Software Zechim builds for financial services

Financial services has a specific problem with AI: regulatory risk is high. A mistake isn't a fun bug to write about in a blog post - it's a fine, a Central Bank flag, a customer indemnified. That's why many fintechs and finance teams in larger companies stopped at "let's wait and see".

Here are the five kinds of software Zechim delivers well in Brazilian financial services. All of them were designed to pass through internal compliance's sieve, not to dazzle in a demo.

1. Bank-reconciliation agent

Bank statement arrives in CSV, PDF, or API. You have a spreadsheet or ERP with what should have come in. Someone on the team cross-references the two, marks matches, flags what's left, investigates. Every day. Every week. Every month.

The agent does the matching automatically using rules (amount, date, description, expected counterparty). What matches gets marked. What doesn't generates an investigation queue with context: "this incoming entry of R$ 12,500 from CNPJ X is likely the payment for invoice Y from 15 days ago, but the amount is R$ 300 above the expected value - possibly interest or penalty. Confirm?".

It doesn't replace the accounting team. It replaces the 4 hours per day the accounting team spends doing it by hand.

2. Conversational anti-fraud agent

A transaction triggered a risk rule. Today, it goes into a queue, someone reviews it in 30 minutes, the customer is already anxious. The agent talks to the customer on WhatsApp: "we noticed a R$ 800 purchase at a hardware store outside your pattern, do you recognize it?". Customer replies yes or no. Based on the answer + the base risk, the agent approves or escalates to human analysis.

It reduces decision time from hours to minutes. And, critically, reduces false positives (customer angry about a blocked legitimate purchase) because the customer participates in the loop.

3. Customer service with real context

Customer calls or opens chat. Today, the agent opens 4 systems, builds context in their head, replies. The AI agent walks in with the context already assembled: customer history, latest transactions, latest tickets, products they hold, open disputes. If the question is standard (balance request, limit change, refund question), the agent answers directly. If it involves judgment or empathy, the human agent gets the conversation with pre-assembled context.

Reduces average handling time by 40 percent. Reduces response error to zero in consultative cases (the agent reads the real data).

4. Internal compliance auditing agent

Central Bank circular got updated. New KYC rule. New COAF report. The agent reads the change, cross-references with your documented internal processes, generates a checklist of what needs adjusting. It doesn't decide for you - it lists so the compliance team can decide. But it never lets a regulatory change slip because nobody read the email.

Bonus: keeps a history of "when this control was updated, by whom, in response to which regulatory change". External auditors love it.

5. Regulatory report generator

Central Bank, COAF, CVM. Every month, someone in the regulatory team generates a report from raw data, formats it in the specific layout, validates, submits. The agent builds the draft from the same data. Regulatory team reviews and submits instead of building from scratch. Reduces human error, frees time for the part that requires judgment (interpretation, response to a formal request).

Important caveat: the agent does not submit alone. Always human in the loop for anything that leaves the company for a regulator.

The common pattern

In financial services, all five have extra constraints:

1. Auditable log of everything - every agent decision is recorded with input prompt, output, model version, timestamp
2. Human in the loop for any decision involving customer money - agent suggests, human approves
3. LGPD and banking secrecy respected by design - sensitive data doesn't leave your environment without explicit consent
4. "No-op" mode for testing - before going to production, the agent runs in observation mode for weeks generating what it would do, but without executing
5. Rollback and shutdown in seconds - if something got out of control, you shut it down with an API call

What we don't do

• We don't replace core banking. We integrate.
• We don't sell generic "AI risk scoring". That's a sub-problem solved by specialists with data larger than yours.
• We don't give regulatory guarantees. We work alongside your internal compliance.

If you run financial operations in Brazil and one of these scenarios hits your day-to-day, worth 30 minutes for us to understand the fit and the risk design.