Banks and financial institutions generate a gargantuan amount of data every minute. Millions of transactions take place in the banking domain, so by its very nature, the industry is data intensive.
All this complex information falls under the ambit of big data which has been defined as “large, diverse and complex sets of data growing at an ever-increasing rate”. This data holds enormous potential for banks and financial institutions that want to garner a better understanding of their customer base, product performance, and industry trends.
With the technological advances in recent times, a large percentage of customers have begun using digital banking. The exponentially growing numbers of smartphones, tablets and other electronic devices have made it easy for customers to perform a range of activities: communicate with companies, research products and services, purchase items, offer feedback and perform banking tasks.
These activities can be used to create customer profiles that banks can analyse to monitor trends, predict customer behaviour and offer personalized services.
In this brief post, we will elucidate the advantages and challenges of using big data in the banking industry. We will also discuss several use cases of big data in banking. So, let’s begin without ado.
As per a report by Research and Markets, big data in banking was valued at $7.19 billion in 2017 and is estimated to reach $14.83 billion by 2023, growing at a CAGR of 13% during the period.
Here are the major advantages of implementing big data in banking:
Holistic View of the Industry
Big data analytics offers banks and financial institutions a complete picture of their industry, right from broader market trends and business process efficiency to customer behaviour patterns. This helps them make informed decisions and avoid drastic consequences.
Optimization of Business Processes
Big data, when combined with machine learning, can help banks analyse internal processes and take steps to optimize them. This way they can reduce operating costs significantly.
Personalised Customer Experience
Big data analytics can help banks and financial institutions study customer profiles and customer behaviour closely and create for them a super-personalized experience.
The sophisticated algorithms in big data analytics can pinpoint anomalies, detect fraud and any other malicious behaviour, thereby reducing risks considerably.
Now that we have discussed the benefits of big data in banking, let us dwell on the challenges involved in implementing big data in banking.
Legacy Infrastructure needs Upgradation
Most of the banking organizations are still relying on legacy infrastructure. As a result, they cannot cope with the constant influx of data, a prerequisite for running big data solutions. Banks that contemplate integrating big data with their systems need to overhaul their existing infrastructure and that’s not an easy task.
Difficulty in Collating Data
Banking institutions offer a diverse range of services. As a result, banking data is often variegated and stored in different departments. So, if a bank needs to create a customer profile based on his investment, it will be difficult because the deposits, loans, insurances of the customer are spread across departments. Collating all this data can be cumbersome.
Difficulty in making Data Usable
Even if the banks are able to collate customer data spread across departments, a lot of irrelevant data needs to be sorted out before the data becomes usable for processing and analysis. Banking institutions need to devise new methods for separating the data that’s potentially valuable from the one that isn’t.
Customer Privacy Concerns
The data used by big data systems remains anonymous at high level, though the bank can track the behaviour of individual customers if they want to. While this data helps in the detection of fraudulent activity, it can be a security threat if it gets into wrong hands. Such security concerns can be an impediment to the large-scale implementation of big data.
Let us have a look at some exciting applications of big data in the banking industry.
Customer Segmentation and Targeting
Customer segmentation is a common practice in all industry verticals and banks stand as no exception. Banks typically separate their customers into distinct categories using demographics. But this segmentation may not have the granularities needed to thoroughly comprehend the wants and needs of customers.
Big data analytics helps companies and these institutions take this segmentation to the next level by creating a detailed customer profile. This profile can take into consideration a wide range of factors such as:
By taking many such factors into account, banks can create a detailed profile for each of their customers. They can harness these profiles for comprehending their needs, identifying ways of fixing their existing issues and optimizing product targeting.
According to a report by Accenture, customers yearn for highly personalized treatment: 48% of the customers expect specialized treatment for being a good customer. Besides, a lot of customers i.e. around 33% abandon business relationship because of the lack of personalization in the service they receive.
Banks have taken note of these trends and are devising novel ways of delivering their customers a hyper-personalised experience. Big data can play a crucial role here. Using big data analytics, companies can garner insights on customer behaviour and cater to their wants and needs in a personalised manner.
American Express, the financial services behemoth, has been using big data analytics and predictive modelling to forecast and minimize customer churn since 2010. The company uses complex big data models to analyse historical transactions in addition to 115 other variables to identify accounts which are most likely to close within the next few months. This allows them to work proactively and enact steps to retain these customers.
Fraud detection is yet another salient application of big data in banking. Every year, the banking industry loses millions on account of fraudulent activities. With more and more customers choosing to go the digital way, banks have become highly susceptible to online frauds. As a matter of fact, the Indian banks lost Rs 109.75 crore to theft and online fraud in 2018 alone.
Big data-driven tools help banks study the behaviour of their customers, particularly their spending habits, in detail. Based on the history of a customer’s transactions, banks are able to define and establish a normal baseline activity. Any deviation from this baseline may be indicative of fraud.
Citibank, for instance, has partnered with technology companies as a part of its initiative known as Citi Ventures. Under this initiative, Citibank has invested in an analytics company Feedzai that uses predictive modelling to carry out deep behavioural analysis of the organization’s data and identify fraudulent activity. As a result, Citibank can pinpoint any suspicious transactions and immediately notify the users about them.
Banking and financial institutions are highly susceptible to risks on accounts of bad investments and bad payers. Agreed, big data analytics cannot eliminate such risks. But it can certainly mitigate them to a significant degree.
Banks can leverage big data technologies like Hadoop to build credit risk models that can offer insights into customer behaviour by analysing information such as credit reports, spending habits and repayment rates of credit applicants. This determines the likelihood that a customer would default on a loan or fail to meet payment deadlines.
Big data solutions allow banks to analyse and share performance metrics of every branch, department as well as an individual employee. This brings more visibility into their day-to-day operations, allowing them to resolve issues proactively.
For instance, BNP Paribas, an international banking group, is using big data-based tools to study data on bank productivity and identify and fix issues as they come up. Using the tool, the bank can get answers to questions like:
So, big data has helped the company drill down on data pertaining to each branch’s performance in terms of customer acquisition, customer retention, employee efficiency and turnover.
Banking institutions have woken up to the indispensability of big data analytics. They are integrating state-of-the-art analytical solutions into their existing workflows in a bid to keep pace with the rapidly evolving business environment. However, what has been done so far amounts to scratching the surface. Banks are yet to tap the full potential of this technology.
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