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.
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.
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.
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.
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.
Regulations are like traffic lights. They are there for our protection but they can, nonetheless, slow down our momentum. If you work in the banking industry especially, you may have a better understanding of how regulation weighs heavily on financial organisations.
From edicts on corporate governance to CCAR, there are plenty of rules banks need to follow.
This presents a challenge in the form of rising costs. In fact, research shows that banks spend over $270 billion a year on compliance, which is equal to 10 per cent of their total operating costs. Moreover, the cost of regulatory compliance is set to double by 2022.
Banks need a solution that can make regulatory compliance a more cost-efficient part of their work. This is where SAS banking analytics can help finance organisations. In this post, we look at why financial analytics platforms are the solution banks are looking for.
Regulatory compliance causes data to balloon in volume.
When regulators pass a new law, it generates a new wave of corporate data. A wave that upends current data governance, data collection, and reporting mechanisms.
When this happens, banks are in a lurch. They have to implement new procedures, policies, and teams to ensure they are complying with the law.
There is also the issue of data management.
New regulations expand a bank’s data lake. Sounds good, right? Well, not if you have the wrong tools in place. Without the right tools, banks can’t keep up with the volume of new data or make sense of it.
Banking data is voluminous and complex because of its different sources. Data sources include transactional data, operational data, reference data, and security data. There is also the fact that each team manages its own branch of data.
Analysts must work with these different types of data to meet compliance regulations, which is a slow, painful procedure. Traditional analytics platforms need several data analysis cycles to complete operations, prolonging analysis and driving up costs.
What banks really need is an analytics platform that can help them adapt to new regulations quickly and reduce compliance costs. The ideal data analytics solution will reduce compliance costs and improve core operations.
Sounds complicated, right? Well, not if you have the right data analytics solution, which is why SAS banking analytics is an invaluable investment.
SAS banking analytics can help you resolve several compliance-related issues. It can meet the needs of banks and other large corporations, making it better equipped to handle the large volume of data stored in their databases.
SAS analytics uses technology like AI, machine learning, and cloud computing to help you optimise certain data collection and analysis processes to make compliance more efficient.
Besides optimising data collection procedures, banking analytics from SAS can optimise reporting procedures. You can create an infrastructure that merges data modelling, measuring, and reporting to better manage risk and regulatory management.
SAS analytics platforms support compliance for most regulatory risks, including regulatory capital, and liquidity risk. They can reduce the length of analytics cycles, improving operational efficiency. By speeding up processing time, we can also reduce the cost of compliance.
Additionally, data management becomes more efficient because it’s much easier for research teams to store data and derive useful information from it.
Along with improving regulation, banks can also improve governance with analytics. SAS analytics provides a risk profile that covers the entire network of the organisation. This ensures a level of transparency, which is difficult to manage using other means.
Better transparency makes it easy to meet regulatory compliance demands and manage internal risk, which can avert potential disasters.
As the banking industry faces tighter regulations, data analytics platforms are the key to helping the industry navigate the complex regulatory environment.
That, however, just scratches the surface of what SAS analytics platforms can do.
SAS solutions can also resolve other problems the banking industry faces, like fraud. Moreover, banking analytics can help banks improve customer service by turning it into a more personalised experience. If used properly, SAS banking analytics can resolve many of the issues the banking industry faces, especially the burgeoning cost of regulation.
To learn more about SAS analytics and what it can do for different industries, visit Selerity.