Banks record millions of transactions daily and these entries are real-time in nature. Capturing and recording such a huge chunk of data is a challenging job for bankers. Banking analytics birthed from sophisticated big data analytics software help banks by providing a platform where these transactions can be recorded systematically and analysed.
Advanced analytics is enabling superior performance in organizations willing to make the proper commitment with the right tools and software. Across all industries, not just finance, companies that are more analytically driven realise financial growth three times higher than their less analytical competitors, according to a recent report.
Here are some of the many ways in which superior banking analytics is helping the banking sector.
Fraud is becoming an area of big concern for every sector and for banking and financial firms, it can cost a lot to them. Digitisation has paved way for cybercriminals to commit more complex fraud that cannot be detected as easily by traditional banking security technology. Thus banks need intelligent systems and tools to deal with them.
Predictive analytics, machine learning, big data, data mining, and stream computing, therefore, are the key tools that have emerged in the fight to better identify fraud. Analytics can be used to recognize fraud that is not very obvious and, subsequently, predictive analytics can be implemented to analyse fraud patters further.
It’s safe to say that analytics helps in further fortifying the security so as to safeguard customers and banks against fraudulent activity.
Customer acquisition and retention
Predictive analytics helps in the process of optimised targeting, making it easier for banks to instantly identify high-value customer segments most likely to respond to their services. Customer bases can be further expanded upon by zoning in on acquiring the right type of customer. Based on a recent report, it was found that banks that had adopted predictive analytics had an increase of about 10% in new customer opportunities over a year.
Customer retention is also an area banks need to focus on in order to reduce customer losses. Loyal customers need to be rewarded and customer attrition needs to be minimised. Predictive analysis helps identify which customers are willing to switch to any other bank and the reason behind their decisions.
It examines a customer’s activity, spending, past services, and other behavioural patterns to predict the likelihood of a customer wanting to discontinue services anytime in the near future. With predictive analytics, banks are now able to use sophisticated data analytics platforms to glean useful insights – with the ultimate objective of concocting effective strategies to retain and acquire customers.
Better liquidity planning with banking analytics
Predictive analytics can help banks track past usage patterns and the daily coordination between the in- and out-payments at their branches and ATMs, hence predicting the future needs of their potential customers.
Optimal management of liquid assets can result in a bank earning extra income and a formidable analytics plan can help obtain an overview of future changes in investment and liquidity options. Liquidity planning is one of the biggest challenges for any bank today. With so many aspects of the commercial space going digital and so much of cash moving via ATMs and cash counters, liquidity planning is anything but easy.
However, analysing customer behaviour along with social insights can help in predicting cash demand. Such insights at an individual level can help maintain a complete understanding of daily inflows and outflows of cash while maintaining liquid assets exclusively to extents that are necessary – generating maximum returns for the bank.
Improved application screening and collection processes
Predictive analysis in banking can help process huge volumes of applications, without excluding important variables, without delays or errors. The results are very much accurate and authentic and can be used to further fortify the application screening process.
Moreover, banking analytics offers clear benefits when it comes to the effectiveness of collection processes. Banks can attain a better understanding of their portfolio risk and thus improve the productiveness of the collections process. Most importantly, analytics can help banks identify customers who would be at most risk in the future and facilitate the ability to provide guidance regarding what actions should be taken to achieve optimal results.
Advanced analytics in banking has evolved considerably in the last few years. Most banks can articulate an analytics strategy and have incorporated – or are in the process of implementing – superior data analytics software like SAS with the assistance of SAS consultants and administrators. However, in many cases, there is a disconnect among the analytics efforts and business goals.
Among the banks surveyed in a recent report, only 30% have effectively matched their analytics efforts with their business goals. Therefore, when you are choosing your tools to gain a competitive edge over your rivals, ensure that you evaluate where best your data can be effectively analysed. Make sure the data analytics platform employed and adopted at your organisations suits your needs and catalyses your push to another level of operations.
For more information on banking analytics and how it is changing the landscape of the finance industry, reach out to us.