Why is Predictive Analytics important?
Customers are always expecting more and more from businesses, including financial institutions. They expect their needs to be met, their complaints heard, and their experiences optimized for their benefit.
To keep up with customer demands at a pace that they expect, it has become essential to incorporate data science in banking for on-demand customer intelligence and insights. This allows for better decision-making and the capabilities to transform asset and facility management.
Banks may already have substantial insights from descriptive analytics and diagnostic analytics with beautiful visualizations, however it is now critical to go beyond and adopt advanced analytical methods like predictive analytics.
Predictive analytics is a subcategory of data science that uses historical data to create and train machine learning models for forecasting future outcomes to improve customer experience and operational efficiency. There are other statistical methods as well, like prescriptive analytics and cognitive analytics, but they will not be the focus of this article.
What are the Uses of Predictive Analytics in Banks?
1. Fraud management
Fraud spells bad news from both the company’s and customer’s perspective. On one hand, it can negatively impact a business and its profitable growth. On the other hand, it can put customers at risk and threaten their privacy. Either way, fraud is a risky occurrence that banks can’t afford to ignore.
Through machine learning, one can reliably identify suspicious patterns and detect fraud well in advance. An enterprise-grade platform like Analance can comb through both structured and unstructured historical data to determine the patterns of fraudulent transactions and raise alerts for potentially risky transactions.
This allows banks can launch necessary investigations and adopt other preventative measures. In the long run, this can save millions of dollars and help mitigate fraud.
2. Predicting credit card and loan default risk
In the same vein, predictive analytics can also help banks identify accounts that are likely to default on their credit card and other loans. Considered one of the biggest concerns in the industry, loan default can become a preventable issue through machine learning.
With predictive analytics, determine the patterns of high-risk borrowers by analyzing historical trends that led or did not lead to loan repayments. Several dimensions can be considered, including payment history or customer traits.
Not only will this type of insight help reduce the financial risks that often come with unpaid loans, it can also streamline the loan approval process, even automating it for a more efficient procedure.
According to a study by McKinsey, a bank optimized its corporate credit assessment using advanced analytics, consequently achieving an 80% higher productivity.
3. Transforming customer service
Customers are the lifeblood of most businesses, banks included. This is enough reason to invest in innovation and digital enhancements to continually improve customer experience, but it can also be considered as a way to reduce churn and differentiate your bank from other financial institutions.
After all, 72% of banking customers who had a negative customer service experience either engaged less or switched banks altogether, according to Cisco.
Incorporating data analytics in banking can greatly enhance your organization’s customer service. You can use machine learning to provide the right information at the right time, utilize chatbots to provide timely responses, and employ predictive modeling to provide personalized experiences. And of course, all of this would be easier with a 360 degree view of the customer.
4. Customer acquisition and retention
Hand in hand with customer service is acquisition and retention. To maintain a customer base that helps your organization meet its business goals, it’s important to acquire quality customers and engage them enough to retain them.
The key here is personalization by segment, which is more than possible with advanced analytics. Predictive models can help you identify high-value leads and launch individualized communications and personalized campaigns to acquire these segments.
On the retention side, you can benefit from machine learning to come up with and promote the right loyalty programs to the right customers. Insights gained from predictive analytics can also inform product development, so the organization can focus on products that meet customer needs and at the same time, generate maximum revenue.
Incorporating Predictive Analytics
While the scenarios listed above are just some of the many examples of predictive analytics in banking, the advantages are crystal clear.
In fact, incorporating predictive analytics in just one business area can create ripple effects across the organization: improving data literacy, streamlining data collection processes, and adopting the mindset of making data-informed decisions.
The analytical approach is especially important in an industry like banking (and the financial sector in general), which is often characterized by information explosions and volatility.
Just imagine the massive amounts of data banks handle on a regular basis: customer accounts and preferences, credit scores, ATM and online transactions, customer feedback, interest rates, and various macroeconomic variables.
In order to effectively utilize and make sense of all these data sources, one would need some machine learning and predictive analytics magic. Basic reporting just won’t do the trick anymore. You have to go all-out with more advanced capabilities, like predictive analytics to start, in order to gain the confidence needed to support and make crucial business decisions.