Banks are the safe house for money. The importance and reliability of bank are paramount and the banking sector has been at the center of constant development and change.
To deal with a myriad of applications the basic building blocks for a bank is data.
When there is data, analysis follows next.
More data means more users. Delighting offers, special compensations, personalized experience is among the few hacks the banks deploy to create a loyal user base. This is where predictive analysis comes into the picture.
Technology has always influenced commercial banking to enhance and personalize the customer experience like never before. ATMs, automated deposit and printing kiosks, online banking are among the evident examples of the fusion of technology and the banking sector.
‘Machines can do things cheaper and better. We’re very used to that in banking, for example. ATMs are better than tellers if you want a simple transaction. They’re faster, they’re less trouble, they’re more reliable’- Geoffrey Hinton.
Data analytics, Machine learning, and Artificial Intelligence are all set to change banking services. The public sector banks are using statistical techniques and various machine learning algorithms to establish useful insights. Banks also use predictive analytics for fraud detection, customer retention, screening etc.
We discuss the primary use cases of predictive analytics in the banking sector.
With over 310 million active savings accounts in Indian banks alone, fraudulent transactions have increased exponentially. The Reserve Bank of India posted 1300+ fraud cases amounting to 5.55 crores as of 2018. Thus fraud detection has become the most critical concern.
To tackle this, banks are implementing a host of intelligent systems and statistical models like predictive analysis, machine learning, big data, data mining, for evaluating data to name a few.
Analytics is used to identify patterns in the data and predict the likelihood of occurrence of fraud by any customer.
Fraud detection techniques are all about establishing abnormalities which when compared with normal behavior seem ‘different’ or ‘alien’.
Data mining gives the financial service providers a lucrative tip-off which then turns to useful information in real time.
Thus, predictive analysis is an excellent way to track, analyze, and avert potential fraudulent activities.
With hundreds of applications flowing in every minute, it becomes cumbersome and exhaustive to screen every single application.
To skim through, banks use predictive analysis to process applications without compromising on necessary insights. The results are accurate and the time required is seemingly less.
Customer Acquisition and Retention
With over 2000 banks, the competition is fierce and the quest to retain customers is never-ending. Predictive analytics can help banks to track when an existing customer is looking out for its peers, and what could the probable reason be.
Analysis of customer spending patterns, investment choices, past service details, are among the many factors which are considered for customer retention.
Loyal customers deserve to be rewarded and any kind of customer dissatisfaction needs to be minimized. Often, such a vast database leads to unwanted customer loss.
Once the problem is identified, the banks leave no stone unturned to retain their customers.
The only way to measure brand value is customer feedback. When it comes to banks, predictive analysis ensures a smooth flow between banks and their customers giving them the desired services and products according to individual preferences and choices.
Customer Lifetime Value
LTV is a measure of how long an organization was able to retain their customers. From customer acquisition to retention, LTV score is also a direct measure of customer satisfaction.
With so many options available, finding a loyal customer is always a tough task.
Here’s how Predictive analysis helps:
Once the banks have a database, to begin with, predictive analysis helps to focus on categorizing the customers while focussing on methods to focus on customer engagement.
This helps to understand the factors which enhance customer engagement over a period.
So Let’s Summarize:
Aforementioned are just a few use cases of predictive analysis for the banking sector. Although the perks of predictive analysis are many, it requires expertise and skill to deliver.
Can predictive analysis along with numerous machine learning algorithms help to fight frauds and smoothen the customer experience? Only time will tell.