The Value of Predictive Analytics in Banking

According to a recent report published by Allied Market Research, predictive analytics is set to generate around $5.43 billion in the banking industry by 2026. Growing at a CAGR of 20.8%, this rapid growth is mainly driven by the availability of IoT-based devices, the surge in fraudulent activities, and the evolving needs of clients. Here are some of the ways predictive analytics is being applied in banking:

Credit scoring

Credit scoring models that are supported by predictive analytics are designed to determine the likelihood of a customer defaulting on a credit obligation and becoming delinquent or insolvent. Petal Card details five major credit score factors: payment history, credit utilization, credit age and history, credit mix and account types, and credit inquiries. Payment history accounts for 35% of a person’s total credit score and pertains to how timely an individual makes his payments. Credit utilization refers to how much of your available credit is currently being used. Credit age and history, on the other hand, are about the age of your accounts. Credit mix and account types let lenders see how applicants handle all kinds of credit accounts and loans. Finally, credit inquiries can make up 10% of a person’s credit score and can be categorized as either a "hard inquiry" or "soft inquiry." Other factors, like reporting errors, missing payments, and utility bills also come into play.

Predictive analytics takes all these into account to decide on whether to accept or reject a customer, or increase or decrease loan value, interest rate, or term. In this way, banks can make the best possible decision given the wide array of data factors to consider.

Fraud detection and prevention

Following the world’s massive shift to cashless transactions and online shopping came the rise of online fraudulent activities like phishing, application fraud, identity fraud, and card skimming. A recent study published by Globe Newswire noted how a staggering 86% of global consumers have fallen victim to identity theft and fraud last year. Around 51% of the 2,600 surveyed consumers also reported experiencing a rise in phishing activity during the height of the pandemic.

But with the help of predictive analytics, financial institutions can identify potential fraud by analyzing common operational patterns, purchases, and payments. They can also use predictive analytics to create monitoring systems that scan data continuously. Triggered by specific actions or the results of sampling analysis, these systems can spot suspicious log-ins and reduce bad check scams significantly.

Risk hedging and collection

Late payments have always been one of the biggest problems banks face. Thankfully, predictive analytics can help financial institutions manage them better. This technology deals with late payments by making it easier for banking professionals to red flag certain clients even before they miss three or more payments. Analytical tools also assist banks in building their clients’ portfolios and hedging risk. As noted in our previous post on ‘How AI is Powering the Future of Banking’, with their ability to think and respond like human experts, cognitive systems and predictive analytics can provide optimal solutions based on available data in real-time.

These technologies can help banking professionals decide if it's more ideal to set a higher interest rate or introduce a new payment schedule to customers who may be in a financial bind and cannot pay on time. Predictive analytics can also be leveraged to streamline collection processes. It can be utilized to effectively distinguish between various portfolio risks and efficiently segregate risky customers from risk-free ones. These capabilities enable banks to devise actions and strategies that can achieve positive collection results.