Metrix for Financial ServicesMetrix for Financial Services:NPL, delinquency and NIM in natural language
The board asks for a number and the BI team takes days to put it together. Metrix answers the CRO about small-business delinquency at 9 AM on a Saturday, on WhatsApp, with a real query to the core and the warehouse, a semantic layer that calculates each metric one way only, and row-level security by role.
How do you query a bank's delinquency or NPL in natural language?
You query delinquency, NPL or NIM in natural language by connecting a conversational business intelligence agent to the data stack the bank already has: Metrix translates the CRO's or the CFO's question ("how is small-business delinquency this month?") into a real query against the core banking system and the data warehouse, and returns the figure in seconds on WhatsApp, with the source, period and calculation logic visible. A semantic layer defines each metric once, so risk, finance and sales stop reporting different numbers for the same bank.
It's worth clarifying the term: here, conversational BI means ask your data (business questions in natural language about your own systems), not call analytics in a contact center. Metrix, the BI agent in the Vantegrate Suite, understands English and Spanish, slang included, operates with role-based permissions and column masking for sensitive data, and never makes up a figure: if the question is ambiguous, it asks for clarification before answering.
The full picture of the sector (the five agents applied to banking, fintech, insurance, wealth management and payments) is on the Financial Services page.
The data pain points at banks, fintechs and insurers
We gathered them in dozens of conversations with CFOs, CROs and heads of risk across the five sub-verticals of the financial sector.
The board asks for a number and it takes days
Every time the board asks for a figure, the BI team takes days to put it together. By the time it arrives, the question has already changed or the decision was made without data.
Power BI for three years and almost nobody uses it
Traditional BI adoption stalls at around 25-35% of employees (Gartner 2019; BARC 2022): a good share of the licenses purchased sits underused and everyone else decides by gut feel.
CFO, CRO and sales look at different numbers
Each department calculates NPL, NIM or delinquency its own way, all for the same bank. The meeting starts with an argument over which number is right instead of deciding.
Delinquency gets caught once it's past due
The alert arrives when the loan is already past due, when recovery costs more. And the BCRA Reporting Regime filing (Argentina's central bank) eats up a full week of the BI team's time every month.
How Metrix solves it in the financial sector
The agent's capabilities, mapped to the vertical's pain points: every answer is a real query to your systems, not a generic AI estimate.
No waiting for the analyst
Ask on WhatsApp, in plain language
The CRO types "small-business delinquency this month?" and the answer arrives in seconds, with the detail that backs it up. It understands English and Spanish, slang included, from WhatsApp, Slack, Teams or the web.
A single source of truth
Semantic layer: one definition per metric
NPL, delinquency by cohort, NIM and combined ratio are calculated the same way across the whole organization, with a unified semantic layer (dbt Semantic Layer, Cube, AtScale). The argument over the number is over.
No migration
Connected to the stack you already have
Core banking (Temenos, Finastra, Mambu, Bantotal), Salesforce Financial Services Cloud, warehouses (Snowflake, Databricks, BigQuery, Redshift) and legacy BI (Power BI, Tableau, Qlik, Looker). Metrix sits on top; it doesn't replace anything.
Early delinquency
Proactive alerts before the threshold
NPL out of threshold, sector credit concentration above the limit, top customers on default alert, combined ratio off target: the alert arrives early, not at month-end close.
Compliance
Regulatory reports at close
BCRA Reporting Regime, debtor reporting to the BCRA debtor registry (Central de Deudores), UIF reports (Argentina's financial intelligence unit) and the CNBV, SFC, CMF and SBS formats (the regulators of Mexico, Colombia, Chile and Peru), generated automatically with the exact definitions each authority requires, without the week of manual assembly.
Governance
Row-level security and full auditing
Each user sees only the data their role allows, with column masking for balances, tax IDs (CUIT) and account numbers (CBU), an immutable audit log of every query and SOX-compliant operation for publicly listed entities.
A Saturday at 9 AM with the CRO
The full flow for the sector's most uncomfortable question: the one Monday's report answers too late.
Saturday 8:57 AM
The question comes in on WhatsApp
The CRO of a regional bank types from a phone: "how is small-business delinquency?". No dashboard to open and no waiting until Monday: Metrix answers in seconds.
8:57 AM
A real query to the warehouse, not an estimate
The answer arrives with the figure and what backs it: delinquency in the segment went from 4.2% to 4.8% over the month, with source, period and calculation logic visible. It's a real query against the core and the warehouse.
8:58 AM
Conversational drill-down
The CRO follows up: "break it down by sector". The deterioration is concentrated in retail trade, construction and manufacturing. There's no need to start the query over: the conversation keeps the context.
8:59 AM
Top 5 on alert, with exposure
Asked for the critical accounts, Metrix lists the top 5 small businesses on alert with a probability of default above 65% and the group's total exposure, sorted by risk.
9:02 AM
Handoff with a proposed course of action
The summary goes to the small-business risk manager with a proposed preventive action for each account. Everything is audited: who asked, what they saw and what was handed off.
On Monday at 9 AM, the risk meeting starts with the action plan, not with an argument over which number is right. A picture that used to arrive on Wednesday, put together by hand by BI, was on the CRO's phone on Saturday morning.
The traditional BI gap, by the numbers
Why dashboards fall short and how the conversational channel widens who makes decisions with data.
25-35%
Of employees actively use traditional BI: the rest of the licenses sit underused
Source: Gartner (2019); BARC (2022)
85%
Of companies are already experimenting with AI agents, but only 5% have taken them to production
Source: Cisco, survey of its major enterprise customers (2026)
72%
Median open rate of WhatsApp campaigns per brand: the risk alert gets seen instead of waiting in an inbox
Public benchmarks for the category, not our own results: every Metrix implementation is measured against a baseline agreed with the customer (query adoption by role, time to answer, metric coverage in the semantic layer).
Secure AI agents that work with your data
Metrix queries balances, delinquency and credit exposure: data no regulator lets circulate lightly. That's why it runs 100% on Salesforce and Oracle Cloud Infrastructure, with those platforms' SOC 2 Type II and ISO 27001 certifications, row-level security, column masking and an immutable audit log of every query. We don't claim anyone else's certifications: Vantegrate enables, the customer operates and certifies.
Frequently asked questions about Metrix for Financial Services
Does Metrix answer NPL, delinquency, NIM and BCRA Reporting Regime questions in natural language?
Does Metrix answer NPL, delinquency, NIM and BCRA Reporting Regime questions in natural language?
Yes. It connects on top of the existing stack (core banking such as Temenos, Finastra, Mambu or Bantotal, warehouses such as Snowflake or Databricks, financial CRM and risk engines) and answers NPL, delinquency by cohort, NIM, combined ratio and AUM with conversational drill-down: "small-business delinquency this month", "break it down by sector", "top 5 on alert". Regulatory reports (the BCRA Reporting Regime, debtor reporting to the Central de Deudores, UIF and the CNBV, SFC, CMF and SBS formats) are generated automatically at close with each authority's exact definitions.
What is a semantic layer and why does it keep two departments from reporting different numbers?
What is a semantic layer and why does it keep two departments from reporting different numbers?
It's the layer where each metric is defined once for the whole organization: what counts as delinquency, which portfolio NPL is calculated on, which period closes NIM. Without it, the CFO, the CRO and sales calculate the same indicator with different criteria and the meeting goes into arguing about which one is right. Metrix builds that layer on top of your sources (compatible with dbt Semantic Layer, Cube and AtScale) and every answer comes from there: a single source of truth, on any channel.
How accurate is AI when answering about financial data? Can it make up a figure?
How accurate is AI when answering about financial data? Can it make up a figure?
Metrix doesn't generate the figure with a language model: it translates the question into a real query that runs against your database, and returns the result with the source, period and calculation logic visible. If the question is ambiguous ("delinquency for which portfolio?"), it asks for clarification before answering. That's the difference from a generic AI like ChatGPT, which has no access to your data and can hallucinate a number when it doesn't know it.
How is it different from the Power BI, Tableau or ThoughtSpot we already have?
How is it different from the Power BI, Tableau or ThoughtSpot we already have?
It doesn't replace them: it connects on top. Dashboards are still useful for structured, pre-built analysis; Metrix solves the conversational last mile, for the executives and middle managers who never open a dashboard (traditional BI adoption sits around 25-35% of employees, according to Gartner 2019 and BARC 2022) and ask on WhatsApp in their own language. The typical implementation takes 6 to 10 weeks, versus 6 to 12 months for a traditional BI project. The permissions and security model is on our security page.
The board's next question gets answered in seconds
Tell us which numbers your board asks for and how long your team takes to put them together, and we'll show you how Metrix would answer them on top of your own stack. A 30-minute conversation, no commitment.
Francisco Morales, co-founder, takes your call. We reply on WhatsApp within 4 business hours, no strings attached.
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