Home Solutions Agentic AI Regulatory Compliance Starter Kit

50%↓

Reduction in time to answer regulatory inquiries

30-40%↓

reduction in development effort to collate responses

100%

Audit-ready, error-free compliance

How quickly can your compliance team answer “What did we know and when did we know it?”

Wealth managers, asset managers, and banks frequently receive critical regulatory inquiries from FINRA, SEC, FDIC, and the Federal Reserve that demand precise answers to seemingly simple questions such as “What did we know and when did we know it?”. Financial institutions are required to reproduce records exactly as they appeared at a specific point in time, but answering these straightforward questions is anything but simple.

Data from financial institutions is scattered across siloed systems like trading platforms, account databases, third-party event streams, and communication logs. These sources are a combination of both structured and unstructured financial data, each with different, fluid schemas. Legal and compliance teams, who interact with regulators, typically lack SQL expertise and depend on IT to extract information across these sources. This results in slow, error-prone, and manual data-gathering efforts that delay responses and risk inaccuracy.

Tracking historical data adds another layer of complexity for financial institutions. Most systems overwrite original records, rely on periodic snapshots, or use clunky metadata solutions that don’t support historical reconstruction. As inquiry volumes grow, financial institutions struggle to keep up, which increases the risk of fines, consent orders, or reputational damage for missed deadlines.

What if legal and compliance teams could travel back in time to view data exactly as it appeared on any given day without needing another ad hoc development effort?

Grid Dynamics’ Agentic AI Regulatory Compliance Starter Kit makes this possible for financial institutions. It combines XTDB, an open-source bitemporal database that preserves complete financial data history, with Agentic AI, which interprets natural language regulatory inquiries. The solution translates those inquiries into SQL commands and retrieves exact historical records, reducing manual effort, accelerating response times, and delivering a tamper-proof audit trail so compliance teams can confidently answer: “What did we know, and when did we know it?”

XTDB: The bitemporal database advantage

XTDB is an open-source bitemporal database, purpose-built to retain a complete history of financial data and enable time-aware analytics essential for supporting regulatory compliance in financial institutions.

Trade data table with reporting and ingestion timestamps

IMMUTABLE TIME-TRAVEL DATA STORE

Bitemporality delivers on-demand historical reconstruction

Track every change without overwriting or deleting records using XTDB’s bitemporal model. Each update creates a timestamped version, enabling financial institutions to query historical data just like current data. Reconstruct the exact state of any record at any point, even after changes, to meet FINRA Rules 4511 (books and records) and 3110 (supervisory reconstruction). XTDB captures both valid_time (when the data was true in business) and transaction_time (when it was recorded). For instance, if a role changed on January 5 but was entered on January 7, a valid-time query as of January 6 shows the old role, while a transaction-time query reflects what the system recorded. This distinction is crucial for handling corrections, backdated entries, and answering: what was known, and when?

TAMPER-PROOF AUDIT TRAIL

Regulatory auditability and data integrity

Prove that records are tamper-proof with historical data produced on demand through XTDB’s immutable bitemporal architecture. Every who, what, and when change is stored, ensuring full traceability and alignment with regulatory expectations for data integrity and audit trails. If a dispute arises, this immutability provides a tamper-proof audit trail, which is a critical capability for regulatory audits and internal control. Ambiguity is removed and compliance is demonstrated with confidence.

Timeline showing data event vs system recording time
Data sources processed by compliance team to answer regulators

SCHEMALESS DOCUMENT DATABASE

Seamless integration & data accessibility

Unify and query data across fragmented sources, including trades, accounts, and reference data, with a schemaless database. XTDB’s query engine supports both standard SQL and XTQL, its native query language. This provides flexibility for a wide range of developer and analyst use cases. Once ingested, the schema is inferred, making the data accessible through a standard PostgreSQL-compatible interface. Even as source systems and data models evolve, compliance queries continue to function without disruption.

INNOVATIONAL TRANSPARENCY

Open-source and enterprise-ready

As an open-source project available on GitHub, XTDB benefits from community-driven transparency and innovation. Trusted across industries like financial institutions, insurance, and e-commerce for auditing and data lineage, it is cloud-ready on both AWS and GCP and extensible through a REST API layer and Kafka Connect. Built on the Apache Arrow Parquet format for high-performance queries, it delivers fast, scalable analytics and works with any S3-compatible datastore to ensure high availability.

Data stack with GitHub, Kafka, Arrow, and S3

Regulatory Compliance Starter Kit capabilities

LLM agents generating and executing SQL queries

NATURAL LANGUAGE PROCESSING

Parse and understand natural language inquiry

When compliance questions such as “Provide all trades in Q1 2023 where employees traded the same stocks as clients.” (a possible insider trading scenario) are posed, analysts traditionally translate them into a combination of database queries across HR records, trading logs, and market data. This Agentic AI system uses a Large Language Model fine-tuned on financial services regulatory compliance tasks to parse and understand the inquiry, identifying key entities (employees, stocks, trades, time period) and the intent (such as detecting insider trading patterns).

ORCHESTRATION BRAIN

Reason agent for data identification

The Reason Agent, the orchestration brain, takes the interpreted inquiry and identifies the required data. For example, in the same query, “Provide all trades in Q1 2023 where employees traded the same stocks as clients”, it determines the need for employee and client trade data, stock-date matches, and account ownership links. It consults a vector database of schema knowledge to map concepts to actual sources (For example, “employee trades” maps to the OMS trade table filtered by employee accounts). The Reason Agent builds a query blueprint with joins, filters, and bitemporal conditions like “as of last quarter,” applying advanced reasoning and reusing prior queries or breaking tasks into sub-queries as needed.

Reason agent accessing structured and unstructured data
LLM generates SQL, joins sources, and gives insights

SQL FROM NATURAL LANGUAGE

Act agent for SQL generation

The Act Agent generates the actual SQL (or XTQL) queries once the Reason Agent has defined the plan. It builds structured queries based on schema context and relationships (For example, joining trades, accounts, and employee tables with Q1 2023 filters) and formats and optimizes them for XTDB’s bitemporal model. It applies temporal predicates based on Allen’s Interval Algebra to compare time intervals across valid time and transaction time. The agent translates natural language into executable database queries, guided by domain knowledge and schema mappings for high precision.

SECURE QUERY EXECUTION

Guardrails and validation

The Guard and SQL Validator validates each query to ensure correctness, security, and compliance with access policies. It supports AI explainability by confirming that the Query Agent interprets inquiries accurately and avoids overly broad pulls. It enforces role-based access control and validates query syntax against the database. The system executes approved queries on XTDB’s bitemporal datastore and formats results into reports or charts. If results are incomplete, the Reason Agent adjusts the logic, and the Query Agent regenerates refined queries in an iterative loop, logging all steps. The system delivers outputs in regulatory formats such as CSV, Excel, XML, FIX, and ISO 20022, or auto-fills formal submissions. A submission review interface enables essential human oversight.

SQL flow for FINRA-compliant data outputs

How the Agentic AI Regulatory Compliance Starter Kit works

The starter kit seamlessly integrates into your existing data landscape through two core layers, including data ingestion & storage and AI query & application, underpinned by robust scalability, governance, and data security controls.  

AI agents turning queries into SQL and reviewing results

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Regulatory compliance use cases

Insider trading investigation

A regulator requests employee/family trades before Announcement X. The solution links HR, trade, and insider-access data instantly, using XTDB’s historical accuracy to detect suspicious activity (for example, spouse’s trading prior to news). Results take under an hour, down from days, providing full auditability and uncovering previously missed insider activity.

Best execution analysis

FINRA asks to verify Q2 2024 trades against NBBO prices. XTDB’s timestamped storage enables immediate trade-market alignment. AI rapidly flags anomalies, automating previously tedious manual comparisons. Reports highlighting potential issues are delivered in minutes, allowing proactive compliance checks that previously took days.

Trade allocation review

Regulators check trade allocations for fairness. XTDB reconstructs historical allocations, client profiles, and allocation rules accurately, despite historical changes. AI swiftly queries and identifies unfair allocation patterns, quickly satisfying auditors. Automation strengthens internal controls, ensuring preferential treatment is promptly detected and resolved.

Market manipulation surveillance

An exchange investigates spoofing via rapid order cancellations. XTDB’s millisecond-precision order-book replay, paired with AI analysis, identifies previously undetectable manipulation patterns. Automated queries reduce false positives, providing regulators with clear, audit-proof evidence quickly. This drives swift enforcement and uncovers insights impossible through manual methods.

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