4 Ways Predictive Analytics Can Help The Banking Sector- Blog

What Is Banking Analytics?

Banking analytics is the practice of using data and statistical analysis to inform and improve decision-making within the banking industry. This can involve analysing data on customer behaviour, financial performance, market trends, and various other factors to gain insights that can help banks serve their customers better, identify new business opportunities, and manage risk.

Banking analytics can be applied in various areas, including marketing, finance, risk management, and operations. It can also involve using predictive analytics to forecast future events or trends.

Why Do Banks Need Predictive Data Analytics?

Predictive data analytics can be particularly useful for banks in helping to identify potential risks and opportunities. For example, a bank may use predictive analytics to forecast potential changes in market conditions that could impact its business, such as interest rate fluctuations or changes in the regulatory environment. This can help the bank proactively manage its risks and make informed decisions about allocating resources.

Banks can also use predictive analytics to identify potential new business opportunities, such as identifying potential new customers or identifying potential upsell or cross-sell opportunities with existing customers. By using predictive analytics to analyse customer data and identify trends or patterns, banks can tailor their marketing efforts and product offerings to meet their customers’ needs better.

The Role of Business Analytics in Banking

Myriad challenges beset today’s banking sector – heavy regulations, evolving customer needs, increasing transaction volumes, increased high-tech financial crimes and rapid technological changes. Managing these challenges requires timely and deeper insights into risk, customer relationships, costs, revenues, and other key parameters. How do banks get access to such insights? The answer is- using Predictive Analytics.

Predictive Analytics is a stream of advanced analytics that uses new and historical data to forecast activity, behaviour, and trends to predict the future. Data analytics in banking involves data mining, modelling, statistical analysis techniques, and automated machine learning algorithms to make predictions. It helps organisations discover business issues in real-time and address them at the right time to get the best outcomes.

Application of Predictive Analytics solutions in the banking industry include the following:

1. Credit Scoring: Advances in technology have enabled financial lenders to reduce lending risk by using various customer data. Employing statistical and machine learning techniques, available data is analysed and boiled down to a single value known as a credit score representing the lending risk. The higher the credit score, the more confident a lender can be of the customer’s creditworthiness. Credit scoring is a form of artificial intelligence based on predictive modelling that determines the likelihood of a customer defaulting on a credit obligation, becoming delinquent or insolvent. The most significant benefit of credit scoring is the ability to help make decisions quickly and efficiently, such as accepting or rejecting a customer or increasing or decreasing loan value, interest rate, or term.

2. Fraud detection: With the advancement in cashless transactions, most physical and time-consuming procedures have been replaced by fast and convenient real-time payments. But all this convenience has led to a rise in fraudulent online activities like phishing, application fraud, identity fraud, and card skimming. Combining multiple analytics methods can act as effective anti-fraud solutions and prevent criminal behaviour using improved pattern detection. Two solutions developed by CRIF, a credit information company in India, are IDENCHECK and SHERLOCK by CRIF. While the former is designed to enhance your existing KYC verification processes by providing digital capabilities to check against Government and other public databases, the latter brings powerful anti-fraud solutions to help detect and investigate application and identity frauds like never before.

3. Collections: Every bank has a set of customers who pay behind time, and collections become an integral activity. What it needs, though, is channelising energies in the right direction. Data Analytics in Banking analytics helps banks distinguish between the various portfolio risks by optimising the collections process. It allows banks to segregate risky customers from risk-free ones effectively. This can help banks devise actions and strategies to achieve positive results.

4. Cross-selling: Efficient cross-selling of products can happen by analysing customer behaviour patterns at places where multiple products are offered. This analysis can help identify which specific products are to be sold to whom and assist banks in channelising their sales and marketing efforts. And all of this results in more effective cross-selling, thus increasing profitability and strengthening the customer relationship. Today, retaining one profitable customer is a big task for banks; cross-selling another product to an existing customer helps a lot.

The above benefits are just a fraction of what banks can achieve using Predictive Analytics. To gain a competitive advantage, banks should recognise the importance of data science, incorporate it into their decision-making process, and develop strategies based on actionable insights from their customer’s data.