Driving banking to the future with banking analytics
Banks are one of the oldest institutions in human history. From the very first civilisations to the present day, banks have always existed in some form or another and have been instrumental in advancing the economy, driving innovation, and nurturing businesses.
Although banks have existed for many centuries, their fundamental principles have remained the same; accepting deposits from surplus and lending to fill deficits.
Along the way, they have adopted the latest technologies to support their core functions. From the first banknotes to modern, contactless payment solutions, banks have been at the forefront of financial evolution.
That said, ever since the advent of the internet and the birth of the information age, banks have come under significant pressure to modernise their processes and operations to meet changing consumer preferences, comply with complex regulations, and tackle sophisticated fraud schemes.
The innovations that have caused a fair share of challenges have also created the solution to tackle them, and that solution is banking analytics. With advances in computer technology, artificial intelligence, and machine learning—modern data analytics solutions can help overcome the contemporary challenges that banks are facing.
In this post, we will explore how banking analytics can power the banks of the future and help them overcome the challenges ahead.
Why is data analytics important in banking?
Through judicious use of data analytics, banks can improve customer service and foster innovation, which is crucial for retaining customers and eventually raising revenue while cutting costs.
Thanks to data analytics, banks can personalise experiences for customers. Analytics platforms can break down and segment customer data to create detailed profiles on each customer and banks can use the information to provide personalised services to new and existing customers.
Banks can gain better insight into customer behaviour by analysing actions, like channel transactions, to better understand how customers operate in real-time. The insight into customer actions allows banks to tweak existing services to better suit their customer’s needs, which improves retention.
Data analytics can also reduce operating costs related to compliance management, fraud detection, and credit risk.
A worthy adversary to banking fraud
For as long as banks have existed, fraud has plagued them. While banking fraud in the past included disguises and counterfeit notes, modern fraud schemes have taken it up a notch.
With the power of the internet and some clever pieces of technology, modern fraudsters can scam individuals out of thousands of dollars without ever leaving their sofas. In 2020, global losses from payment frauds involving banks reached $32.39 billion and are estimated to reach $40 billion by 2027.
It doesn’t end there, banks now have to deal with more elaborate and sophisticated identity theft attempts and more accurate counterfeit notes. Identity theft was the most common type of bank fraud in both 2018 and 2019.
Banking analytics, however, can help banks ensure maximum protection against these frauds. For example, some leading banks have been using anomaly detection algorithms, powered by artificial intelligence, to detect potentially fraudulent transactions across their payment systems.
Modern biometric authentication systems and facial recognition systems are also known to be effective against identity theft when implemented with machine learning to detect abnormal activities in customer accounts.
A powerful credit risk analysing tool
The global financial crisis of 2008 helped banks realise the need for more robust credit risk assessment tools to minimise risk exposure. They found out the hard way that conventional risk assessment techniques do not always produce accurate or reliable results.
This prompted banks to look for more innovative and efficient credit risk management processes. Data analytics has emerged as a powerful tool to help banks power more efficient ways to manage their credit risks.
Banks can build more accurate and comprehensive financial profiles of their prospective borrowers using banking analytics tools. These profiles help predict and reduce instances of loan defaults.
These tools can also help banks lend to the correct type of borrowers, reducing credit risks.
Powering a novel approach to regulatory compliance
Banks are one of, if not, the most important institutions in an economy. This is why they are governed by some of the most complex and stringent regulations. In addition, the regulatory landscape is in constant flux with regulators introducing new guidelines and amending existing ones consistently.
To navigate this dynamic regulatory landscape, banks need to be on top of every single regulatory change, which can be hard to achieve with your teams. By nature, people are prone to occasional error, which makes regulatory compliance an arduous task.
Thanks to advances in data analytics, machine learning, and artificial intelligence, banks can automate their compliance process. These RegTech solutions can power proactive compliance processes by automating compliance documentation, regulatory horizon scanning, and compliance monitoring.
Banking analytics is the future of banking
As the lifeblood of an economy, banks across the globe need to adopt disruptive technologies to combat contemporary challenges. Banking analytics is the tool banks need to prepare for the future.