The FinTech industry has evolved rapidly over the last decade. What began as simple online banking portals has expanded into a diverse, hyper-competitive ecosystem of payment gateways, algorithmic trading platforms, digital wallets, lending services, and blockchain-based applications. Each of these systems generates vast amounts of transactional data that must be processed, stored, and retrieved in near real time. These workloads operate under extreme concurrency and unforgiving latency requirements.
Consider a few examples. A payment processor may need to sustain 50,000 transactions per second at peak load. A trading platform ingests millions of tick updates per minute. Digital wallets track balances across millions of concurrent users, ensuring accuracy down to the cent. Credit and risk scoring systems require real-time analytics over massive datasets, with results delivered in less than two seconds. In every case, a delay in posting a transaction or an error in scoring has direct consequences for profitability, compliance, and customer trust.
PostgreSQL has become a favorite among FinTech developers because of its reliability, ACID guarantees, and powerful SQL feature set. Yet as transaction volumes rise into the millions per second, even the most carefully tuned single-node PostgreSQL instance begins to show strain. This is where PostgreSQL Citus comes in—an extension that transforms PostgreSQL into a distributed, horizontally scalable system capable of handling financial workloads at scale.
In this article, we explore how PostgreSQL Citus powers FinTech use cases, while also diving into practical topics like data modeling, migration strategies, cost efficiency, and operational management—showing how distributed PostgreSQL delivers the performance, compliance, and reliability that high-frequency financial systems demand. Curious about use cases beyond finance? Read our blog on the Top 5 Real-World Use Cases for Citus to Scale PostgreSQL to explore its impact across different sectors.
How Citus Extends PostgreSQL for Financial Workloads
Citus is not a fork of PostgreSQL—it is an extension that integrates seamlessly into the database engine. This means FinTech teams keep the full power of PostgreSQL—familiar SQL syntax, drivers, and tooling—while gaining the ability to scale horizontally across many nodes.
The architecture centers on a coordinator node that receives client queries and distributes them across multiple worker nodes. Each worker stores a subset of the data in the form of shards, processes queries locally, and then returns partial results to be combined at the coordinator. This parallelization transforms what would be a bottleneck on a single server into a workload spread across the cluster. You can learn more in our Citus for PostgreSQL article.
For financial systems, this design is especially powerful. Sharding by a logical key—such as account_id, instrument_id, or user_id—keeps related workloads local to a shard and reduces global contention. Heavy analytical queries can run in parallel across workers without interfering with real-time transactions. Reference tables, such as currency codes or merchant metadata, replicate to all workers for fast lookups. Colocated tables allow joins to execute within the same shard, maintaining efficiency.
The result is that PostgreSQL retains its strengths—reliability, ACID guarantees, and rich SQL functionality—while gaining the elasticity of a distributed database. For FinTech engineers, this blend is essential: it preserves trusted tooling while solving the scaling challenges of high-frequency finance.
Real-World Applications: Citus for FinTech Use Cases
The clearest way to show the value is to examine real production patterns. Each Citus for FinTech use case below maps a common bottleneck to a specific distributed design.
Use Case 1: Real-Time Payment Processing
Business Context
Payment gateways are at the core of e-commerce, retail, and peer-to-peer transfers. They need to confirm transactions in less than a second, often while applying fraud checks, risk scoring, and settlement logic. With traffic spikes during events like Black Friday or holiday sales, the system must sustain tens of thousands of transactions per second without failing.
Challenges Without Citus
On a single PostgreSQL node, concurrency creates serious lock contention. High-traffic accounts or merchants become hotspots, delaying approvals. Running fraud detection queries alongside payment transactions puts further pressure on the same dataset, increasing latency. Scaling vertically helps temporarily, but eventually the hardware ceiling limits throughput.
Citus Solution
By distributing the transactions table on account_id or merchant_id, Citus spreads load across multiple workers. Each shard processes its own subset of transactions, isolating hotspots and reducing contention. Fraud detection queries execute locally on shards and return aggregated results quickly, enabling continuous anomaly scanning without blocking OLTP workloads.
Implementation Example
-- Distribute transactions by merchant_id
SELECT create_distributed_table('transactions', 'merchant_id');
-- Detect suspicious rapid transactions for a merchant in the last 30 seconds
SELECT merchant_id, COUNT(*) AS txn_count
FROM transactions
WHERE transaction_timestamp > NOW() - interval '30 seconds'
GROUP BY merchant_id
HAVING COUNT(*) > 20;
Operational Notes
- Use hash distribution to ensure even spread of merchants/accounts across workers.
- Monitor for shard hotspots and rebalance periodically using
rebalance_table_shards(). - Keep fraud detection rules precomputed in materialized views on workers for sub-millisecond lookups.
- Consider multi-region setups if payments must comply with data residency requirements.
Use Case 2: Algorithmic and High-Frequency Trading Platforms
Business Context
Trading platforms depend on millisecond-level precision. Every order placed, tick update received, or execution recorded needs to flow through the system in real time. For institutional trading desks and algorithmic strategies, a delay of even a few milliseconds can mean missed opportunities or financial losses. At the same time, regulators require full retention of order data for compliance and auditing.
Challenges Without Citus
On a single PostgreSQL instance, handling millions of tick updates per minute quickly overwhelms the database. OLTP workloads (order execution) and OLAP workloads (market analytics, compliance queries) compete for the same resources. Historical data storage further slows down queries, making it nearly impossible to serve real-time order books while also keeping years of trade data available for audits.
Citus Solution
By distributing order book data on instrument_id, Citus isolates updates for each security to its own shard. This allows multiple securities to be processed independently and in parallel. Time-partitioned distributed tables keep hot trading data responsive while archiving older trades efficiently. Analytics such as best bid/ask calculations execute across shards in parallel, ensuring real-time accuracy without slowing down execution.
Implementation Example
-- Distribute order book table by instrument_id
CREATE TABLE order_book (
instrument_id TEXT NOT NULL,
bid_price NUMERIC(18, 4),
bid_size INT,
ask_price NUMERIC(18, 4),
ask_size INT,
last_update TIMESTAMPTZ NOT NULL
);
SELECT create_distributed_table('order_book', 'instrument_id');
Operational Notes
- Co-locate ingestion pipelines with worker nodes to minimize coordinator overhead.
- Use time-based partitioning to manage long-term storage and compliance retention.
- Apply connection pooling to handle thousands of simultaneous client feeds.
- Periodically detach old partitions to keep active datasets lean and performant.
Use Case 3: Digital Wallets and Peer-to-Peer Transfers
Business Context
Digital wallets power billions of microtransactions worldwide, from contactless payments to international money transfers. Platforms like PayPal, Cash App, or GrabPay must keep balances accurate in real time and process millions of concurrent updates without error. Cross-border transactions add complexity, with multi-currency support and compliance with regional regulations.
Challenges Without Citus
On a single PostgreSQL node, concurrent updates to popular accounts lead to hotspots, slowing down transfers. Currency conversions create additional read-heavy queries, and ensuring compliance with regional data residency requirements is difficult if all wallet data lives in one central database.
Citus Solution
Citus distributes wallet balances by user_id, ensuring all activity for a user is processed locally on the same shard. Peer-to-peer transfers spanning multiple users on different shards are managed through two-phase commits, guaranteeing atomicity across the cluster. Reference tables for exchange rates replicate to all workers, enabling local currency conversions without expensive cross-shard queries. This design makes digital wallets one of the most compelling PostgreSQL Citus for FinTech use cases.
Implementation Example
-- Distribute wallet balances by user_id
CREATE TABLE wallet_balances (
user_id UUID PRIMARY KEY,
currency CHAR(3) NOT NULL,
balance NUMERIC(18, 2) NOT NULL
);
SELECT create_distributed_table('wallet_balances', 'user_id');
Operational Notes
- Use two-phase commits to ensure consistency on cross-shard transfers.
- Deploy regional clusters to align with jurisdictional data laws (e.g., GDPR).
- Replicate exchange rate tables to all workers for efficient lookups.
- Implement
FOR UPDATE SKIP LOCKEDto avoid deadlocks in concurrent payout queues.
Use Case 4: Loan and Credit Scoring Engines
Business Context
Lending and credit scoring platforms rely on real-time decision-making. Every loan application must be assessed quickly against transaction histories, repayment behaviors, and risk indicators. At the same time, regulators demand full transparency and auditability of credit scoring decisions.
Challenges Without Citus
Single-node PostgreSQL systems struggle to aggregate massive datasets under strict latency requirements. Aggregates like rolling spend averages or delinquency counts block writes, slowing down both OLTP and analytics. Large historical datasets needed for compliance audits add further overhead.
Citus Solution
Citus distributes transaction history by user_id, enabling scoring features to be computed in parallel across shards. Loan and transaction tables can be colocated on the same shard to keep joins local. Time-partitioned tables store historical data for auditability while ensuring live scoring queries remain fast.
Implementation Example
-- Aggregate features for scoring
SELECT user_id,
AVG(amount) AS avg_monthly_spend,
COUNT(*) FILTER (WHERE status = 'late') AS late_payment_count
FROM transactions
GROUP BY user_id;
On a Citus cluster, each worker runs this aggregation locally and sends only the results to the coordinator, reducing data transfer volume.
Operational Notes
- Store model features in JSONB columns for flexibility as models evolve.
- Ensure colocated joins for loan and transaction tables to minimize cross-shard overhead.
- Archive old partitions regularly to keep active scoring performant.
- Use replicated reference tables for audit trails and bureau data.
While these examples show the power of Citus in practice, designing a FinTech system that scales reliably requires more than choosing the right use case. The foundation lies in data modeling and regional strategy.
Data Modeling & Multi-Region Strategies for FinTech on Citus
Data modeling is one of the most important architectural choices in a FinTech system. The distribution key defines how workloads scale, how queries behave, and whether the cluster runs smoothly under peak demand. In Citus, this decision determines how data is sharded across worker nodes, and the right choice can prevent contention and uneven load.
In practice, distribution keys map naturally to business entities. Payment systems often shard by account_id, keeping all transactions for an account on a single shard. Trading platforms prefer instrument_id, isolating each security’s order book updates. Digital wallets align with user_id, ensuring that all balance updates and transfers for a customer are colocated. These choices minimize cross-shard queries, reduce lock contention, and keep performance consistent under scale.
For queries that frequently join multiple entities, colocating tables on the same key is essential. In some cases, denormalizing the schema is the better option, embedding values like merchant categories or currency codes directly into transaction tables. This approach reduces distributed joins and ensures high-throughput queries remain predictable when workloads spike.
Beyond performance, data modeling must account for compliance. Regulations such as GDPR in Europe and the U.S. GLBA impose strict controls on financial data, often requiring customer information to remain within regional borders. While GLBA is not a strict data-localization law, it drives U.S. financial institutions to keep sensitive customer data under domestic safeguards. Citus supports multi-region deployments where shards can be pinned to specific geographies. A European customer’s transactions can live exclusively on EU-based workers, while U.S. customer data remains domestic. The coordinator routes queries automatically to the correct region, maintaining both low latency and compliance.
Ultimately, careful modeling turns Citus into more than a scaling layer. By selecting appropriate distribution keys, minimizing cross-shard operations, and aligning shard placement with regional requirements, FinTech teams can build systems that are both high-performing and regulation-ready.
Adopting Citus in Existing FinTech Systems
For FinTech teams already running PostgreSQL, moving to Citus is more of an evolution than a full migration. The foundation remains the same—SQL syntax, tools, and drivers—so developers don’t need to learn a new database. The real work lies in preparing the schema for distribution and ensuring workloads are routed efficiently across shards.
The first step is to analyze existing workloads: which tables are read most frequently, which see the heaviest writes, and where contention occurs. From there, teams select distribution keys aligned with business entities, such as account_id, instrument_id, or user_id. This decision sets the performance profile of the cluster. Large static datasets, like currency codes or reference interest rates, are best treated as replicated tables to ensure they’re accessible everywhere without cross-shard queries.
Once the schema is prepared, staging clusters are invaluable for testing. They reveal potential pitfalls like unbalanced shard distribution or unexpected cross-shard joins. Benchmarking also gives confidence in throughput and latency before production rollout. Migration is then a matter of progressively cutting over services, often beginning with non-critical workloads. This staged approach minimizes risk while demonstrating immediate performance gains, helping teams build confidence internally that Citus is the right path forward.
For teams that want to avoid the complexity of building and operating Citus clusters themselves, ScaleGrid for PostgreSQL with Citus provides a managed path. It automates setup, scaling, and multi-region deployments, making it easier to realize Citus’ benefits while staying focused on building FinTech applications.
Cost Efficiency & Resource Management
In finance, efficiency is about more than speed—it’s also about cost control. Traditional vertical scaling demands specialized, expensive hardware that remains underutilized during off-peak hours. This creates a cost profile where capacity is sized for the worst case, even if those peaks happen only a few times per year.
Citus changes this model by enabling horizontal scale on commodity cloud infrastructure. New workers can be added dynamically as trading volumes surge, or when payment activity spikes during holiday shopping. When demand falls, clusters can shrink back down, returning capacity to the pool. This elasticity keeps infrastructure spending proportional to actual traffic, aligning cost directly with value delivered.
Beyond raw elasticity, distributing workloads evenly prevents hidden costs like underused CPU or memory. By balancing shards across workers, organizations extract full utilization from existing resources before provisioning more. For startups, this approach extends runway; for established enterprises, it avoids runaway cloud bills. In both cases, Citus creates a scaling model that is technically and financially sustainable.
Operational Efficiency and Managed Services
Citus removes single-node limits but introduces operational complexity. Clusters must be monitored for shard imbalance, queries optimized for distributed execution, and failover orchestrated seamlessly to maintain uptime. For FinTech teams, these responsibilities compete directly with the need to build product features, maintain compliance, and innovate quickly.
Managed Citus services reduce this burden. Platforms like ScaleGrid handle the operational side—automated provisioning, high availability, continuous monitoring, and intelligent shard rebalancing. Backups, point-in-time recovery, and disaster recovery policies are applied consistently across the cluster, removing uncertainty.
This allows FinTech engineers to spend their time where it matters most: building fraud detection models, optimizing trading strategies, and creating seamless customer experiences. The database becomes a dependable utility rather than a daily operational concern. For organizations where uptime, compliance, and speed are non-negotiable, offloading operations to a managed service is often the difference between moving fast and standing still.
Conclusion and Future Outlook
FinTech platforms operate under pressure unlike any other sector: millions of transactions per second, global compliance requirements, and customers who expect instant responses. Citus extends PostgreSQL to meet these demands, turning a trusted relational database into a distributed system that can handle scale, concurrency, and regional complexity with ease.
The lessons throughout this article are clear. Citus for FinTech use cases—from payments and trading to wallets and credit scoring—demonstrate that PostgreSQL is capable of scaling for the most demanding workloads without abandoning its proven reliability. At the design level, data modeling and distribution strategies shape performance and compliance, making shard choices and schema patterns critical for long-term success. Adoption is evolutionary, not disruptive, since teams keep PostgreSQL’s familiar ecosystem while gaining horizontal scalability. On the business side, elasticity provides financial discipline by aligning infrastructure spend with actual usage, while managed services free FinTech teams to focus on innovation instead of operations.
Looking forward, the demands on FinTech databases will only intensify. AI-driven fraud detection will require streaming analytics over billions of live transactions. Blockchain integrations will add new audit requirements alongside traditional financial records. Global payments will continue to push the boundaries of compliance, data residency, and latency expectations. Citus positions PostgreSQL as the engine for this next wave of financial technology, combining open-source flexibility with distributed power.
For FinTech engineers, the takeaway is simple: scaling PostgreSQL no longer means abandoning it. With Citus, your database grows with your business, adapting to the realities of high-frequency finance. For decision-makers, the opportunity is equally clear: invest in an architecture that balances performance, compliance, and cost while empowering teams to move faster. To explore how other industries are leveraging Citus to scale PostgreSQL, read our blog Top 5 Real-World Use Cases for Citus to Scale PostgreSQL.
Get started with ScaleGrid for PostgreSQL today—simply configure your Citus Cluster and ScaleGrid will take care of the rest for you. As you plan ahead, ask yourself: will your current database be ready for the next decade of financial innovation?
