Banking Analytics: Unlocking Data Opportunities in Finance
Banking analytics is answering the finance industry’s most pressing questions: Is this transaction likely fraudulent? Should this customer receive this credit card offer? How can we reduce costs and improve profitability? Armed with real-time information, financial institutions are better able to navigate an increasingly complex and competitive industry. In this article, we’ll explore why more organizations are prioritizing data analytics and how data is being used in banking today.
Advantages of Using Data Analytics in Banking
Banking analytics involves using AI and machine learning to analyze massive amounts of data from a variety of sources, both internal and external. Armed with this ability, financial institutions are powering growth and limiting their exposure to risk. Here are just a few advantages organizations gain when using data analytics.
Identifying sources for growth
Boosting customer lifetime value requires an in-depth understanding of customer behaviors and desires. Advanced analytics identifies patterns in data sets to create micro-segmented customer groups. This allows marketers to reach out with highly personalized product recommendations and promotions, applications for developers to build curated digital experiences, and financial advisors to determine Next Best Action based on their clients’ preferences.
Many manual processes in the finance industry are ripe for automation. Automation reduces staffing requirements, increases accuracy, and streamlines the customer experience. One example is loan approval. Using AI-powered analytics, algorithms can aggregate data from sources such as credit agencies and enrich it with other data, such as social media activity, to provide a loan decision faster.
When fraud occurs, banks often shoulder the loss. For example, a green light to authorize a charge on a credit card can have significant consequences over billions of transactions per day when fraudulent charges get through. Behavioral analytics, predictive analytics, and machine learning technologies can help financial institutions more quickly and accurately recognize an unauthorized purchase.
Improving the digital banking experience
Increasingly, banking activity happens across multiple channels including desktop, mobile, and in-person engagement. Using data analytics, banks can better understand how their customers access their services, providing a more consistent customer experience across all touchpoints.
Banking Analytics in Action
Data analytics is revolutionizing how financial institutions do business. Let’s look at six ways banks are using data to capture opportunities for growth and reduce risk:
Developing a clearer understanding of how customers use their existing financial products can help banks be more effective with their cross-selling and upselling efforts. For example, using customer data, banks can identify accounts that hold promise for conversion to a higher membership level or the purchase of advisory services.
Personalized marketing and financial product recommendations
Marketing the right product to the right individual at the right time is a deceptively simple formula that’s incredibly challenging to get right. Banking analytics programs sift through data sets to identify high-potential prospects for new banking products. Data sources include consumer demographics, purchasing behaviors, credit card statements, and information on the financial products a customer already has and how they’re being used.
Traditional credit risk modeling fails to make use of much of the highly relevant data available. Modern analytics approaches supplement conventional data, such as loan history, sources of income, and credit ratings with additional information gathered from social media sites, utility bills, and more to make more informed decisions on how, when, and if to extend credit.
Customer-facing virtual assistants
AI-powered personal assistants can provide customers with quick answers to questions and provide instant access to information on financial products and services. Recommendation engines for financial products such as credit cards can take customer-provided inputs about what type of rewards they value and their spending patterns to provide personalized product offerings.
Banks and other financial institutions collect and analyze data with value to retail, advertising, and other industries. Packaging and selling data products can be a valuable, ongoing source of additional revenue.
Identifying areas for improvement/automation
Data analytics can help financial institutions achieve substantial reductions in their operating expenses. By identifying inefficient internal processes, such as the time it takes to process loan applications or open new accounts, financial institutions can provide a better customer experience and improve productivity.
Advance Your Banking Analytics Initiatives with Snowflake
The Snowflake Financial Services Data Cloud helps financial organizations break down data and technology silos and thrive in a data-intensive, highly regulated, and competitive environment. With the help of Snowflake’s capabilities, tailored solutions, and our partner ecosystem, organizations can run robust banking analytics initiatives. Thanks to Snowflake’s sophisticated cloud security technologies, robust built-in security features, and government and industry compliance capabilities, you can focus on analyzing your data, not protecting it.
Try Snowflake free for 30 days and experience the Financial Services Data Cloud that helps eliminate the complexity, cost, and constraints inherent in other solutions. Available on all three major clouds, Snowflake supports a wide range of workloads, such as data warehousing, data lakes, and data science.