5 Reasons Business Analytics is Crucial For Modern Banking

With the emergence of analytics banking has become more personal, it has a much bigger impact in shaping consumer culture. Banks can now identify and target prospective clients with more confidence as it can locate customer spending patterns and investment or loan history to convert into usable insight.  With the analytical market surveys, fiscal research, banks can connect a consumer’s business objective with their banking needs to make way for a more profitable upgrade or loan package for the customer.

In progressive banking, the entire pattern is a lot more flexible and customisable for individual needs, and it has proven to be beneficial for banks. The tools for analytics are more focused and direct now, aimed at identifying retail behaviour or investment planning and shortlisting high-value customer profiles. 

Simply put, losing customers costs the bank money because even if it gets three new customers in exchange for one, retaining new customers is a lot more expensive than holding on to the existing one.

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This is where analytics comes into play, it can predict consumer priorities, and can foresee upcoming financial needs, and offer value-based prospects and several upgrades, benefits, credit offers to help out with their economic planning. So, how exactly does analytics help banking? Let’s find out:

Why Analytics For Modern Banking

1. Knowing Customer’s Priorities

For obvious reasons analytics help the banking industry detect the buying and investing patterns of customers which in turn, helps them in curating their policies. Zeroing in on the right product and monitoring consumer usage is crucial and one of the most challenging areas in the modern banking sector, when the urban spending habits are so wide-ranging and diverse.

Analytics can help banks classify customers according to their economic paradigms, which makes a whole lot of difference in determining their needs and challenges. Moreover, it aids in educating the banks about their customers, so they can tailor offers, upgrades and other details in a fashion that’s relevant to them, and appeals to them individually, leading to growth in productivity. 

Advanced or predictive analysis can determine what the customer will need next or what they are not interested in. It is important to note that in customer care, communication and response channels are constantly evolving and only increasing in number.

This puts the personal banker in direct touch with their clients, this means the challenges are higher; a financial provider will have to make real-time assessments, based on their consumer’s credit history or retail behaviour. In this scenario, in order to improve customer service and make it more efficient, a specialized form of customer-serving analytic set-up is the need of the hour for banking. 

Learn more: What is Customer Analytics and Why it matters?

2. Fraud Detection

Analytics deals with data and it has the potential to actually foresee fraudulence based on the patterns and investment behaviour of customers. This is especially significant in the urban milieu when every spending or investment is so closely monitored by banks, it also equips them with a deep knowledge about their clients’ financial capabilities and potential future needs.

Customers who have loan accounts, mutual fund accounts or use credit cards have a usage pattern which can be studied by analytics and it can determine if any major imbalance is an indicator of fraud. With the emergence of big data, most banks rely upon monitoring systems which are operated by human experts. 

With the digital boom, modes of fraudulent activities have become manifold, cyber fraud, especially, is a really tricky area. With the help of analytics, a bank can probe oddities in purchase habits or investment decisions. Analytics is also very useful in studying the gap between several transactions and determining their causes, their correlation to past or future transactions, which can make for a case study to figure out if there’s fraudulence afoot.

Experts believe that most fraudsters leave behind a trail of breadcrumb in the form of data or information, which can be uncovered by a deep dive into their patterns, especially in tax-related fraud. 

3. Strengthening the Customer Base

The key to making your customer base grow is to retain customers while you simultaneously get new ones. Analytics are very useful in optimized selection which helps banks identify and high-value consumers and cater to what they are looking for. This is also about creating new opportunities and options for your existing customer base; this is a direct means of bringing in more money and also a way to engage them, in order to understand their economic patterns better.

When a customer wants to leave a bank, analytics can probe the reasons as to how or why the customer is disappointed, and even prompts upgrades to fix the situation. It’s eventually about strategy-building, about coming up with ways to be better than your competition and fulfilling the needs of your existing customer base. 

Also read: Business Analytics – Tools & Applications

In this cutthroat commercial market, customer retention is the trickiest part for banks, mainly because so many people are committed to more than one banks. It often turns into a quest to offer better, more streamlined offers, better loan packages, smarter fund recovery options, reward points, these are just some ways to retain customer loyalty.

But these again, are determined with the help of analytics, the bank has to know what exactly the customer responds to, in terms of upgrades and benefits, it can’t afford to be redundant. 

4. To Improve Marketing Prospects

With the help of analytics, banks can design products to optimize sales and minimize attrition. Analytics plays a huge role in minimizing risks associated with a product, as it can fathom its limitations beforehand and also by identifying non-performing assets.

Banks can obviously maximize their Return on Investments (ROI) with analytics, since it has the ability to create marketing that’s more streamlined, so the right product is aimed at the right kind of consumer, and this is key to new-age banking.

5. For Reducing Risks

Using predictive analytics, companies can effectively manage their risks, especially since it can monitor so many diverse forms of data sets at once, be it raw or structured. So it can assess the potential risks involved in any field, be it marketing or be it workforce-related. Most importantly, it can be used to detect the root of past mistakes or bad fiscal phases, and to determine ways to fix loopholes.

With analytics, certain calculated parameters can be put in place to assess high risk decisions better, since there’s always an element of unpredictability when it comes to the market. The key is to plan along the lines of the risk, be it with the help of predictive modeling, or even individual case studies, but understanding the range of a risk and its eventual aftermath is crucial and analytics is very specific when it comes to gathering insight about the causality or the outcomes. 

Read more: Top 5 Big Data Applications in Banking & Insurance

Concluding Thoughts

It is essential to note that analytics has a lot to do with business intelligence; it’s data that you can convert into usable knowledge. In banking, especially with predictive analytics and big data, it has become a lot easier to figure out the opportunities which would fetch the highest response rates among customers.

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When it comes to service, especially, analytics has the potential to offer exactly what the customer has been looking for in terms of value-added offerings, so banks can actually design opportunities to cater to certain niches and it helps immensely in securing customer loyalty.

Business Analytics has become an integral part of the business world. As data keeps on piling up by the minute, more and more organizations are relying on BA and BI tools to boost profitability and optimize business operations. And more students and professionals are rushing to pursue MBA business analytics course to brush up their knowledge and experience.

And with the cut-throat competition that exists today, businesses that do not integrate business analytics within their framework are not only missing out on growth opportunities but also might fail to keep up with the market over time.

How is business analytics used in banking?

Business analytics is perhaps one of the most important skills in today’s world. Almost every new technical entrepreneur is developing complex programs that could help large businesses take important decisions in various functions – such as finance, marketing, operations, business strategy, customer service & retention, and more. India’s BFSI industry is currently being transformed with digitization. With business analytics, banks can make better decisions in various functions including risk, underwriting, policy, marketing, business development & strategy and more. Business analytics also helps banks digitize their systems, processes and channels in order to serve customers quicker, better and in a more cost-effective manner.

How to get a business analytics role in the BFSI industry?

If you are interested in a business analytics role in BFSI, it would be very helpful to have a graduate degree in engineering, maths or statistics. Combining this with an MBA in digital finance, business analytics, or banking would be an excellent decision. Depending on your interest, you can get roles as a financial analyst, financial planner, risk manager, policy manager, investment banker, trading specialist, product manager, and so on. If you do not have a background in maths, you can take up part-time courses to train in the use of business analytics tools, statistical and financial modelling.

Is it required to know coding to get a job in business analytics?

It is not necessary to know coding if you are serious about a career in business analytics. However, coding skills certainly come in handy if you would like to get into hard-core analytics such as designing algorithms that could help organisations interpret vast amounts of data and make decisions based on these interpretations. A knowledge in tools such as R and SaaS combined with a working knowledge of Python, Java Script, and Ajax would be a huge bonus. If you would like to get such roles, it would be a good idea to take part-time courses in coding languages.

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