By Fiona Villamor on April, 24 2019

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 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.

The infographic below illustrates how banks can effectively use machine learning capabilities and data analysis for predictive analytics in the banking sector.


How the banking industry can benefit from predictive analytics: Fraud Management | Predicting Credit Card and Loan Default | Transforming Customer Service | Customer Acquisition and Retention

Download the infographic here.

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, 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. 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 way, banks can launch necessary investigations and adopt other preventative measures. In the long run, this can save millions of dollars and help mitigate fraud.

Must-read: Fraud and scams are always evolving


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.

A platform like Analance can 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.

In fact, a study in association with the London Institute of Banking & Finance showed that 38% of banks feel that predictive analytics have significantly improved their credit assessment process, making it faster and more accurate.

“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.” Tweet this >

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 an integrated view of data (that means no more data silos!). Providing quality customer service requires a 360-degree view of the customer, possible with analytical platforms like Analance that has capabilities built-in from BI to AI.


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 analytical platforms like Analance can also inform product development, so the organization can focus on products that meet customer needs and at the same time, generate maximum revenue.

Related: 4 ways the healthcare industry can benefit from Predictive Analytics


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 must 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.

Find out how Analance™ can help you and your organization leverage predictive analytics to enhance customer engagement and loyalty, optimize operations, and mitigate risks. Download the infographic here or get in touch with us today.



Fiona Villamor

Fiona Villamor is the lead writer for Ducen IT, a trusted technology solutions provider. In the past 8 years, she has written about big data, advanced analytics, and other transformative technologies and is constantly on the lookout for great stories to tell about the space.