FAQs
BlastPoint is a customer intelligence platform built for credit unions and utilities that turns your member data into predictive models and actionable segments. It is not a generative AI tool and not a large language model. Think less ChatGPT, more cancer-detection AI: purpose-built models trained on real behavioral data to surface what is shifting before it becomes your problem.
Unlike generic AI tools, BlastPoint handles the full data stack: ingestion, identity resolution, feature engineering, model validation, and production scoring. Your team gets answers without needing a data science department to run the system.
No. Segmentation is one output. BlastPoint is a full data infrastructure layer that handles everything from ingestion to production scoring.
Teams use it for member retention, loan offer timing, product cross-sell, and risk identification, all from a single governed platform. It is the complete system a credit union would otherwise have to build and staff internally.
Four things separate BlastPoint from most AI vendors in financial services: US-only data storage with a written guarantee, no use of your data to train other customers’ models, fixed contract pricing with no per-token cost, and a live system within 30 days.
Most AI vendors offer some of these. Few offer all four in writing. For a financial institution, the contractual guarantees matter as much as the features.
Every model is built specifically on your data and aligned to your objectives. The same model is not sold to your competitors.
Each customer’s environment is single-tenant and isolated. That is architecture, not a configuration toggle that could be changed later.
No. BlastPoint uses predictive models built on structured data, closer to what a credit bureau does than what OpenAI does. Generative AI never touches raw customer or member data and never makes decisions about the people in your book.
Generative AI may be used in limited internal ways, such as summarizing results. Your governance committee should evaluate predictive analytics and generative AI separately, as they carry different risk profiles and different regulatory implications.
Yes. Every BlastPoint employee is US-based, every server is US-based, and data does not cross US borders in transit, storage, or processing. This is a contractual commitment, not a policy statement.
This matters for NCUA examiners, who are increasingly asking institutions to document exactly where data lives and who can access it. A policy statement does not satisfy that question. A contract clause does.
No. Your raw data is never used to train models that serve other organizations. The master agreement contains an explicit contractual prohibition, not just a policy.
The prohibition covers training, fine-tuning, and enhancing any AI system outside the direct scope of services to your organization. Ask any AI vendor you evaluate to put this in writing. If they will not, that is the signal.
It is destroyed immediately. BlastPoint’s platforms never hold social security numbers, payment card information, or driver’s license numbers. Accidental transmissions are identified and destroyed during onboarding as a hard pipeline stop, not an incident response procedure after the fact.
This distinction matters operationally. An incident response procedure means the data was stored, even briefly. A pipeline stop means it never enters the system.
No. Your data is not sold and not shared beyond the enrichment sources named in your contract. All third-party data sources are covered under one BlastPoint contract, with no separate vendor relationships for you to manage.
This is worth confirming in writing with any AI vendor. Secondary use and data sharing clauses are often buried in terms of service rather than the main agreement.
Yes. The platform produces immutable, exportable audit logs of decisions and access, and generates specific adverse-action reason codes required under ECOA and FCRA.
If your examiner asks why a member received a particular offer or was excluded from a campaign, BlastPoint can show the documented process. That documentation is what separates a defensible AI deployment from a liability.
Yes. Models are tested for bias across protected classes and proxies, both at build and in production, with documented before-and-after results.
For any financial institution using AI in credit, pricing, or eligibility decisions, this is a Tier 1 compliance requirement under NCUA AI guidance. The testing needs to happen at build and again after every model update.
Yes. BlastPoint maintains SOC 2 compliance and follows security practices designed for highly regulated industries.
If your governance committee requires independent security certification rather than a self-assessment, BlastPoint can provide documentation. A vendor that offers only a self-assessment is not the same as one with a third-party audit on record.
No API required. You send a CSV export from your core system over an encrypted SFTP connection. No middleware to build, no integration project to manage.
BlastPoint’s team handles everything after the transfer. The first step is an audit of what you have, where it lives, and what is needed to reach your specific objectives.
No. BlastPoint’s pipelines were built for the kind of fragmented, siloed data that financial institutions actually run on: duplicate records, inconsistent address formats, naming conventions that differ across systems, and missing fields.
Data does not need to be prepared or pre-cleaned before transfer. The objective you are trying to reach drives every cleanup and enrichment decision that follows.
Six to eight weeks from first data transfer to production-ready models. First insight for simpler use cases arrives closer to 30 days.
The standard path breaks into four stages: alignment and secure transfer (weeks 1 to 2), enrichment and QA (weeks 2 to 4), model development and validation (weeks 5 to 6), and platform delivery with full team access (weeks 6 to 8).
Above 80%. When BlastPoint matches outside data to your member records, the match rate exceeds 80%. The industry average for this kind of enrichment is around 40%.
That gap matters because a higher match rate means more of your members get a complete propensity score, and fewer fall into a default or untargeted bucket.
No separate vendor relationships required. All third-party enrichment sources are covered under one BlastPoint contract.
BlastPoint maintains proprietary behavioral data covering 25% of US households and businesses, built over more than ten years, with more than 100 unique attributes unavailable elsewhere. That data fills gaps your internal records cannot.
A minimum credible internal AI platform requires five concurrent technical roles and runs $700K to $1.1M per year in direct labor alone. Infrastructure, data licensing, security tooling, monitoring, legal, and pipeline rebuilds commonly add another $1.2M or more.
Most build estimates price the first demo and miss the ten-year obligation. Every production model creates permanent obligations around retraining, monitoring, governance documentation, and exam readiness that require dedicated headcount indefinitely.
An internal team building a production-ready, exam-defensible AI platform realistically takes 12 to 24 months or more. BlastPoint is live with your data in 30 days.
The months spent building are months a competitor using an existing platform is running campaigns against your members. Time-to-production is not just a convenience question. It is a competitive exposure question.
The model becomes a liability. Without the original builder, you have a system that scores but cannot be defended, retrained, or explained to an examiner.
A custom internal model still drifts when the economy changes and still needs retraining. It still runs on member data and still needs encryption, access controls, and backups. It can still produce a biased or unexplainable output. None of those obligations disappear when the builder does.
Probably yes. A data scientist can build a model. Maintaining the surrounding production system is a different and much larger problem.
What typically cannot be built and maintained by one person is the full stack: data ingestion, identity resolution, historical dataset management, feature engineering, monitoring, retraining pipelines, governance documentation, and exam-ready auditability. BlastPoint provides that infrastructure so your data scientist can focus on analysis.
Yes. BlastPoint provides the data infrastructure, not just the models. If you can supply your transaction data, BlastPoint handles ingestion, cleaning, identity resolution, and enrichment.
You do not need a pre-built data lake or a mature data team. Most credit union customers go from raw data to first insight in 30 days or fewer.
An AI committee is an internal governance body that evaluates new AI tools before adoption, covering data risk, compliance exposure, vendor viability, and operational fit. NCUA examiners are increasingly asking institutions what process they used to approve AI systems and who signed off.
BlastPoint provides a playbook for standing up this committee, including how to tier software by risk level and which evaluation domains matter most for financial institutions. The committee structure protects the institution and the individual who approved the purchase.
Five questions that separate strong answers from weak ones: Can the vendor produce specific adverse-action reason codes and evidence of bias testing? What identifiable data leaves your environment, and can any of it be re-identified? Is your raw data ever used to train models for other customers, and is that a contractual no? Is there meaningful human oversight for consequential decisions? Is there a current independent security certification, not just a self-assessment?
Good vendors welcome these questions. Evasive answers are the signal.
BlastPoint typically delivers first actionable member intelligence within 30 days. Because pricing is fixed with no per-token cost, the ROI math is predictable from contract day one.
Clients have used the platform to increase campaign conversion rates, identify at-risk members before attrition, and surface loan product opportunities not visible in standard reporting. The fixed pricing model means utilization does not create unexpected cost overruns.
A competitor running daily member intelligence does not wait for your next planning cycle. They send the offer before your member searches. Beyond competitive exposure, the regulatory risk compounds over time.
An AI tool adopted without documented evaluation, bias testing, or audit trails puts the approving executive personally accountable when an examiner asks. The risks of moving slowly are less visible than the risks of moving fast, but they are just as real.
AI adoption in credit unions has crossed from optional to examined. NCUA examiners are asking institutions what AI tools they use, how they were evaluated, and what governance exists.
The institutions that have this infrastructure now are pulling members from those that do not. BlastPoint’s 30-day implementation means the cost of waiting is measured in missed campaigns and lost members, not just future effort.
Schedule a demo and discover how BlastPoint can help you achieve your organization’s specific operational and customer goals.
