Most banking data dies in a warehouse. Data Intelligence unifies your core, channel, and fintech data into a relationship‑level view — then activates it where decisions actually get made: at the teller line, in the CRM, in the next campaign, in the next product offer.
A decade of data warehouse projects. Lake houses. Customer 360 initiatives. The dashboards got built. The reports got generated. The cross-sell numbers did not move. The attrition numbers did not move. Somewhere between the warehouse and the front line, the signal evaporated.
The reason is not a data quality problem. It's an activation problem. The warehouse was the destination. It needed to be a relay. Insight that does not reach the teller mid-conversation, the marketer mid-campaign, or the CRM mid-call is insight that doesn't exist for anybody who can act on it.
Data Intelligence inverts the architecture. We unify your core, channel, and fintech data into a relationship-level view — household-resolved, real-time, ready to be queried. Then we push the signal back out: next-best-action in the teller UI, dynamic segments in the marketing platform, prioritized lists in the CRM. The model is closed-loop. Outcomes get measured against actions. Actions evolve against the data.
The models are pre-trained on banking data, not generic e-commerce signal — attrition, propensity to product, lifecycle stage, channel preference, deposit migration. Common Cents benchmarks let you compare yourself against 1,000+ peer institutions and find the gap worth closing first.
“If the signal doesn't reach the conversation, the signal doesn't exist.”
K‑Insight ingests across the core, the channels, and the fintech stack — resolves identity at the household level — and pushes the signal into the surfaces your teams already use. Propensity models, lifecycle triggers, attrition risk, deposit migration, lending opportunity — every one tied to a person, a workflow, and a measurable outcome.
Identity resolution at the household level across cores, fintech systems, and channels — so the model is looking at a person, not a row.
Pre‑trained models for the moments that matter — deposit attrition, lending intent, life‑stage transition, channel preference — calibrated on banking data, not generic e‑commerce signal.
The signal lands where the conversation happens — next‑best‑action in the teller UI, dynamic segments in marketing automation, prioritized lists in the CRM.
Anonymized network benchmarks across 1,000+ institutions. Compare deposit velocity, product mix, channel adoption — and find the gap that's worth closing first.
We do not run a months-long discovery before you see value. The first models go live within weeks of data connection.
K‑Connect wires K‑Insight into your core, your channels, your fintech stack, and any internal systems. Real-time and batch, both supported.
Household-level resolution across every system. The model stops looking at rows and starts looking at relationships.
Pre-trained models for attrition, propensity, and lifecycle get tuned on your data. Calibration is measured against your actual outcomes, not generic benchmarks.
Next-best-action lands in the teller UI. Dynamic segments sync to your marketing platform. Prioritized lists hit the CRM and contact center.
Every signal-to-action-to-outcome path is tracked. Lift is measured against control. The dashboard tells you what to invest in next.
Common Cents shows you where you sit against 1,000+ peer institutions on the metrics that matter — and where the gap is worth closing.
Rate-shopping members drain deposits to a digital-first competitor. By the time the report runs, the money is gone.
Attrition model flags at-risk relationships 30+ days before the move. Next-best-action lands in the teller UI and the contact center queue.
Save rate up 4–7x. Hundreds of millions in retained deposits, attributed.
Marketing blasts the whole base with a HELOC offer. Open rate is fine, conversion is dismal, the offer fatigues the audience.
Propensity model scores every household. Only the high-intent segment gets the campaign. Low-intent gets a different message.
Conversion up 27% on a smaller sent volume. Audience fatigue down.
A member just had a baby. The institution finds out three statements later, after the competitor opened the 529.
Lifecycle triggers fire in real time — new account types, transaction patterns, channel shifts. The MSR sees the moment before the conversation.
Share-of-wallet at the lifecycle moment. The relationship deepens at the point it should.
“We set a goal of at least 80% member retention after conversion. Ninety days in, we're at 96%.”
No. K‑Insight ingests directly from your core, channels, and fintech systems via K‑Connect. If you already have a warehouse, we can read from it — but we don't require one.
