The Role of Data Analytics in Banking
Data is an extremely valuable resource across various industries, especially within the financial services sector. Along with new opportunities AI delivers benefits to employees and customers alike. Banks especially rely on data across the sales and marketing funnels and in fraud prevention. Banks were one of the first to adopt big data analytics which applies sophisticated analytic techniques to big and diverse sets of structured and unstructured data from various sources. Understanding how banks use big data requires familiarity with the tools used to collect, clean, and analyze the information.
What is big data?
Big data refers to the ever-growing volume of structured and unstructured data of various formats. With the growing quantity of unstructured data from various sources today, such data sets are beyond what traditional information processing systems can manage. Big data uses artificial intelligence and machine learning to analyze extremely large data sets for multiple sources. Once analyzed, big data can reveal patters, trends, and associations especially related to human behavior and interactions.
Why do banks need big data?
The term big data has been used for several decades. Today, big data is understood as analyzing large data sets and gathering insights to use in real-time. In a nutshell, by collecting, cleaning, and analyzing large quantities of data, banks can monitor risk, anticipate the behavior of their customers, and develop strategies that will provide better, safer, and more relevant services. The value of data lies completely in the way it is gathered, analyzed, and interpreted.
Banks and consumers who utilize finance products generate an enormous amount of data daily. Thanks to advanced analytics, analytics software has changed the way information is processed. With analytics, banks can now identify patterns and trends and use them to inform business decisions at scale by taking multiple pieces of information and creating a larger picture that can recognize patterns in customer behavior, purchasing decisions, and other key insights.
How are banks using big data?
Significant resources are required to process large amounts of data. For example, powerful servers and a team of technical experts is necessary to build and run analytics. Banks also need to ensure the right machine learning and artificial intelligence software are in place so they can be utilized by technical experts. Additionally, banks must invest in cloud-based software to host large amounts of data in a secure location without compromising data privacy.
Banks first adopted big data analytics for strategic planning and to identify market trends. As big data and predictive analytics allowed banks to quickly understand their customers and plan long-term on what products and services to offer and when, these new analytics tools enabled them to stay innovative and ahead of competitors.
AI, which is used in big data analytics, is used to identify data types, find possible connections among datasets, and recognize knowledge using natural language processing. This helps drive strategy, prevent customer churn, and identify new opportunities.
With the widespread digitalization of financial services, banks generate data from online banking portals, payment processing services, mobile banking apps, banking call centers, chat bots, and ATMs. Along with these sources, technology is enabling banks to acquire even more data rapidly. However, until now banks have failed to maximize on their data for their benefit.
AI and big data analytics are enabling banks to harness the potential of customer data and analyze it for their benefit. Although analytics trends can empower banks and give them a competitive edge within their market, only seven percent of banks are fully utilizing key analytics within their workflows.
4 ways data analytics is used in banking
Sales and marketing
Much of the direct marketing and sales efforts in the banking industry are driven by analytics. Analytics can be used to identify strategies that will generate the highest returns. It also helps bankers understand the segmentation of consumers across several categories and how to make cross-vertical marketing easier to manage.
Big data can provide insights into specific demographics. This empowers bankers to market with campaigns that are tailored to their specific needs and expectations based on the demographics. Big data enables banks to create more targeted marketing campaigns, which has transformed the sales funnel. More qualified leads can be pushed to the sales team and which lead is more likely to turn into a long-term customer is determined through the insights gathered from big data.
Preventing fraud
A primary challenge for banks is in identifying and preventing fraud. Bank deposit accounts are the targets of over $1 billion in wire fraud accounts, according to a recent survey by ABA Insurance. Credit card and loan applications only require some personal information, which makes it easy for fraudsters to commit fraud. Fraudsters will always try to find a way to insert themselves where there is a large movements of capital. The data and patterns recognized by big data analytics and AI can help banks to combat fraud by identifying consumer patterns.
Machine learning uses algorithms that can “learn” new information from the data it collects. The more data that AI has to work with, the more it learns and the greater insights banks can receive from their AI. AI can help prevent fraud by learning customers' spending and behavior patterns over time and alerting staff to any anomalies.
Fraud continues to evolve impacting all banks. With predictive analytics, banks can gather information about client activities to identify potential fraud. For example, AI and machine learning can spot patterns in consumer behavior and transactional data and then alert the bank or consumer when an unusual activity is recorded. Banks can then quickly identify and investigate the alert to know what necessary measures to take, saving time and money and improving levels of customer trust.
Understanding and anticipating customer needs
Digital transformation offers businesses more opportunities to interact with customers. Customers are increasingly demanding for brands to personalize their services and will stay loyal to companies that engage with them and understand their needs. This is especially seen within the banking industry where banks that can show clients they understand and can meet their needs are chosen over those that cannot.
In order to engage with customers, bankers must understand what drives people's behaviors and decision-making processes. Predictive analytics can paint a 360-degree view of each customer. This includes data into a customer's buying habits, interests, needs, risk assessment, and how likely or able they are to expand on an existing or apply for a new credit card or loan.
Traditionally, bankers would manually gather data and make educated guesses on clients. However, with predictive analytics, bankers can now identify a customer's needs accurately and effectively. They can even understand which customers they can profit from the most, which will be most loyal, and which are at risk of closing an account. This has been useful especially within the Microfinance sector, where financial institutions are expanding their services to those with little or no credit. With big data insights, banks can save time and resources by knowing where to focus more of their efforts and who their most valuable customers are.
More personalization
Personalizing customer interactions builds loyalty as many customers value a customized experience. Nearly half of customers have left a company for a competitor that better understood and delivered on their needs. Data analytics makes it easier for employees to navigate through the sea of digital data and understand customers' needs and desires. The analytics provides insight into a customer's history, behavior, and interests so that bankers can anticipate their needs. The data can also be used so that bankers can modify their services or processes based on changes in information.
Predictive analytics can also help banks segment customers into groups that share similar interests, expectations, and needs. This enables bankers to identify different strategies for each group of customers, specific to their needs. The data and insights gained from predictive analytics is valuable in determining where to spend the most resources and in identifying the best opportunities.
Moving forward, banks will rely on predictive analytics to help forecast events and for valuable insights into their customers and competitors. Predictive analytics helps banks make the most of digital data and save time, money, and resources. It provides a competitive advantage to banks through the insights gained in the market, on customers, and on competitors.
For more information about how AI can help banks, visit https://accern.com/bankers.
About Accern
Accern enhances artificial intelligence (AI) workflows for financial services enterprises with a no-code AI platform. Researchers, business analysts, data science teams, and developers use Accern to build and deploy AI use cases powered by adaptive natural language processing (NLP) and forecasting features. The results are that companies cut costs, generate better risk and investment insights, and experience a 24x productivity gain with our smart insights. Allianz, IBM, and Jefferies are utilizing Accern to accelerate innovation. For more information on how we can accelerate artificial intelligence adoption for your organization, visit accern.com