October 27, 2021

Article
3 min

Advanced Analytics Provide Faster, Better Insights to Financial Institutions

With the right tools, complex data becomes an essential resource for market and competitive analyses.

Christine McPherson

The rapid adoption of digital banking has transformed financial services for individuals and institutions alike. New competitors are disrupting the industry, consumers have moved much of their financial business online, and apps such as Venmo and Zelle have pushed transactions outside the traditional banking environment.

These changes generate massive amounts of data that, in turn, present challenges and opportunities for financial institutions. From a cybersecurity perspective, banks, insurers, investment firms and other institutions have much more customer data to protect. At the same time, they have access to vast amounts of data that, with the right analytics, can become valuable decision-making inputs. 

Financial institutions are adopting advanced analytics — powered by artificial intelligence and machine learning — to better understand their own risk, performance and customers.

Machine Learning Turns Unstructured Data into a Valuable Asset

Traditionally, analytics has relied on manual manipulation of data. That’s fine for limited amounts of structured data, but large, unstructured data sets are a different story. For example, a bank could glean valuable insights by conducting sentiment analysis on customers’ reviews and social media engagements — but powerful machine learning would be required to make that analysis feasible.

Many institutions already have the data they need, especially if they are developing applications and building their own digital tools. Yet they still must consolidate that data to make it usable. As data becomes more complex and diverse, institutions need databases or data warehouses that can incorporate various types of data, along with software to structure and analyze data quickly.

Real-Time Insights Help Institutions React to Market Shifts

For many institutions, leveraging real-time data is a challenge. Machine learning and algorithmic utilization can overcome that hurdle by facilitating faster, in-depth analyses. That, in turn, helps institutions enhance their decision-making and reduce risk.

For example, better gap analysis helps institutions understand whether their performance will improve if they take certain steps or invest in certain areas. Competitive analysis illuminates shifts in the market so institutions can respond proactively. 

Analytics also lets institutions develop deeper insight into customers. In the past, banks relied on surveys and gut instinct to guide the creation of new products. Today, they can use data-driven insights to fine-tune product development. Granular insights enable institutions to offer products and services to customer subgroups whose needs might not have been visible in the past.

Financial Institutions Minimize Losses with Faster Fraud Detection

Advanced analytics tools have been a major factor in speeding up fraud detection and reducing associated losses. As many consumers have discovered, the increasing digitization of commerce has made it easier for criminals to steal banking and credit card information online. 

When institutions have the capability to understand a consumer’s typical spending behavior, they are better able to recognize potentially fraudulent activity. The faster institutions can catch this activity, the faster they can alert customers and minimize their own losses.

Responding to disruption requires much faster time-to-knowledge than financial institutions have needed in the past. Today, they must be able to understand consumer behavior, competitive trends and market shifts quickly — and in ways that can reliably inform critical business decisions.

Story by Christine McPherson, a Sr. Field Solution Architect focused on Analytics. She supports customers and sales teams by educating and advising them on CDW’s Modern Data Solutions portfolio.