What is data analytics in finance and how is it used?

Financial data analytics enables organizations to obtain actionable insights from their financial data to improve overall financial performance, offer new products and services to clients, model and mitigate risk and reduce churn. Organizations can also use financial data analytics to maintain regulatory compliance, manage ESG compliance, optimize revenue, evaluate profitability by product and client, and improve cash flow from operations.

This article focuses on how financial services firms — banks, insurers, asset managers and others — apply data analytics across their business. For how a company's own finance department analyzes its ledgers, payables, receivables and forecasts, see what is financial analytics?

How is data analytics benefiting the financial industry?

While financial data analytics is helpful in all industries, it is imperative in the financial services industry. Organizations such as banks, insurers and fund managers face various data-related challenges, including compliance, the need to access data from multiple data sources, and the need to provide timely and accurate information and serve competing requirements from the front, middle and back-office.

Traditionally, landing financial data into a data warehouse from diverse sources such as CRM systems, databases and streaming data feeds has been slow and time-consuming. Data is frequently stale by the time it is available, resulting in poor line of sight and making it difficult to make sound business decisions.

How data analytics supports the front, middle and back office

• Front office: Customer-facing teams use analytics to segment clients, personalize offers, price products and identify cross-sell opportunities from CRM and transaction data.
• Middle office: Risk and compliance teams use analytics to measure credit, market and liquidity risk, monitor exposures, and detect suspicious activity.
• Back office: Operations teams use analytics to reconcile transactions, monitor settlement and claims processing, and reduce the cost of servicing accounts.

Each group needs different views of the same underlying data, which is why consistent definitions and governed access matter.

How is data analytics used in financial services?

With more capable financial data analytics, financial firms can:

• Identify new customer segments for revenue and profit growth
• Offer new products and services to clients based on data from CRM systems
• Enhance revenue and profitability by being able to quickly answer business-level questions about customers, products or business operations
• Improve efficiency and productivity by dramatically reducing the cost and time required to translate data into insights
• Ensure future competitiveness by leveraging data-driven analytic and machine learning initiatives to enable digital transformation initiatives and reduce service delivery costs

Common applications by sector include:

• Banking: credit risk scoring, customer churn analysis, branch and channel performance, and anti-money laundering monitoring
• Insurance: claims analysis, fraud detection, underwriting risk and policyholder retention
• Asset and wealth management: portfolio performance, client profitability and regulatory reporting

Key challenges

• Fragmented data: core banking, policy administration, trading, CRM and market data systems each hold part of the picture.
• Stale data: batch pipelines delay information that risk and fraud teams need quickly.
• Regulatory pressure: firms must show where figures came from, who accessed data and how calculations were made.
• Data security: customer and transaction data requires strict access controls.

Best practices

• Build a complete view of each client and account by bringing together account, transaction and CRM data rather than analyzing each system separately.
• Apply role-based security at the object, column and row level.
• Maintain auditability and versioning to support compliance and calculation repeatability.
• Serve reporting and data science from the same data so that risk models, regulatory reports and client analysis use consistent figures.

Incorta for financial data analytics

Incorta's Direct Data Platform™ enables financial services organizations of all kinds to quickly and securely provide self-service data access to various stakeholders, including analysts and data scientists. By leveraging Incorta's innovative Direct Data Mapping® technology, organizations can easily tap data from diverse sources without the need for traditional data modeling and data engineering work.

With Incorta security features, organizations can ensure that sensitive financial data is fully protected. Application-level role-based access controls, including object, column and row-based restrictions, ensure that data remains secure. Access is also fully auditable, supporting financial compliance and reporting requirements. Data governance and data versioning in Incorta also help financial firms meet additional obligations, including data retention and calculation repeatability.