Financial Analytics

Financial analytics is the application of big data analytics techniques to support data-driven decision-making, improve financial risk management, ensure regulatory compliance, and similar purposes.

Organizations generating large volumes of financial data depend on analytics platforms that efficiently scale to meet compute and storage demand while meeting low-latency performance metrics.

Modern financial analysis draws from real-time and historical data sources to derive insights into financial performance, identify fraud, and develop machine learning algorithms. However, traditional data warehouses increasingly struggle to keep pace with the scale and variety of financial data sets.

This guide will introduce ways financial institutions use analytics and present cases where Starburst’s open data lakehouse analytics platform helped financial organizations improve data management in a highly regulated industry.

Financial analytics use cases

As with other industries, organizations in the financial sector use analytics to improve efficiency, sales, and profitability. The big data analysis techniques successful businesses apply go far beyond calculating financial ratios in Excel spreadsheets or creating visualizations of regression analysis. They involve querying multiple petabyte-scale data sources to generate insights that individual analysts could never spot on their own. Where financial institutions differ is the degree to which they must comply in real-time with complex regulations. Financial analytics is critical to preserving the financial system’s integrity. The following use cases demonstrate how analytics improves financial institutions’ compliance and operational excellence.

Regulatory reporting

Financial institutions must report to regulators in every jurisdiction they serve. Within the United States, these jurisdictions comprise a complex web of local, tribal, state, and national regulators and law enforcement agencies. Reporting and compliance becomes more complex once the institution expands internationally.

Moreover, the nature of regulatory reporting has changed dramatically. Corporate finance departments still create financial statements documenting balance sheets, cash flow, and liquidity. However, regulatory reporting involves much more than these monthly and quarterly filings. 

For instance, US financial institutions have 72 hours to report significant security breaches to the Cybersecurity and Infrastructure Security Agency — 24 hours if the incident involves a ransomware demand. Failing to do so because analysts can’t compile the data could expose the company to severe penalties.

Anti-money laundering

The sheer volume of financial transactions is impossible for compliance teams to monitor manually, creating opportunities for criminal organizations to sever the link between crimes and their proceeds. Anti-money laundering regulations require financial institutions to monitor for and report potential money laundering to law enforcement. 

Quick reporting requires timely access to consistent, complete, and accurate data sources. At the same time, financial institutions must comply with data privacy and sovereignty regulations that limit data motion and access. Legacy data platforms struggle to meet these demands. 

Today’s financial analytics platforms use modern query engines that can process data across the global enterprise, delivering reliable, low-latency results while providing effective security and governance.

Know Your Customer: Underwriting optimization

Financial services providers collect and process Know Your Customer (KYC) data to comply with regulations and to optimize underwriting. Working with any customer involves risk. Underwriting determines the risk’s acceptability by building a complete financial profile to properly assess the customer’s risk.

KYC analytics requires speedy access to varied information drawn from internal and external data sources. While underwriting minimizes financial risks, collecting KYC information raises the company’s data privacy risk exposure and requires controlled access to this highly sensitive data.

Risk modeling and assessment

Regulatory compliance is one aspect of risk financial institutions must manage. At the same time the complex, dynamic financial system requires constant management of other business risks. 20th Century techniques like trend analysis and forecasting are no longer sufficient. Although the fundamentals remain the same, modern financial modeling happens at a once unimaginable scale, drawing upon more diverse data sources.

For example, extreme weather’s increasing frequency significantly impacts risks to insurers and lenders. Legacy models based on historical data may no longer apply. Minimizing risk and ensuring future performance requires models that constantly adjust to new weather patterns. Analytics platforms can combine structured and unstructured data sources to create up-to-the-minute estimates of the company’s risk exposure.

Customer 360

Financial services companies increasingly use Customer 360 strategies to model customer behavior in near real-time to deliver tailored products and promotions designed to maximize profitability.

Predictive customer analytics consumes vast quantities of data from multiple sources, often streaming systems generating continuous flows of unstructured data that warehouses were not set up to handle.

Financial analytics platforms can ingest and process this real-time data, delivering results in seconds. Automated marketing systems apply these results to customer behavior models to design promotions that will improve conversion rates. For example, when a brokerage firm predicts a customer is open to purchasing a value-added service, its marketing systems may present promotional pricing to encourage the customer to make the move.

Real-time analytics

Customer 360 is an example of real-time, or near real-time, analytics use case. Analyzing network activity logs is another. The scale and complexity of enterprise networks is impossible to monitor manually. Analytics platforms can ingest the sparse data streams logging systems generate and quickly process them to identify abnormal behavior. The rapid identification of potential security events lets response teams take action before an incident becomes a serious breach.

Near real-time analytics also enhances the way organizations make business decisions. These platforms can feed data to business intelligence dashboards, allowing the CFO and other stakeholders to make informed decisions based on the absolute latest data available.

Financial analytics case studies

Starburst’s data lakehouse analytics platform to streamline data management, tighten regulatory compliance, and improve business performance. Here are four examples of Starburst customers in the financial industry.

Global investment firm anti-money laundering case study

AML monitoring at one of the world’s largest banking and financial services companies requires scanning hundreds of millions of daily transactions residing in its global network of data warehouses. The complexity of its products and its regulatory burden made identifying AML events difficult.

Deploying Starburst Enterprise unified the bank’s data sources within a single analytics and governance solution. Starburst’s pushdown queries process data at the source, ensuring compliance with data sovereignty regulations while quickly identifying potential AML events.

Learn more about how Starburst helped this global bank fight financial crime.

Detecting fraud, insider trading, and abuse case study

A not-for-profit regulatory organization chartered by the United States to oversee brokers and broker dealers for signs of insider trading, fraud, and abuse. To do this, it must analyze nearly 80 billion trading events and add terabytes of data to its repositories every day. Its data warehousing infrastructure couldn’t keep up without excessive infrastructure investments.

Looking for sustainability, the agency built a scalable data lake on Amazon Web Services with Starburst Enterprise analytics. The seamless migration and improved performance accelerated the agency’s trading fraud marketing activities.

Learn more about how this regulatory organization uses Starburst for interactive, ad hoc analysis.

Insurance analytics case study

Assurance is an online insurance platform that uses data science to create personalized customer experiences that simplify the biggest personal financial transactions. The company’s fragmented PostgreSQL architecture was slow and siloed. Starburst Enterprise unified Assurance’s data to create a single point of access for all its analytics activities. Time-to-insight dropped from weeks to hours and conversion rates increased ten percent.

Learn more about Assurance’s journey to a cloud data lakehouse.

Data team productivity and efficiency case study

Bank Hapoalim is headquartered in Israel with more than 2.5 million customer accounts in branches around the world. The bank’s Apache Hive infrastructure had become a bottleneck due to slow query turnaround times. Adopting Starburst Enterprise gives analysts and data scientists access to twenty years of transaction history as well as real-time data. Self-service analytics now speeds insight generation and automated scenario testing shortens product development lifecycles.

Learn more about how Bank Hapoalim uses Starburst to improve data team productivity.

How Starburst helps with financial analytics

Four of the top six North American banks use Starburst to accelerate insight generation, mitigate risks, and enhance revenue while improving regulatory compliance. Starburst features enabling financial analytics include:


Data privacy and sovereignty regulations bar the data movement needed to create a single source of truth. Starburst creates a single point of access that leaves data in place. Connectors to over fifty enterprise data sources unify architectures within a virtual access plane. Pushdown queries process data where it lives to produce analyses without moving data.

Data management

Starburst is the enterprise version of the Trino massively parallel processing SQL query engine. Using ANSI-standard SQL lets any authorized user query data across the enterprise, reducing data team workloads. Starburst eliminates many complex ETL pipelines and makes the remaining pipelines faster and more reliable.

Query efficiency

Starburst’s optimized Trino queries let data teams strike the optimal balance between cost and performance. Dynamic filtering, smart indexing, and materialized views minimize data in motion while automated query planning and cluster scaling optimize compute resources.


Gravity is Starburst’s universal search, discovery, and governance layer. From a single pane of glass admins can create granular role and access-based policies that Starburst will enforce throughout the company’s data architecture.

Learn more about how Starburst powers financial analytics at the world’s largest institutions.

Start Free with
Starburst Galaxy

Up to $500 in usage credits included

  • Query your data lake fast with Starburst's best-in-class MPP SQL query engine
  • Get up and running in less than 5 minutes
  • Easily deploy clusters in AWS, Azure and Google Cloud
For more deployment options:
Download Starburst Enterprise

Please fill in all required fields and ensure you are using a valid email address.