As well as everyday life. Artificial intelligence (AI) is making its way into banking. Computers are getting more powerful, datasets are becoming bigger and people are connected wherever they go. There are perhaps no industries that have more data than banking. With millions of transactions happening around the world each day, the industry is ripe for innovation through data.
One of the ways banks are achieving this is through what is known as predictive analytics. In this article, we will explain what predictive analytics means and how banks are using it to both improve their own systems and benefit the consumer.
What is Predictive Analytics?
Predictive analytics is an application of artificial intelligence (AI) that makes use of computer models to forecast future events. By using large amounts of historical data, algorithms built into analytics programs can attempt to determine what will happen next. One of the best examples outside of banking is Amazon.
Amazon uses predictive analytics to recommend products to buyers that they might want to purchase next. They do this by analyzing data of similar customers and seeing their purchase patterns and turning that into insights and decisions. These models are not always perfect, but they learn as they gather more information (an AI application known as machine learning).
According to Deltec Bank, Bahamas – “Predictive analytics can be deployed in banking to help the institution and also to provide better services for customers to manage their accounts.” A variety of sources such as transactions and demographics can be combined to make predictive decisions.
Banking on predictive analytics
There are numerous applications of predictive analytics that make it an ideal tool for the banking industry, where institutions have access to a lot of data.
Over 1.4 million fraud-related cases were reported in 2018 alone. Fraud is one of the leading causes of cybercrime, especially where digital technology is being used more often. With that in mind, predictive analytics can help financial institutions take preventative action.
Using data collected over time on customer transactions, banks can identify patterns and where they look irregular. Machine learning is applied against the patterns to provide insight on behaviors that show signs of fraud. The algorithms used can analyze millions of rows in real-time which would be impossible for humans or traditional computing to undertake.
Customer service agents can speak with clients straightaway to discuss potentially troublesome transactions and verify them.
This use of predictive analytics is not necessarily new but is one of the most common examples used in banking. Credit scoring is a method used by banks to determine the risk of a customer. This would normally use their credit and financial history to make a decision on whether they should be accepted for a loan, credit card or other product.
A predictive analytics platform will process all the data held on a customer via an algorithm and calculate the risk the bank would take if they decide to underwrite them. Some banks have even started applying social media or e-commerce data into their scoring processes to improve accuracy by using more information.
Technology firm SAS uses Credit Scoring for SAS Enterprise Miner to help institutions like Piraeus Bank Group. The bank has used the software to create better risk models and improve the speed of their data analysis by 30%.
From the consumer perspective, predictive analytics can help with income and expenditure budgets. For example, they can use data to predict when income and expenses will show on your account and provide insight accordingly. The aim of this is to help customers prevent any financial problems from occurring. For example, if you are low on money for a bill due in 5 days’ time, a predictive notification might allow time for customers to transfer funds from another account or request help.
Customer Acquisition and Targeting
Just like any other business, banks need to find ways to optimize acquisition strategies for minimal cost and maximum impact. There will be some customers that fit the profile of the bank’s products and many others who don’t.
Predictive analytics can look at all the demographic and transactional data of the existing banking customers. In doing it, decisions can be made as to whether a potential new customer fits the profile of what might be considered “good.” For example, if customers matching the profile of a new lead tend to be those who save regularly, always pay credit card bills on time and have a regular salary, the bank will likely predict that the potential customer will do the same.
Whilst predictive analytics can never be 100% accurate in the acquisition, it usually does a good job of weening out the customers that wouldn’t be a good fit.
Moving on from that, predictive analytics will also tell banks the likelihood of a customer visiting a branch, making a phone or needing customer support. Banks can plan accordingly based on the insight gathered from those predictions. Some analytics platform will help banks know the best channels to reach customers at different stages in their lifecycle.
All these different types of predictive analytics will help banks create an optimized and targeted customer journey.
There are many risks in financial services. We have already spoken about fraud but beyond that, there could be cybersecurity, compliance or operational risks as well. Using historical data and predictive analytics, banks can identify previous times that risks have not been properly dealt with and ascertain the root cause. This helps to proactive manage future risks.
Processes like Know Your Customer (KYC) and Money Laundering (ML) are traditionally time-consuming and laborious for banks. Data and predictive analytics can help speed up the procedures.
Predictive analytics is being used in modern-day banking to carry out important functions. It allows banks to engage better with their customers whilst keeping up with the competition posed by rival fintech firms. Banks hold a unique advantage with the data they hold, and it is important that they put it to good use.
Disclaimer: The author of this text, Robin Trehan, has an Undergraduate degree in economics, Masters in international business and finance and MBA in electronic business. Trehan is Senior VP at Deltec International www.deltecbank.com. The views, thoughts, and opinions expressed in this text are solely the views of the author, and not necessarily reflecting the views of Deltec International Group, its subsidiaries and/or employees.
About Deltec Bank
Headquartered in The Bahamas, Deltec is an independent financial services group that delivers bespoke solutions to meet clients’ unique needs. The Deltec group of companies includes Deltec Bank & Trust Limited, Deltec Fund Services Limited, and Deltec Investment Advisers Limited, Deltec Securities Ltd. and Long Cay Captive Management.