Bringing AI Analytics to Financial Services with MindsDB

Organizations like Fintechs and banks need powerful tools to transform vast, siloed data into strategic decision-making assets. That’s where MindsDB steps in—delivering AI analytics that unify diverse data sources and turn them into actionable intelligence for banking, fintech, and investment firms.

Risks and inefficiencies faced without effective AI implementation

Delays in risk and return modeling can hinder portfolio managers, as decisions are only as timely as the data being surfaced—potentially exposing vulnerabilities in credit or market dynamics. Relying on manual analysis increases the risk of human error, especially during data compilation and interpretation, even when human oversight remains essential for final decision-making. 

Additionally, financial services often struggle to provide personalized experiences due to fragmented KYC data across business units like credit, home loans, and retirement planning. These data silos prevent institutions from serving clients as a unified provider, leading to missed opportunities to offer relevant financial products and support customers in achieving their goals.

Often, analysts spend more time extracting and cleaning data than generating insights—hindering efficiency and business impact. This results in facing many difficulties. Let’s explore a few of these problems and the solution MindsDB offers.

Problem 1: Fragmented & Siloed Data: 

• Financial institutions house a mix of structured (transactional, account data) and unstructured data (KYC documents, emails, call transcripts). This fragmentation creates:

  • Slow, manual analytics processes

  • Risk of compliance gaps and fraud

  • Limited 360° customer insights

• Data is scattered across CRMs, core banking systems, trading platforms, and spreadsheets

• Hard to unify or access data for analytics or reporting

• Slows decision-making and increases manual effort

Solution: MindsDB is a powerful federated query engine designed to unify both structured and unstructured data across your organization. By connecting directly to diverse data sources—from databases, data warehouses, and applications up to documents, files, and webpages which can be unified in a Knowledge Base—MindsDB allows you to query and analyze everything in one place. This unified data can then be seamlessly passed to large language models (LLMs) and AI agents, enabling context-rich, intelligent applications without the need for complex data pipelines or replication.

Problem 2:  Legacy Infrastructure & Low AI Adoption:

• Outdated systems that aren’t AI-ready

• Difficulty in deploying machine learning models into production

• Long lead times for building or integrating modern data pipelines

Solution: With MindsDB, users can easily connect their data to AI without the need for complex transformations or data movement—whether it's in a database, cloud storage, or file system—eliminating the need for replication or restructuring. It also supports a wide range of AI models for building agents, making it simple to operationalize AI across diverse use cases. You can explore the supported models here: MindsDB Agent Models.

Problem 3: Lack of Real-Time Insights

• Many systems still rely on batch processing

• Delays in detecting customer behavior, risk events, or operational inefficiencies

• Missed opportunities to optimize or intervene

Solution: As MindsDB allows you to access data in real time, directly from the connected data sources—it ensures that AI models and agents always work with the most up-to-date information. When you connect a data source to an agent, there's no need for manual syncing or batch updates; the agent retrieves live data straight from the source every time it runs. This real-time integration helps maintain data accuracy, reduces latency, and enables faster, more reliable decision-making across your AI-powered applications.

Problem 4: Regulatory Compliance & Risk Management

• Constantly evolving regulations (e.g., GDPR, Basel III, AML, KYC)

• High costs for compliance teams and legal oversight

• Risk of fines, reputational damage, and legal consequences from non-compliance

Solution: MindsDB helps financial institutions and enterprises stay ahead of regulatory compliance and risk management by enabling real-time, AI-powered monitoring across structured and unstructured data sources. With MindsDB, organizations can connect compliance-related data—such as transaction logs, policy documents, customer profiles, and audit trails—without moving or transforming it. This allows AI agents to continuously analyze for anomalies, flag potential violations (e.g., AML triggers or KYC gaps), and generate auditable, explainable insights. The result: reduced compliance costs, faster response to regulatory changes, and minimized risk of fines or reputational damage.

Without effective AI implementation, financial services struggle with fragmented data, slow decision-making, and rising operational costs. Manual processes dominate everything from compliance reporting to fraud detection, making it difficult to respond in real time. Outdated infrastructure and siloed systems limit visibility, while customer expectations for instant, personalized service continue to rise. As a result, institutions face increased risk, reduced efficiency, and mounting pressure to modernize. 

MindsDB’s AI Analytics Advantage

Imagine leaving your data where it resides and accessing it where it is on demand using the existing tools without the need for extensive software engineering to implement the capabilities to move or transform your data. 

By enabling zero-copy, zero-ETL, real-time data access across your organization’s trusted infrastructure, MindsDB empowers teams to make faster, more informed decisions. Whether serving clients or driving internal business goals, this streamlined approach ensures that AI and analytics operate with the freshest, most context-rich data—securely and efficiently.

MindsDB fits seamlessly into your existing workflow by using familiar, industry-standard tools. You can connect and query data using SQL or make use of MindsDB's APIs and SDKs that allows users to pull data from MindsDB and send results to Microsoft Copilot or any external application, service, or agent.—making it easy to add powerful AI capabilities without reinventing your tech stack.

Here are some more advantages:

1. Connect & Unify your data — no ETL needed
With no data copying or ETL required, MindsDB delivers real-time results based on the most current data available.With over 200 connectors—including SQL/NoSQL, SaaS platforms, and file systems—MindsDB’s federated query engine lets you query across multiple disparate systems as if you were querying one database via MindsDB’s Knowledge Bases, saying goodbye to complex data pipelines.

2. Conversational analytics with natural language
MindsDB’s Agents enables you to have a conversation with your data where users can pose complex questions—like “Show profit by product for Q1”—and receive instant, accurate answers generated in natural language. No extensive coding required.

3. Embed analytics into apps
Integrate AI-powered search and analytics directly into internal/external apps using MindsDB’s REST API and SDKs—enhancing customer support tools, relationship management dashboards, and reporting portals.

4. Governed, secure, enterprise-grade
MindsDB preserves your existing security, controls, and access policies—enforcing compliance and maintaining data integrity, including row-level security, without moving data outside your trusted network -- which is crucial in heavily regulated environments. This is available via our Minds Enterprise solution. If you’d like to see a demo, you can get in contact with our team.

Real-World Use Cases

As the financial industry continues to accelerate, timely and accurate decision-making hinges on the ability to harness data across fragmented systems. MindsDB empowers organizations to unify structured and unstructured data—enabling intelligent automation and deeper insights across critical use cases such as onboarding, compliance, and customer engagement:

• Client onboarding & KYC
  Merge structured form data with unstructured documents and news. Analysts can instantly query customer risk profiles, speeding onboarding and reducing manual review.

• Risk & compliance monitoring
  Aggregate data from transactions, communications, and logs. Ask questions like “Flag high-risk wire transfers from new customers” and automatically detect policy breaches.

• 360° customer insights
  Combine transaction histories, account balances, CRM notes, and past queries. Discover high-value segmentation, cross-sell opportunities, or churn signals faster.
  
  

Furthermore, MindsDB’s AI solutions can be applied to:

• Loan and Credit Origination, Approval, and Status management

• Fraud Detection

• Credit and Portfolio Risk Analysis

• Customer and Asset churn or contraction prediction

Business Impact

MindsDB drives business impact by accelerating insights, reducing operational costs, and enabling smarter, real-time decision-making across data, analytics, and AI workflows:

• Time saved: Analysts spend ~20 hours/week on prep; with MindsDB, most questions take under 5 minutes

• Cost savings: Reduce reliance on custom ETL pipelines, centralized reporting, and external analytics teams.

• Enhanced compliance: Built-in governance reduces risk and audit complexity.

• Lower TCO: Eliminate the need for duplicating, transforming, or managing data in additional systems—access data in place using existing tools, with no deep engineering investment required.

• Infrastructure efficiency: Reduce costs across data, analytics, and AI stacks by streamlining access and model deployment.

• Improved modeling: Lower default and loss rates with more accurate, context-rich predictions.

• Fraud reduction: Boost fraud detection precision to minimize financial exposure.

• Faster decision-making: Enable compliance, audit, and analytics teams to generate insights in real time.

• Higher portfolio performance: Accelerate time-to-insight for more responsive investment strategies.

Example: Query Your Data in 3 Steps

Let’s explore an example of how you can query your financial data in 3 simple steps. The goal will be to connect a database that hosts financial portfolio information, create an agent and have a conversation with the data.

First, you can access MindsDB’s GUI via local installation of Docker, MindsDB’s Docker Desktop Extension or AWS Marketplace. 

Step 1: Connect Your Data

To connect your data, use the CREATE DATABASE syntax to establish a connection.

CREATE DATABASE postgresql_conn
WITH ENGINE = 'postgres',
PARAMETERS = {
   "user": "demo_user",
   "password": "demo_password",
   "host": "samples.mindsdb.com",
   "port": "5432",
   "database": "demo",
   "schema": "sample_data"
};

You can query the database to see that the data successfully pulls through. In this use case, we will make use of the `financial_portfolio` table.

SELECT *
FROM postgresql_conn.sample_data.financial_portfolio
LIMIT 10;

Step 2: Create an AI Agent

In this step, we will create an agent with the CREATE AGENT statement and feed it with data from the database.

Note: You can also unify your data by creating a Knowledge Base and provide it to your agent

CREATE AGENT financial_agent
USING
model = {
   "provider": "openai",
   "model_name": "gpt-4o",
   "api_key": "sk-xxxxxx"
},
data = {
   "tables": ['postgresql_conn.financial_portfolio']
},
prompt_template = 'postgresql_conn.financial_portfolio stores data about financial 
                    portfolios of an investment company';

We create the agent with the name financial_agent and provide the following parameters to the agent:

• model: This parameter specifies the underlying language model, including:

  • provider: This required parameter specifies the model provider from the list of supported providers.

  • model_name: This required parameter specifies the model name selected from the list of supported models.

  • api_key: This optional parameter (relevant to certain providers) stores the API key used to access the model. Users can supply it here or via environment variables.

• data: This parameter holds the data linked to the agent, including knowledge bases and data sources integrated with MindsDB.

  • tables: Stores the list of tables from data sources integrated with MindsDB.

• prompt_template: This parameter stores instructions for the agent. It is recommended to provide data description of the data sources listed in the knowledge_bases and tables parameters to help the agent locate relevant data for answering questions.

Step 3: Converse with the Agent

Now that the agent has been successfully created, you can have a conversation with the agent in MindsDB’s AI Chat Interface. We will ask the agent a few questions based on the financial_portfolio data.

Note: To access the chat interface and converse with agents, users need to enable the A2A API that is used to communicate between the chat interface and the agent.

Navigate to the Respond tab and select the agent you created under the Agents tab. Let’s go ahead and ask the Agent questions.

Question 1: What stocks are in the portfolio?

Question 2: What's the percentage breakdown of investments by category in the portfolio?

Question 3: What category has the most exposure?

Question 4: Leadership has mandated that we limit our exposure to crypto to 10%. Is the portfolio compliant and if not, what must I do to make it compliant?

Our agent has successfully answered our questions and provided suggestions/advice based on the question asked.

Why It Matters

MindsDB puts AI analytics within reach across the financial sector—whether it's for enterprise banks, midsize lenders, or fintech startups. By simplifying data complexity, democratizing analytics and eliminating the need for extensive engineering, the solution accelerates ROI from AI initiatives, improves customer experience, and mitigates risk.MindsDB empowers organizations to achieve markedly improved outcomes and consistently exceed client and business financial goals by supporting critical decision-making with real-time, AI-driven insights.