Banking & Insurance
AI Solution · AI Analyst for Banking and InsuranceBanking & Insurance

The analyst that doesn't just show the data, it explains why the approval rate dropped

AI Analyst connects your CRM, your banking or policy core system, and your scoring systems, and automatically detects anomalies in application volume, approval, or claims. It explains in plain language what happened and what action to take.

Week 3
decisions based on data
1 analyst
saved on the team
2-4 wks
implementation
130+
connectable data sources
The Challenge · Why risk reports arrive late

The approval rate drops and no one knows if it's the scoring, the channel, or the customer segment

Between the CRM, the core system, and the scoring systems, a variation in approval or claims can have several causes that no one cross-references in time.

📉

Approval variations that go unnoticed

A drop in a product's approval rate is detected weeks later, once it has already affected business volume.

🧩

Data split across systems

CRM, banking or policy core, and scoring live in separate systems that no one cross-references in time.

The report doesn't say why

The risk dashboard shows that claims rose, but not whether it's a segment, a channel, or a scoring change.

The Solution

From scattered risk data to the root cause, in plain language

Connects your financial operation's key sources and turns numbers into explanations and actions.

🔗

Connection to your financial stack

Integrates with your CRM, your banking or policy core system, and your scoring systems.

🚦

Automatic anomaly detection

Identifies variations in applications, approval, or claims against normal behavior.

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Natural language explanation

Translates the anomaly into a clear explanation: which product, which channel, and which segment is causing it.

Action recommendation

Proposes reviewing the scoring criteria or the originating channel, instead of leaving the interpretation to the risk team.

How It Works · The value, step by step

From the risk variation to the why, in minutes

The solution continuously monitors CRM, core, and scoring, and only interrupts when it detects something relevant.

CRM, core, scoringContinuous ingestionDetect anomalyExplain causeRecommend actionNotify team
1
Connect the priority sourcesStart with 1-2 key sources: the CRM or the main product's scoring system.
2
Establish the baselineAnalyzes at least 90 days of history of applications, approval, and claims by segment.
3
Detects the anomalyIdentifies when a product, channel, or segment deviates significantly from that normal behavior.
4
Explains the cause in plain languageCross-references CRM, core, and scoring to identify which factor explains the detected variation.
5
Notifies with the suggested actionSends the risk team the explanation and a concrete recommendation on criteria or channel.
Integration

Connects to your current financial stack

It doesn't replace your core or your risk engine: it connects to them and adds the missing layer of interpretation.

Input

CRM, core, and scoring

CRM, banking or policy core, and scoring systems, depending on the institution's priority sources.

Grovium Solution

AI Analyst

Detects anomalies in applications, approval, or claims and explains them in plain language with a suggested action.

Output

Notification and spreadsheet

Explanations sent to the risk team and exported to Google Sheets for committee tracking.

The risk team validates the recommendation before acting; the solution provides the explanation, not the final decision.

Use Cases · The Business Value

Where the impact is most noticeable

Consumer credit lenders

Explains why a specific product's approval rate dropped, cross-referencing channel and scoring criteria.

Auto and home insurers

Detects which segment or region is behind a claims spike before it affects the technical result.

Risk committees

Provides recurring context on portfolio variations with no need for a dedicated analyst per committee.

Problem → Solution

A lender that didn't know why its approval rate had dropped

A real example of the kind of situation this solution solves.

Before · the problem

The risk dashboard showed a 15% drop in the approval rate for a credit product, with the team unsure whether it was the scoring or the originating channel.

The risk team manually reviewed applications for days without finding a clear explanation, while business volume kept dropping.

After · with the solution

AI Analyst detected the anomaly the first week and explained that a specific origination channel had changed the risk profile of its leads.

The team adjusted that channel's criteria that same week, with a clear, actionable explanation instead of a panel that only showed the symptom.

Stop staring at your risk panel with no answers

We'll show you with your own data how AI Analyst detects and explains approval and claims anomalies from the first week.