Using AI to Improve Products in Financial Projects | Generated by AI

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Based on your experience working on the Finance Transformation Platform, where you’re handling backend systems for financial data processing, workflows, and integrations with tools like Java, Spring Boot, and Db2, AI can indeed play a transformative role. Your idea of using AI to “search financial headers” aligns well with natural language processing (NLP) applications, like converting user queries into SQL for efficient data retrieval. This can democratize access to complex financial datasets, allowing non-technical stakeholders (e.g., finance teams) to query things like ledger entries, transaction headers, or approval statuses without writing code. Your example of generating SQL from natural language is a perfect starting point—let’s break it down and expand on broader applications.

Analyzing Your SQL Generation Example

Your natural language query (“get some users whose firstname is andy, created_at around the day last month, who are 20 years old as of 2025, and whose last_login like recent week”) is a solid demonstration of how AI can bridge everyday language to database operations. The generated SQL query you provided is mostly effective and leverages PostgreSQL features well:

SELECT *
FROM users
WHERE first_name ILIKE 'andy'
  AND CAST(created_at AS DATE) BETWEEN 
      (CURRENT_DATE - INTERVAL '1 MONTH' - INTERVAL '1 DAY') 
      AND 
      (CURRENT_DATE - INTERVAL '1 MONTH' + INTERVAL '1 DAY')
  AND EXTRACT(YEAR FROM AGE(date_of_birth)) = 20
  AND last_login >= CURRENT_TIMESTAMP - INTERVAL '7 DAYS';

AI tools like Copilot (which you mentioned using heavily) or advanced models (e.g., via APIs from OpenAI or Google Cloud) excel at this NL-to-SQL translation. In your setup, integrate it into workflows by building a chatbot interface that parses queries about financial headers (e.g., “Show unapproved headers from last quarter with balances over $10K”) and generates/executes SQL safely, with guardrails for security.

Broader Ways to Use AI in Financial Backend Systems

In projects like yours—focusing on data import/validation/export, workflows, and banking systems—AI can boost efficiency, reduce errors, and enable innovation. Drawing from industry trends, here are practical applications tailored to backend engineering:

These approaches can lead to cost savings (e.g., 20–30% productivity gains in software engineering) and better products, but start small: Pilot NL-to-SQL in a sandbox, ensure data privacy (critical in banking), and measure ROI through metrics like reduced deployment time.

If you’re building that AI agent, consider open-source frameworks like LangChain for chaining log analysis with SQL generation. For pricing on APIs or subscriptions, check official sites as details vary.

References

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