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Banking Data Analytics: The Growing Importance of Big Data in Finance

Banking is a data-intensive industry, and financial institutions that capture the value of their data are positioned to outpace their competition. Banking data analytics helps to make sense of this data, providing the tools and technology required to extract insights. In recent years, the importance of analytics in finance has grown significantly, as banks have begun leveraging their data assets to improve customer service, reduce costs, manage risk, and accelerate operations. This article explores the growing importance of big data in banking, focusing on the essential role that data analytics plays in improving profitability.

Use Cases for Banking Data Analytics

As the banking industry becomes more data-driven, financial institutions are turning to advanced analytics to gain valuable insights into their business operations. Here are five types of analytics techniques being used to help financial institutions drive better business outcomes and gain a competitive edge in the marketplace.

Risk management (predictive analytics)

As global and domestic market conditions grow more complex and unpredictable, predictive analytics is playing an increasingly important role in detecting the underlying drivers of risk, enabling decision-makers to respond effectively. By connecting data from market, economic, and consumer-related sources, predictive analytics solutions can uncover trends and anticipate impending changes in market conditions, supporting data-driven investment decisions and risk management response.

Fraud detection (real-time analytics)

Real-time analytics has important implications for fraud detection. This technology detects anomalies and patterns in financial data as it is being created. Powerful algorithms identify financial fraud through analyzing factors such as transaction size, time of day, and location. By monitoring financial transactions in real time, financial institutions can actively prevent fraudulent activity as it occurs. 

Customer segmentation, marketing, product & revenue penetration  (behavioral & share of wallet analytics)

By providing insights into customer behavior and preferences, behavioral analytics helps financial institutions understand their customers as individuals. Identifying customer segments based on their behavior and preferences enables marketing teams to target individual customers with personalized marketing messages and offers. Additionally, share of wallet analytics can be used to determine how well certain products have penetrated target market segments, informing strategic planning.

Streamline business processes (operational analytics)

Operational analytics provides insights into the performance of various aspects of operations and is used to identify inefficiencies and resource bottlenecks. Analyzing data on the time, resources, and costs associated with various processes permits organizations to more easily identify where improvements can be made. 

Position optimization (machine learning)

Machine learning is a type of artificial intelligence that allows computers to learn independently. With the ability to quickly analyze massive amounts of data and identify obscure patterns and relationships, machine learning is a valuable asset for position optimization. It supports more informed asset selection by analyzing data on a wide range of investment options to identify those most likely to perform optimally in a given market environment. Machine learning algorithms can be used to help investors and investment advisors optimize portfolios by identifying an asset mix tailored to the desired level of risk and return.

The Role of the Cloud Data Platform in Finance Analytics 

In an increasingly data-centric world, financial institutions are turning to cloud data platforms to store, manage, and analyze their data. The cloud data platform provides banks and other financial service businesses with a single platform to centralize their data, making it easier to access and analyze. Here are four key ways this technology is helping organizations streamline their data management processes, reduce costs, and gain deeper insights into their business operations.

Near-limitless speed and scale

Thanks to near-limitless speed and scale, the cloud data platform is helping financial organizations realize the full potential of their banking data analytics initiatives. With isolated, elastic storage and compute resources dedicated to each workload, unlimited users can experience fast and efficient performance. Scalable and flexible architecture enables financial institutions to capitalize on cutting-edge analytics and business intelligence capabilities, including predictive insights, interoperability, organic and inorganic growth actions, and more. A future-proof foundation that can grow with the organization provides robust security and business continuity.

Secure data sharing & data accessibility ease

Financial institutions collect, store, and use large amounts of sensitive data that’s subject to strict regulations. Understandably, teams are exceptionally cautious about exchanging data outside the organization. But thriving in today’s data-centric business environment requires data collaboration between partner companies. 

Cloud data platforms facilitate instant, easy, and secure data collaboration. Using a platform such as Snowflake, companies can share data between parties in a compliant manner without having to move it. With a fully governed data exchange, companies retain complete control of their data, allowing them to easily monitor usage and access, control the publishing workflow, and use a multitude of built-in data security features.

A single source of truth for structured and semi-structured data

Modern banking data analytics tools leverage data from many different sources generated in a variety of formats. When data is siloed across various systems and solutions, it can be difficult to see the complete picture. Centralizing relevant financial and customer data into a single source of truth facilitates greater accessibility and accuracy, providing a unified view across all data sources that strengthen analytics initiatives in finance. 

Superior security for sensitive and regulated data

The best modern data platforms are well equipped with built-in security and governance features. With fine-grained governance and access controls, organizations can ensure the privacy of consumer data. Look for security that supports SOC 1 Type 2, SOC 2 Type 2, and PCI DSS requirements for industry-leading data protections.

Mobilize Banking Data Analytics with Snowflake

Snowflake’s Financial Services Data Cloud is ideal for banking data analytics initiatives. With it, financial organizations can mobilize their data, eliminating silos. Thanks to Snowflake’s capabilities, organizations can also access tailored solutions delivered by Snowflake and our partner ecosystem and take advantage of the growing amount of financially relevant data that is natively available in the Snowflake Marketplace. With Snowflake’s Financial Services Data Cloud, financial institutions can thrive in a data-intensive, highly regulated, and competitive environment.