the data, frame it as a question for the committee ("48-month used auto: TechCU at 5.24% effective vs. peer median 4.89% — 35bps above, widening 12bps over 4 weeks") rather than a recommendation. The report serves the decision-maker; it does not make the decision. Use credit union and financial terminology without definition: APR, APY, effective rate, basis points (bps), NIM, cost of funds, LTV, SOFR, term structure, tier threshold, share certificate. These are the reader's working vocabulary. Do not simplify for a general audience — there is no general audience. Avoid vendor or marketing jargon: no "actionable insights," "competitive intelligence," "market dynamics," or "strategic positioning" as filler phrases. Express all rates to two decimal places (e.g., 4.49%, not 4.5%). Express rate differences in basis points (e.g., +35bps, -12bps). Express week-over-week changes with direction and magnitude (e.g., ↑15bps, ↓8bps, unchanged). Use "effective rate" to denote the normalized comparison rate and "posted rate" for the raw collected value. Flag data confidence with inline markers: [Confirmed] for values collected from public disclosure and passing QA; [Verify] for values that may reflect promotional terms, stale pages, or ambiguous tier conditions. Dates should be explicit (e.g., "collected 2025-05-23") — never "recent" or "current." Every rate value must carry a collection source (institution website, NCUA publication, or other public disclosure) and a collection date. Effective rate calculations must show the adjustment applied and the assumption behind it (e.g., "Posted 4.49% adjusted to 4.82% effective assuming Tier 2 credit, no relationship discount, 80% LTV"). Macro context (Fed rate, treasury yields) should reference the specific data point and date, not general characterizations like "the current rate environment." When a competitor's rate cannot be verified from public sources, say so explicitly rather than omitting it or presenting an uncertain value as confirmed. The value of this publication is inseparable from its timeliness. A rate comparison collected Thursday and delivered Monday morning is useful. The same comparison delivered Wednesday of the following week is stale. Every issue must state the collection window (e.g., "Rates collected 2025-05-19 through 2025-05-22"). When a specific institution's rate page has not been updated since the prior collection window, flag the staleness rather than carrying the old value forward as current. Deposit-side content runs weekly; loan-side content runs on a slower cadence because loan rates move less frequently and normalization requires additional verification time. Primary sources are competitor institution websites (rate pages, product disclosures, promotional landing pages) and NCUA/FFIEC published data. Secondary sources are Federal Reserve releases, Treasury yield data, and S&P Global/RateWatch published benchmarks where publicly accessible. All rate data must be traceable to a specific URL and collection timestamp. NCUA quarterly data is useful for trend context and certificate concentration metrics but is too lagged and too aggregated for weekly competitive positioning — use it as a validation layer, not a primary comparison. Industry research from Callahan & Associates, Cornerstone Advisors, or similar firms is acceptable for structural context (e.g., deposit concentration trends, benchmarking methodology) but never as a substitute for direct rate observation. - Competitor "as low as" rates treated as standard rates — these are top-tier, best-case values that most members will not receive. Always note the tier assumption. - Promotional or introductory rates mixed into standing rate comparisons — a 12-month CD special at 5.10% APY is not comparable to a standing 12-month CD rate at 4.25% APY. Flag promotional rates separately. - Stale rate pages presented as current — competitor websites do not always update rates promptly or display a "last updated" date. If the page content matches the prior collection exactly and there is reason to believe rates may have changed (e.g., a Fed move occurred), flag as potentially stale. - National average data used as a local competitive proxy — NCUA publishes national averages for 23 products quarterly. These blend thousands of institutions across all markets and asset sizes. They are directionally useful and structurally misleading for local competitive decisions. - APR/APY confusion in cross-product or cross-institution comparisons — loan rates are typically expressed as APR (which includes fees); deposit rates as APY (which reflects compounding). Mixing these without conversion produces false comparisons. - Relationship discounts and conditional pricing invisible in public disclosures — some institutions discount rates by 25-50bps for direct deposit, minimum balances, or bundled products. These conditions may appear only in footnotes or not at all on the rate page. When suspected but unconfirmed, note the uncertainty.

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