Svg Vector Icons : http://www.onlinewebfonts.com/icon More Trending Articles

Leveraging Data Analytics in Finance to Drive Growth

Data analytics has empowered financial services businesses to form better customer relationships, offer new products at greater scale, and better compete in the marketplace with offerings precisely targeted to customer needs. Integrating live data sources maximizes these benefits by allowing companies to take advantage of real-time IoT, financial market, and account activity data. Read on to discover how organizations are using data analytics in finance to become more competitive.

3 Trends in Data Analytics for Financial Services

Financial services companies are increasingly leveraging the massive volumes of data at their disposal to glean insights and improve decision-making. Three trends have emerged for unlocking the value of data in financial services.

1. Data in the cloud 

Legacy on-premises systems have several limitations that result in a lack of data visibility, inability to securely access critical third-party data, and incapacity to scale quickly. For this reason, organizations in many industries—not just finance—are moving to the cloud. The cloud’s unique capabilities enable financial services companies to grow their business without sacrificing security. The cloud is flexible and nearly infinitely scalable. And modern platforms such as Snowflake can easily handle semi-structured data, essential for real-time analytics.

2. Data collaboration

Security is primary when it comes to financial data. But in today’s tech-rich world, financial services companies must be able to share data instantly and seamlessly so customers can perform necessary tasks with their accounts. Additionally, financial organizations must work together to facilitate services. For example, credit agencies must share information with banks for account and loan approval. 

Secure data sharing requires a centralized repository that eliminates the need for data extracts, which are inefficient and create security risks. A platform such as Snowflake allows financial institutions to easily determine who sees what data and ensure all business units and partners access a single and secure version of their data. 

3. Modern data strategies

Modern data strategies are coming to the forefront, enabling financial services companies to improve their analytics and business intelligence. These strategies enable companies to make better and more efficient use of the data available to them.

  • Data democratization: Data used to be the sole purview of data professionals. But today’s world moves so quickly that decision-makers can’t afford to wait hours or even days for a report to be delivered by the data team. They must be able to access relevant, quality data and mine it for insights on their own. To accomplish this, financial services companies are implementing cloud data platforms that serve as a single source of truth and result in a unified view of data across products, businesses, and relationships. 

  • Gleaning insights from alternative data: Third-party data is invaluable to today’s financial services companies. Organizations can leverage alternative data from various platforms, apps, and websites to gain additional insights into prospective customers. This data includes demographic and psychographic information as well as business details. 

  • Bolstering financial data analysis capabilities: Financial data analytics can help companies generate predictive insights, marketing insights, and guidance for product development. Additionally, analytics can spot operational inefficiencies and opportunities to reduce risk. Financial services companies are implementing more-robust data analytics tools that allow their teams to conduct business intelligence and put insights into action quickly.

Examples of Data Analytics in Finance 

Let’s look at a few real-world examples of data analytics in finance to see how a financial services company might use data to improve its business.

Customer analytics

Customer analytics helps organizations better engage customers and more effectively target prospects to generate new business. For example, companies can track the customer journey, segment customers by interest or demographic details, and spot customer retention patterns. 

Risk analytics

Risk analytics helps companies identify and respond to a variety of risks, improve risk management, and meet compliance requirements. Examples include generating fair-lending risk assessments and sales practices assessments, and tracking related metrics.

Business strategy analytics

Business strategy analytics allows companies in the finance industry to improve decision-making in a variety of areas. For example, they can generate benchmarking and maturity assessments, identify ways to attract and retain talent, and spot opportunities for new products and new markets for existing products.

Challenges to Data Analytics in Finance

Several challenges prevent companies in the finance industry from implementing robust data analytics strategies and seeing the benefits. Three are particularly common. 

  • Data silos: Because many organizations have data living in silos (various systems, databases, applications, and so on), they can’t effectively cross-reference data or build multidimensional dashboards. A modern data platform that serves as a centralized repository is a must for data analytics in finance. 

  • Inability to efficiently analyze semi-structured data: Valuable data comes from many different external sources, and not all of it is structured. In fact, semi-structured data such as JSON is typical for real-time data streams. Therefore, financial services companies must have the ability to quickly and easily ingest alternative data into their data platform and analyze it.  

  • Poor governance strategies: Opening up access to data without strategic governance creates significant risk and can result in inaccurate insights. Organizations must have a reliable framework for managing data, ensuring quality, minimizing risk, and meeting data-focused regulatory compliance mandates.  

How Snowflake Enables Financial Services Companies to Harness the Benefits of Analytics

The Snowflake Financial Services Data Cloud is helping banks, brokerages, insurers, and financial technology (FinTech) companies collect, store, analyze, and share data easily and securely. With Snowflake, you can leverage the power of your data to create personalized experiences, improve business efficiency, and mitigate risk. 

Snowflake eliminates data silos so you can access all your data in a single, globally available platform. It allows you to securely share live, governed data from multiple on-premises and cloud-based systems, across lines of business and subsidiaries, and with partners, all while meeting compliance and regulatory objectives.

Supercharge personalized experiences

Use real-time insights from all your data to create 360-degree views of the customer experience and personalize the customer experience to improve it. Enhance your financial products and understand what customers, investors, and partners want at any given moment.

Support mission-critical processes

Support your processes with timely, comprehensive, and secure access to data. Optimize the performance of data models and automate processes with a platform for data science.

Facilitate governance, compliance, and reporting

Use insights to combat fraud, increase security, and mitigate operational risk in real time. Capitalize on Snowflake’s built-in security features, such as end-to-end encryption for data at rest and in transit to protect your most valuable business asset.

See Snowflake’s capabilities for yourself. To give it a test drive, sign up for a free trial